diff --git a/.changeset/vquery-parameterized-dialects.md b/.changeset/vquery-parameterized-dialects.md
new file mode 100644
index 0000000000..c7975c7aba
--- /dev/null
+++ b/.changeset/vquery-parameterized-dialects.md
@@ -0,0 +1,9 @@
+---
+'@visactor/vquery': patch
+---
+
+Expose parameterized `compileQuery` with MySQL as the default dialect and explicit `convertDSLToMysql` / `convertDSLToDuckDB` helpers, replacing `convertDSLToSQL`. Keep dataset queries on the DuckDB strategy.
+
+Align MySQL and DuckDB aggregation, date grouping, string matching, NULL ordering, and median/quantile behavior. Add a shared real-schema DSL catalog that snapshots both dialects without database test dependencies.
+
+Default browser and Node dataset storage to memory. Keep separate Node and browser strategy entrypoints with shared constructor option types, and enable IndexedDB persistence only with `new VQuery({ enableIndexedDB: true })`.
diff --git a/apps/website/docs/zh-CN/vquery/development/tests.md b/apps/website/docs/zh-CN/vquery/development/tests.md
index c735efedd1..487ff5e482 100644
--- a/apps/website/docs/zh-CN/vquery/development/tests.md
+++ b/apps/website/docs/zh-CN/vquery/development/tests.md
@@ -3,25 +3,70 @@
 VQuery 使用 `rstest` 框架进行测试。**所有命令需在根目录执行。**
 
 ## 测试机制
-VQuery 的测试覆盖了：
-- **Unit**: 工具函数与编译器逻辑。
-- **examples**: 完整的 SQL 生成与数据查询流程。
+
+- **Unit**：工具函数、编译器逻辑与内存查询引擎。
+- **examples**：SQL 生成与内存数据查询流程。
+- **dialects**：共用一套 DSL，快照校验 MySQL 和 DuckDB 的 SQL 生成结果，不连接数据库。
 
 ## 常用命令
 
 ### 运行所有测试
+
 ```bash
-pnpm --filter=@visactor/vquery run test
+pnpm --filter @visactor/vquery run test
 ```
 
 ### 更新快照
-如果 SQL 生成逻辑变更符合预期，需更新快照：
+
+确认 SQL 变化符合预期后更新快照，并审阅生成的差异：
+
 ```bash
-pnpm --filter=@visactor/vquery run test:update
+pnpm --filter @visactor/vquery run test:update
 ```
 
 ### 覆盖率报告
-生成并查看测试覆盖率：
+
+```bash
+pnpm --filter @visactor/vquery run test:coverage
+```
+
+## 方言 SQL 快照
+
+方言测试只有一套共享用例和一份快照：
+
+- `tests/dialects/queries.json`：以 `supermarket` 为查询表，保存真实 CSV 的完整 20 字段逻辑 schema 和共享 DSL 用例。`source` 指向仓库已有的 `tests/unit/assets/supermarket.csv`；该文件与本次提供的 CSV 内容一致，测试无需访问用户的 Downloads 目录。每条用例用唯一且稳定的 `name` 标识，可附 `description` 解释业务场景。
+- `tests/dialects/compile.test.ts`：每条 DSL 分别调用 MySQL、DuckDB 的 `compileQuery`，将两种方言的 `{ sql, parameters }` 一起核对快照。
+- `tests/dialects/__snapshots__/compile.test.ts.snap`：按用例保存两种方言的 SQL 和参数；通过快照差异审阅编译逻辑变化。
+
+测试纳入现有的 `test`、`test:update`、`test:coverage`，无需专用配置、数据库服务、CLI 或连接凭据。新增方言时复用同一套 DSL，增加对应编译结果即可。
+
+CSV 中 `id`、`sales`、`amount`、`discount`、`profit` 使用 number，`order_date`、`delivery_date` 使用 date，其余列使用 string。日期样例按 SQL DATE 格式表示为 `YYYY-MM-DD`。用例涵盖销售利润、商品层级、省市地域、客户贡献、折扣亏损与配送分析，使用真实字段和业务值；不在业务用例中改写 schema 或虚构源字段。
+
+测试将 schema 字段与 CSV 表头逐一比较，并逐条检查 SELECT、WHERE、HAVING 的源字段；GROUP BY、ORDER BY 可以引用真实字段或 SELECT 输出别名。聚合查询中的维度必须参与分组，聚合结果别名不能作为分组键。别名是输出名称，不视为新的源字段。
+
+只运行方言快照测试：
+
 ```bash
-pnpm --filter=@visactor/vquery run test:coverage
+pnpm --filter @visactor/vquery exec rstest --include 'tests/dialects/**/*.test.ts' --coverage.enabled=false
 ```
+
+覆盖率沿用包现有的四项 100% 门槛，非法 DSL 的拒绝分支由 Unit 测试覆盖，不维护另一套方言覆盖率配置。
+
+## 功能与组合覆盖
+
+源码覆盖率 100% 不等于所有 DSL 组合都被验证。嵌套层数、条件数量和参数值没有有限的穷举集合；用例库持续积累实际场景和历史回归，不以数量代替覆盖范围。
+
+共享 JSON 用例库持续扩充，测试同时检查以下明确的组合要求，删除必要用例会失败。功能与操作符清单通过 DSL 类型约束；现有 `typecheck` 同时检查源码和测试，新增功能时需要同步补充清单与用例。
+
+| 范围 | 覆盖要求 |
+| --- | --- |
+| SELECT | 11 种基础聚合、8 种日期聚合，以及字段、别名与全部字段投影 |
+| HAVING | 11 种基础聚合 × 12 种比较操作符，共 132 种组合 |
+| 日期分组 | 8 种日期聚合 × 11 种基础聚合，共 88 种组合 |
+| 排序 | 11 种基础聚合 × 升降序 × 默认/first/last NULL 位置，共 66 种组合 |
+| WHERE | 16 种操作符分别位于 AND、OR 嵌套树中，共 32 种组合 |
+| 完整查询 | SELECT、WHERE、GROUP BY、HAVING、ORDER BY、limit 同时出现 |
+
+另包含商品名称 MIN/MAX 的 HAVING、下单日与配送日分组、多日期多字段分组、销售与利润跨字段统计、未投影 HAVING、别名冲突、2/4/8 层嵌套等回归场景。这些场景不代表任意组合均已穷尽。
+
+追加场景时，直接在 `queries.json` 的 `queries` 数组中增加基于这份 schema 的完整 DSL，用 `description` 说明触发条件或历史问题，再运行 `test:update` 并审阅两种方言的 SQL 差异。不要复制方言专属用例或加入随机生成步骤。无 schema、特殊标识符、JS `Date`、`Infinity` 等编译器边界由 Unit 测试维护。
diff --git a/apps/website/docs/zh-CN/vquery/index.md b/apps/website/docs/zh-CN/vquery/index.md
index e3ef1adeff..922548beee 100644
--- a/apps/website/docs/zh-CN/vquery/index.md
+++ b/apps/website/docs/zh-CN/vquery/index.md
@@ -1,5 +1,21 @@
 # VQuery
 
-:::info In Development
-Coming Soon
-:::
+VQuery 在 Node.js 和浏览器中默认使用内存查询与存储 adapter，无需 IndexedDB。每个实例独立管理自己的数据集。
+
+```ts
+import { VQuery } from '@visactor/vquery'
+
+const vquery = new VQuery()
+```
+
+默认存储的数据只在当前实例生命周期内有效，关闭实例或刷新页面后不保留。
+
+## 启用 IndexedDB
+
+需要跨会话保存数据集 schema 和数据源时，显式开启 `enableIndexedDB`：
+
+```ts
+const vquery = new VQuery({ enableIndexedDB: true })
+```
+
+开启后使用 IndexedDB 存储 adapter，运行环境需要支持 IndexedDB；查询仍通过内存查询 adapter 执行。省略此选项、传入空 options 或设置为 `false` 时，均使用内存存储，无需 IndexedDB API。
diff --git a/packages/vquery/README.md b/packages/vquery/README.md
index 362511d7f8..19dfc75920 100644
--- a/packages/vquery/README.md
+++ b/packages/vquery/README.md
@@ -1 +1,17 @@
 # VQuery
+
+Node.js and browser entrypoints use in-memory query and storage adapters by default. No IndexedDB API is required, and each VQuery instance owns its datasets.
+
+```ts
+import { VQuery } from '@visactor/vquery'
+
+const vquery = new VQuery()
+```
+
+Enable IndexedDB explicitly to persist dataset schemas and sources between sessions:
+
+```ts
+const vquery = new VQuery({ enableIndexedDB: true })
+```
+
+The runtime must support IndexedDB when this option is enabled. Queries still run in memory. With the default storage, closing the instance or reloading the page discards its datasets.
diff --git a/packages/vquery/rstest.config.ts b/packages/vquery/rstest.config.ts
index 8faaf18345..3fb7565358 100644
--- a/packages/vquery/rstest.config.ts
+++ b/packages/vquery/rstest.config.ts
@@ -4,7 +4,7 @@ export default defineConfig({
   globals: true,
   testEnvironment: 'node',
   includeSource: ['src/**/*.{js,ts}'],
-  exclude: ['src/browser.ts', 'src/vquery-browser.ts'],
+  exclude: ['src/browser.ts'],
   setupFiles: ['./tests/setup.ts'],
   coverage: {
     enabled: true,
@@ -12,9 +12,8 @@ export default defineConfig({
     exclude: [
       '**/node_modules/**',
       '**/dist/**',
-      // rstest can't run browser entrypoints.
+      // Export barrels have no runtime logic; both entrypoints are exercised by unit tests.
       'src/browser.ts',
-      'src/vquery-browser.ts',
     ],
     include: ['src/**'],
     reporters: ['html', ['text', { skipFull: true }], 'json', 'json-summary'],
diff --git a/packages/vquery/scripts/build-tests.js b/packages/vquery/scripts/build-tests.js
index e1f3fd49a0..8faebda2b0 100644
--- a/packages/vquery/scripts/build-tests.js
+++ b/packages/vquery/scripts/build-tests.js
@@ -34,7 +34,7 @@ function generateTest(jsonPath) {
     const testPath = path.join(path.dirname(jsonPath), testFileName)
 
     const template = `import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './${jsonFileName}'
 
 describe('${description}', () => {
@@ -52,7 +52,7 @@ describe('${description}', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot()
 
diff --git a/packages/vquery/src/browser.ts b/packages/vquery/src/browser.ts
index 5612752b58..6188837426 100644
--- a/packages/vquery/src/browser.ts
+++ b/packages/vquery/src/browser.ts
@@ -1,4 +1,5 @@
-export { convertDSLToSQL, Dataset } from './dataset'
+export { Dataset } from './dataset'
+export * from './sql-builder'
 export { DatasetSourceBuilder } from './data-source-builder/dataSourceBuilder'
 export * from './utils'
 export * from './types'
diff --git a/packages/vquery/src/dataset/dataset.ts b/packages/vquery/src/dataset/dataset.ts
index b10957314c..e0201278fc 100644
--- a/packages/vquery/src/dataset/dataset.ts
+++ b/packages/vquery/src/dataset/dataset.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, DatasetSource, QueryDSL, QueryAdapter, StorageAdapter, VQueryDSL } from '../types'
-import { convertDSLToSQL } from '../sql-builder'
+import { convertDSLToDuckDB } from '../sql-builder'
 
 export class Dataset {
   private queryAdapter: QueryAdapter
@@ -38,7 +38,7 @@ export class Dataset {
   }
 
   public async query<T extends Record<string, number | string>>(queryDSL: QueryDSL<T> | VQueryDSL<T>) {
-    const sql = convertDSLToSQL(queryDSL, this.datasetId)
+    const sql = convertDSLToDuckDB(queryDSL, this.datasetId)
     return this.queryBySQL(sql)
   }
 
diff --git a/packages/vquery/src/dataset/index.ts b/packages/vquery/src/dataset/index.ts
index e56401a20a..126e036ab4 100644
--- a/packages/vquery/src/dataset/index.ts
+++ b/packages/vquery/src/dataset/index.ts
@@ -1,2 +1 @@
 export { Dataset } from './dataset'
-export { convertDSLToSQL } from '../sql-builder'
diff --git a/packages/vquery/src/node.ts b/packages/vquery/src/node.ts
index a9f145156f..91f18b3137 100644
--- a/packages/vquery/src/node.ts
+++ b/packages/vquery/src/node.ts
@@ -1,4 +1,5 @@
-export { convertDSLToSQL, Dataset } from './dataset'
+export { Dataset } from './dataset'
+export * from './sql-builder'
 export { DatasetSourceBuilder } from './data-source-builder/dataSourceBuilder'
 export * from './utils'
 export * from './types'
diff --git a/packages/vquery/src/query-engine/sql.ts b/packages/vquery/src/query-engine/sql.ts
index 36eb3ca0e9..d84159b0e0 100644
--- a/packages/vquery/src/query-engine/sql.ts
+++ b/packages/vquery/src/query-engine/sql.ts
@@ -29,7 +29,7 @@ type Predicate =
   | { type: 'truthy'; value: Expression }
 
 type SelectItem = { expression: Expression; alias?: string }
-type OrderItem = { expression: Expression; direction: 'asc' | 'desc' }
+type OrderItem = { expression: Expression; direction: 'asc' | 'desc'; nulls: 'first' | 'last' }
 
 export type SelectQuery = {
   table?: string
@@ -263,7 +263,12 @@ class Parser {
     if (this.match('like') || this.match('ilike')) {
       const matchedOperator = this.tokens[this.index - 1].value.toLowerCase() as 'like' | 'ilike'
       const operator: LikeOperator = negated ? `not ${matchedOperator}` : matchedOperator
-      return { type: 'comparison', operator, left, right: this.scalar() }
+      const right = this.scalar()
+      if (this.match('escape')) {
+        const escape = this.scalar()
+        if (escape.type !== 'literal' || escape.value !== '') throw new Error('Only empty LIKE escape is supported')
+      }
+      return { type: 'comparison', operator, left, right }
     }
     if (negated) throw new Error('Expected IN, BETWEEN, LIKE, or ILIKE after NOT')
     const token = this.current()
@@ -299,7 +304,12 @@ class Parser {
         do {
           const expression = this.scalar()
           const direction = this.match('desc') ? 'desc' : (this.match('asc'), 'asc')
-          result.orderBy.push({ expression, direction })
+          let nulls: 'first' | 'last' = 'last'
+          if (this.match('nulls')) {
+            if (this.match('first')) nulls = 'first'
+            else this.expect('last')
+          }
+          result.orderBy.push({ expression, direction, nulls })
         } while (this.match(','))
       } else if (this.match('limit')) {
         const token = this.current()
@@ -358,10 +368,8 @@ const compareText = (left: string, right: string): number => {
   return leftCodePoints.length - rightCodePoints.length
 }
 
-const compare = (left: unknown, right: unknown): number => {
+const compareNonNull = (left: unknown, right: unknown): number => {
   if (left === right) return 0
-  if (left === null || left === undefined) return 1
-  if (right === null || right === undefined) return -1
   if (typeof left === 'number' && typeof right === 'number') return left - right
   return compareText(String(left), String(right))
 }
@@ -411,8 +419,10 @@ const aggregate = (expression: Extract<Expression, { type: 'call' }>, rows: Data
     const numbers = numericValues(argument, rows)
     return numbers.length ? numbers.reduce((sum, value) => sum + value, 0) / numbers.length : null
   }
-  if (expression.name === 'min') return values.length ? values.reduce((a, b) => (compare(a, b) <= 0 ? a : b)) : null
-  if (expression.name === 'max') return values.length ? values.reduce((a, b) => (compare(a, b) >= 0 ? a : b)) : null
+  if (expression.name === 'min')
+    return values.length ? values.reduce((a, b) => (compareNonNull(a, b) <= 0 ? a : b)) : null
+  if (expression.name === 'max')
+    return values.length ? values.reduce((a, b) => (compareNonNull(a, b) >= 0 ? a : b)) : null
   const numbers = numericValues(argument, rows).sort((a, b) => a - b)
   if (!numbers.length) return null
   if (expression.name === 'median') {
@@ -426,10 +436,8 @@ const aggregate = (expression: Extract<Expression, { type: 'call' }>, rows: Data
     if (value === null || value === undefined || !Number.isFinite(quantile) || quantile < -1 || quantile > 1) {
       throw new Error('QUANTILE parameter must be a number in the range [-1, 1]')
     }
-    const index =
-      quantile < 0
-        ? Math.min(numbers.length + Math.floor(quantile * numbers.length), numbers.length - 1)
-        : Math.max(Math.ceil(quantile * numbers.length) - 1, 0)
+    const position = Math.max(numbers.length - Math.floor(numbers.length - numbers.length * Math.abs(quantile)), 1)
+    const index = quantile < 0 ? numbers.length - position : position - 1
     return numbers[index]
   }
   const average = numbers.reduce((sum, value) => sum + value, 0) / numbers.length
@@ -513,7 +521,7 @@ const evaluatePredicate = (predicate: Predicate, row: DataRow, rows: DataRow[]):
   if (predicate.operator === 'in' || predicate.operator === 'not in') {
     const values = predicate.values.map((value) => evaluate(value, row, rows))
     if (left === null || left === undefined) return null
-    const found = values.some((value) => value !== null && value !== undefined && compare(left, value) === 0)
+    const found = values.some((value) => value !== null && value !== undefined && compareNonNull(left, value) === 0)
     if (found) return predicate.operator === 'in'
     if (values.some((value) => value === null || value === undefined)) return null
     return predicate.operator === 'not in'
@@ -522,7 +530,7 @@ const evaluatePredicate = (predicate: Predicate, row: DataRow, rows: DataRow[]):
     const values = predicate.values.map((value) => evaluate(value, row, rows))
     if (left === null || left === undefined || values.some((value) => value === null || value === undefined))
       return null
-    const found = compare(left, values[0]) >= 0 && compare(left, values[1]) <= 0
+    const found = compareNonNull(left, values[0]) >= 0 && compareNonNull(left, values[1]) <= 0
     return predicate.operator === 'between' ? found : !found
   }
   const operator = predicate.operator
@@ -533,7 +541,7 @@ const evaluatePredicate = (predicate: Predicate, row: DataRow, rows: DataRow[]):
     return operator.startsWith('not ') ? !result : result
   }
   if (left === null || left === undefined || right === null || right === undefined) return null
-  const comparison = compare(left, right)
+  const comparison = compareNonNull(left, right)
   if (operator === '=') return comparison === 0
   if (operator === '!=' || operator === '<>') return comparison !== 0
   if (operator === '>') return comparison > 0
@@ -607,7 +615,13 @@ export const executeSQL = (query: SelectQuery, tableRows: DataRow[]): DataRow[]
         fieldName && Object.hasOwn(right.output, fieldName)
           ? right.output[fieldName]
           : evaluate(order.expression, right.row, right.rows)
-      const result = compare(leftValue, rightValue)
+      const leftNull = leftValue === null || leftValue === undefined
+      const rightNull = rightValue === null || rightValue === undefined
+      if (leftNull || rightNull) {
+        if (leftNull !== rightNull) return (leftNull ? 1 : -1) * (order.nulls === 'first' ? -1 : 1)
+        continue
+      }
+      const result = compareNonNull(leftValue, rightValue)
       if (result !== 0) return order.direction === 'desc' ? -result : result
     }
     if (!query.groupBy.length || !query.orderBy.length) return 0
diff --git a/packages/vquery/src/sql-builder/builders/having.ts b/packages/vquery/src/sql-builder/builders/having.ts
index 9a213898f6..0d014c9b03 100644
--- a/packages/vquery/src/sql-builder/builders/having.ts
+++ b/packages/vquery/src/sql-builder/builders/having.ts
@@ -2,45 +2,12 @@ import type { RawBuilder, SelectQueryBuilder } from 'kysely'
 import type { Having, HavingAggregation, HavingClause } from '../../types'
 import { sql } from 'kysely'
 import { toSqlOperator } from '../utils'
-
-const AGGREGATE_FUNCTIONS = [
-  'sum',
-  'avg',
-  'count',
-  'count_distinct',
-  'min',
-  'max',
-  'variance',
-  'variance_pop',
-  'stddev',
-  'median',
-  'quantile',
-] as const
-
-const isAggregateFunction = (func: string): boolean => {
-  return AGGREGATE_FUNCTIONS.includes(func as (typeof AGGREGATE_FUNCTIONS)[number])
-}
-
-const toAggregateExpression = (func: string, field: string, aggr?: HavingAggregation): RawBuilder<unknown> => {
-  switch (func) {
-    case 'count':
-      return sql`count(${sql.ref(field)})`
-    case 'count_distinct':
-      return sql`count(distinct ${sql.ref(field)})`
-    case 'variance':
-      return sql`var_samp(${sql.ref(field)})`
-    case 'variance_pop':
-      return sql`var_pop(${sql.ref(field)})`
-    case 'quantile':
-      return sql`quantile(${sql.ref(field)}, ${aggr?.quantile ?? 0.5})`
-    default:
-      return sql`${sql.raw(func)}(${sql.ref(field)})`
-  }
-}
+import type { SQLStrategy } from '../types'
 
 export const applyHaving = <DB, TB extends keyof DB & string, O, T>(
   qb: SelectQueryBuilder<DB, TB, O>,
-  having?: Having<T> | HavingClause<T>,
+  having: Having<T> | HavingClause<T> | undefined,
+  strategy: SQLStrategy,
 ): SelectQueryBuilder<DB, TB, O> => {
   if (!having) {
     return qb
@@ -48,6 +15,7 @@ export const applyHaving = <DB, TB extends keyof DB & string, O, T>(
 
   const toRaw = (h: Having<T> | HavingClause<T>): RawBuilder<boolean> => {
     if ('op' in h && 'conditions' in h) {
+      if (h.conditions.length === 0) return h.op === 'and' ? sql<boolean>`1=1` : sql<boolean>`0=1`
       const parts: RawBuilder<boolean>[] = (h.conditions as Array<HavingClause<T>>).map((c) => toRaw(c))
       const sep: RawBuilder<unknown> = sql` ${sql.raw(h.op)} `
       return sql<boolean>`(${sql.join(parts, sep)})`
@@ -60,11 +28,8 @@ export const applyHaving = <DB, TB extends keyof DB & string, O, T>(
     }
     const field = leaf.field
     const value = leaf.value
-    if (!leaf.aggr?.func || !isAggregateFunction(leaf.aggr.func)) {
-      throw new Error(`Invalid having clause for field "${String(field)}": aggr.func is required`)
-    }
     const compareOp = toSqlOperator(leaf.op.trim().toLowerCase())
-    const leftExpr: RawBuilder<unknown> = toAggregateExpression(leaf.aggr.func, field, leaf.aggr)
+    const leftExpr: RawBuilder<unknown> = strategy.aggregate(field, leaf.aggr)
 
     switch (compareOp) {
       case 'is null':
@@ -73,22 +38,22 @@ export const applyHaving = <DB, TB extends keyof DB & string, O, T>(
         return sql<boolean>`${leftExpr} is not null`
       case 'in': {
         const items = Array.isArray(value) ? (value as unknown[]) : [value]
-        return sql<boolean>`${leftExpr} in (${sql.join(items.map((v) => sql.val(v)))})`
+        return sql<boolean>`${leftExpr} in (${sql.join(items.map((v) => strategy.value(v)))})`
       }
       case 'not in': {
         const items = Array.isArray(value) ? (value as unknown[]) : [value]
-        return sql<boolean>`not ${leftExpr} in (${sql.join(items.map((v) => sql.val(v)))})`
+        return sql<boolean>`not ${leftExpr} in (${sql.join(items.map((v) => strategy.value(v)))})`
       }
       case 'between': {
         const [a, b] = value as [unknown, unknown]
-        return sql<boolean>`${leftExpr} between ${sql.val(a)} and ${sql.val(b)}`
+        return sql<boolean>`${leftExpr} between ${strategy.value(a)} and ${strategy.value(b)}`
       }
       case 'not between': {
         const [a, b] = value as [unknown, unknown]
-        return sql<boolean>`${leftExpr} not between ${sql.val(a)} and ${sql.val(b)}`
+        return sql<boolean>`${leftExpr} not between ${strategy.value(a)} and ${strategy.value(b)}`
       }
       default:
-        return sql<boolean>`${leftExpr} ${sql.raw(compareOp)} ${sql.val(value)}`
+        return sql<boolean>`${leftExpr} ${sql.raw(compareOp)} ${strategy.value(value)}`
     }
   }
   return qb.having(toRaw(having))
diff --git a/packages/vquery/src/sql-builder/builders/limit.ts b/packages/vquery/src/sql-builder/builders/limit.ts
index 87f3b02d18..b3d559029b 100644
--- a/packages/vquery/src/sql-builder/builders/limit.ts
+++ b/packages/vquery/src/sql-builder/builders/limit.ts
@@ -1,8 +1,13 @@
 import type { SelectQueryBuilder } from 'kysely'
+import type { SQLStrategy } from '../types'
 
-export const applyLimit = <DB, TB extends keyof DB & string, O>(qb: SelectQueryBuilder<DB, TB, O>, limit?: number) => {
-  if (limit && typeof limit === 'number') {
-    qb = qb.limit(limit)
+export const applyLimit = <DB, TB extends keyof DB & string, O>(
+  qb: SelectQueryBuilder<DB, TB, O>,
+  limit: number | undefined,
+  strategy: SQLStrategy,
+) => {
+  if (limit !== undefined) {
+    qb = qb.limit(strategy.limit(limit))
   }
   return qb
 }
diff --git a/packages/vquery/src/sql-builder/builders/order.ts b/packages/vquery/src/sql-builder/builders/order.ts
index e7f2c2c3b4..7008ab7888 100644
--- a/packages/vquery/src/sql-builder/builders/order.ts
+++ b/packages/vquery/src/sql-builder/builders/order.ts
@@ -1,19 +1,16 @@
 import type { SelectQueryBuilder } from 'kysely'
-
-type OrderByItem = {
-  field: string
-  order?: 'asc' | 'desc'
-}
+import type { OrderBy } from '../../types'
+import type { SQLStrategy } from '../types'
 
 export const applyOrder = <DB, TB extends keyof DB & string, O>(
   qb: SelectQueryBuilder<DB, TB, O>,
-  orderBy?: Array<OrderByItem>,
+  orderBy: OrderBy<any> | undefined,
+  strategy: SQLStrategy,
 ) => {
-  if (orderBy && orderBy.length > 0) {
-    orderBy.forEach((o) => {
-      // @ts-expect-error - Kysely orderBy type complexity
-      qb = qb.orderBy(o.field, o.order ?? 'asc')
-    })
+  for (const item of orderBy ?? []) {
+    for (const expression of strategy.order(item.field as string, item.order ?? 'asc', item.nulls ?? 'last')) {
+      qb = qb.orderBy(expression)
+    }
   }
   return qb
 }
diff --git a/packages/vquery/src/sql-builder/builders/select.ts b/packages/vquery/src/sql-builder/builders/select.ts
index fbc6ab1eb1..190f753c2a 100644
--- a/packages/vquery/src/sql-builder/builders/select.ts
+++ b/packages/vquery/src/sql-builder/builders/select.ts
@@ -2,64 +2,20 @@ import type { SelectItem } from '../../types/dsl/Select'
 import { isSelectItem } from '../utils'
 import { sql } from 'kysely'
 import type { SelectQueryBuilder } from 'kysely'
-
-const DATE_FORMAT_MAP: Record<string, string> = {
-  year: '%Y',
-  month: '%Y-%m',
-  day: '%Y-%m-%d',
-  week: '%Y-W%W',
-  hour: '%Y-%m-%d %H',
-  minute: '%Y-%m-%d %H:%M',
-  second: '%Y-%m-%d %H:%M:%S',
-}
+import type { SQLStrategy } from '../types'
 
 export const applySelect = <DB, TB extends keyof DB & string, O, T>(
   qb: SelectQueryBuilder<DB, TB, O>,
-  select?: Array<keyof T | SelectItem<T>>,
+  select: Array<keyof T | SelectItem<T>>,
+  strategy: SQLStrategy,
 ) => {
-  if (select && select.length > 0) {
-    return qb.select((eb) =>
-      select.map((item) => {
-        if (isSelectItem(item)) {
-          const field = item.field as Extract<keyof T, string>
-          const expression = eb.ref(field as any)
-          // 优先使用用户提供的 alias，其次使用 field，保证唯一性
-          const alias = item.alias ?? field
-
-          if (item.aggr) {
-            const { func } = item.aggr
-            if (['avg', 'sum', 'min', 'max', 'variance', 'variance_pop', 'stddev', 'median'].includes(func)) {
-              if (func === 'variance') {
-                return sql`var_samp(${expression})`.as(alias)
-              }
-              if (func === 'variance_pop') {
-                return sql`var_pop(${expression})`.as(alias)
-              }
-              return sql`${sql.raw(func)}(${expression})`.as(alias)
-            } else if (func === 'count') {
-              return sql`CAST(count(${expression}) AS INTEGER)`.as(alias)
-            } else if (func === 'quantile') {
-              const q = item.aggr.quantile ?? 0.5
-              return sql`quantile(${expression}, ${q})`.as(alias)
-            } else if (func === 'count_distinct') {
-              return sql`CAST(count(distinct ${expression}) AS INTEGER)`.as(alias)
-            }
-            const dateTrunc = func.replace('to_', '')
-            const format = DATE_FORMAT_MAP[dateTrunc]
-            if (format) {
-              return sql`strftime(CAST(${expression} AS TIMESTAMP), ${format})`.as(alias)
-            }
-            return sql`strftime(CAST(${expression} AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST(${expression} AS TIMESTAMP))`.as(
-              alias,
-            )
-          }
-          // 优先使用用户提供的 alias，其次使用 field
-          return expression.as(alias)
-        }
-        return item as any
-      }),
-    )
-  } else {
-    return qb.selectAll()
-  }
+  if (select.length === 0) return qb.selectAll()
+  return qb.select(() =>
+    select.map((item) => {
+      if (!isSelectItem(item)) return item as any
+      const field = item.field as string
+      const expression = sql.ref(field)
+      return (item.aggr ? strategy.aggregate(field, item.aggr) : expression).as(item.alias ?? field)
+    }),
+  )
 }
diff --git a/packages/vquery/src/sql-builder/builders/where.ts b/packages/vquery/src/sql-builder/builders/where.ts
index 12793f150b..7e96ccf802 100644
--- a/packages/vquery/src/sql-builder/builders/where.ts
+++ b/packages/vquery/src/sql-builder/builders/where.ts
@@ -2,10 +2,12 @@ import type { Where, WhereClause } from '../../types'
 import { sql } from 'kysely'
 import type { RawBuilder, SelectQueryBuilder } from 'kysely'
 import { toSqlOperator } from '../utils'
+import type { SQLStrategy } from '../types'
 
 export const applyWhere = <DB, TB extends keyof DB & string, O, T>(
   qb: SelectQueryBuilder<DB, TB, O>,
-  where?: Where<T> | WhereClause<T>,
+  where: Where<T> | WhereClause<T> | undefined,
+  strategy: SQLStrategy,
 ): SelectQueryBuilder<DB, TB, O> => {
   if (!where) {
     return qb
@@ -13,6 +15,7 @@ export const applyWhere = <DB, TB extends keyof DB & string, O, T>(
 
   const toRaw = (w: Where<T> | WhereClause<T>): RawBuilder<boolean> => {
     if ('op' in w && 'conditions' in w) {
+      if (w.conditions.length === 0) return w.op === 'and' ? sql<boolean>`1=1` : sql<boolean>`0=1`
       const parts: RawBuilder<boolean>[] = (w.conditions as Array<WhereClause<T>>).map((c) => toRaw(c))
       const sep: RawBuilder<unknown> = sql` ${sql.raw(w.op)} `
       return sql<boolean>`(${sql.join(parts, sep)})`
@@ -22,28 +25,33 @@ export const applyWhere = <DB, TB extends keyof DB & string, O, T>(
     const value = leaf.value
     const sqlOp = toSqlOperator(leaf.op)
     switch (leaf.op) {
+      case 'like':
+      case 'not like':
+      case 'ilike':
+      case 'not ilike':
+        return strategy.like(sql.ref(field), value, leaf.op.startsWith('not '), leaf.op.includes('ilike'))
       case 'is null':
         return sql<boolean>`${sql.ref(field)} is null`
       case 'is not null':
         return sql<boolean>`${sql.ref(field)} is not null`
       case 'in': {
         const items = Array.isArray(value) ? (value as unknown[]) : [value]
-        return sql<boolean>`${sql.ref(field)} in (${sql.join(items.map((v) => sql.val(v)))})`
+        return sql<boolean>`${sql.ref(field)} in (${sql.join(items.map((v) => strategy.value(v)))})`
       }
       case 'not in': {
         const items = Array.isArray(value) ? (value as unknown[]) : [value]
-        return sql<boolean>`not ${sql.ref(field)} in (${sql.join(items.map((v) => sql.val(v)))})`
+        return sql<boolean>`not ${sql.ref(field)} in (${sql.join(items.map((v) => strategy.value(v)))})`
       }
       case 'between': {
         const [a, b] = value as [unknown, unknown]
-        return sql<boolean>`${sql.ref(field)} between ${sql.val(a)} and ${sql.val(b)}`
+        return sql<boolean>`${sql.ref(field)} between ${strategy.value(a)} and ${strategy.value(b)}`
       }
       case 'not between': {
         const [a, b] = value as [unknown, unknown]
-        return sql<boolean>`${sql.ref(field)} not between ${sql.val(a)} and ${sql.val(b)}`
+        return sql<boolean>`${sql.ref(field)} not between ${strategy.value(a)} and ${strategy.value(b)}`
       }
       default:
-        return sql<boolean>`${sql.ref(field)} ${sql.raw(sqlOp)} ${sql.val(value)}`
+        return sql<boolean>`${sql.ref(field)} ${sql.raw(sqlOp)} ${strategy.value(value)}`
     }
   }
   return qb.where(toRaw(where))
diff --git a/packages/vquery/src/sql-builder/compile/inlineParameters.ts b/packages/vquery/src/sql-builder/compile/inlineParameters.ts
index aff9bf0b60..216ec911fc 100644
--- a/packages/vquery/src/sql-builder/compile/inlineParameters.ts
+++ b/packages/vquery/src/sql-builder/compile/inlineParameters.ts
@@ -1,20 +1,14 @@
 import { escapeValue } from '../utils'
+import type { QueryDialect } from '../types'
 
-export const inlineParameters = (sql: string, params: readonly unknown[]): string => {
+export const inlineParameters = (sql: string, params: readonly unknown[], dialect: QueryDialect = 'duckdb'): string => {
   if (params.length === 0) return sql
-  if (sql.includes('?')) {
-    let out = sql
-    for (const p of params) {
-      out = out.replace(/\?/, escapeValue(p))
-    }
-    return out
-  }
-  if (/\$\d+/.test(sql)) {
-    return sql.replace(/\$(\d+)/g, (_, idx) => {
-      const i = Number(idx) - 1
-      const v = params[i]
-      return escapeValue(v)
-    })
-  }
-  return sql
+  let index = 0
+  // Match quoted SQL first, and substitute each placeholder only once. Parameter
+  // values containing '?' or '$1' must never become new placeholders.
+  return sql.replace(/("(?:[^"]|"")*"|'(?:[^']|'')*'|`(?:[^`]|``)*`)|\$(\d+)|\?/g, (match, quoted, numbered) => {
+    if (quoted) return match
+    const value = params[numbered ? Number(numbered) - 1 : index++]
+    return escapeValue(dialect === 'mysql' && typeof value === 'string' ? value.replaceAll('\\', '\\\\') : value)
+  })
 }
diff --git a/packages/vquery/src/sql-builder/compileQuery.ts b/packages/vquery/src/sql-builder/compileQuery.ts
new file mode 100644
index 0000000000..2998c6a36a
--- /dev/null
+++ b/packages/vquery/src/sql-builder/compileQuery.ts
@@ -0,0 +1,27 @@
+import { Kysely } from 'kysely'
+import type { QueryDSL } from '../types'
+import type { CompileQueryOptions, CompiledQuery, SQLStrategy } from './types'
+import { mysqlStrategy, duckdbStrategy } from './dialect'
+import { applyWhere, applyGroupBy, applyHaving, applyLimit, applyOrder, applySelect } from './builders'
+import { validateQuery } from './validateQuery'
+
+const strategies = { mysql: mysqlStrategy, duckdb: duckdbStrategy }
+
+export const compileQuery = <T>(dsl: QueryDSL<T>, options: CompileQueryOptions): CompiledQuery => {
+  const dialect = options.dialect ?? 'mysql'
+  if (!Object.hasOwn(strategies, dialect)) throw new Error(`Unsupported query dialect: ${dialect}`)
+  const strategy: SQLStrategy = strategies[dialect]
+  validateQuery(dsl, options.columns)
+  const db = new Kysely<Record<string, T>>({ dialect: strategy.dialect })
+  let qb = strategy.source(db, options.table, options.columns ?? [])
+  qb = applyWhere(qb, dsl.where, strategy)
+  const prepared = strategy.prepare(qb, dsl, db, options.columns ?? [])
+  const preparedStrategy = { ...strategy, aggregate: prepared.aggregate }
+  qb = applySelect(prepared.query, dsl.select, preparedStrategy)
+  qb = applyGroupBy(qb, dsl.groupBy as string[] | undefined)
+  qb = applyHaving(qb, dsl.having, preparedStrategy)
+  qb = applyOrder(qb, dsl.orderBy, strategy)
+  qb = applyLimit(qb, dsl.limit, strategy)
+  const { sql, parameters } = qb.compile()
+  return { sql, parameters }
+}
diff --git a/packages/vquery/src/sql-builder/convertDSLToDuckDB.ts b/packages/vquery/src/sql-builder/convertDSLToDuckDB.ts
new file mode 100644
index 0000000000..5b85a4adb9
--- /dev/null
+++ b/packages/vquery/src/sql-builder/convertDSLToDuckDB.ts
@@ -0,0 +1,13 @@
+import type { QueryDSL } from '../types'
+import type { QueryTable, CompileQueryOptions } from './types'
+import { compileQuery } from './compileQuery'
+import { inlineParameters } from './compile'
+
+export const convertDSLToDuckDB = <T, TableName extends QueryTable = QueryTable>(
+  dsl: QueryDSL<T>,
+  table: TableName,
+  options: Omit<CompileQueryOptions, 'table' | 'dialect'> = {},
+): string => {
+  const { sql, parameters } = compileQuery(dsl, { ...options, table, dialect: 'duckdb' })
+  return inlineParameters(sql, parameters)
+}
diff --git a/packages/vquery/src/sql-builder/convertDSLToMysql.ts b/packages/vquery/src/sql-builder/convertDSLToMysql.ts
new file mode 100644
index 0000000000..9f30492f82
--- /dev/null
+++ b/packages/vquery/src/sql-builder/convertDSLToMysql.ts
@@ -0,0 +1,13 @@
+import type { QueryDSL } from '../types'
+import type { QueryTable, CompileQueryOptions } from './types'
+import { compileQuery } from './compileQuery'
+import { inlineParameters } from './compile'
+
+export const convertDSLToMysql = <T, TableName extends QueryTable = QueryTable>(
+  dsl: QueryDSL<T>,
+  table: TableName,
+  options: Omit<CompileQueryOptions, 'table' | 'dialect'> = {},
+): string => {
+  const { sql, parameters } = compileQuery(dsl, { ...options, table, dialect: 'mysql' })
+  return inlineParameters(sql, parameters, 'mysql')
+}
diff --git a/packages/vquery/src/sql-builder/dialect/compilationDialect.ts b/packages/vquery/src/sql-builder/dialect/compilationDialect.ts
new file mode 100644
index 0000000000..68414d4daf
--- /dev/null
+++ b/packages/vquery/src/sql-builder/dialect/compilationDialect.ts
@@ -0,0 +1,14 @@
+import { DummyDriver } from 'kysely'
+import type { Dialect, DialectAdapter, QueryCompiler } from 'kysely'
+
+// Compilation never opens a database connection or introspects a live schema.
+export const compilationDialect = (compiler: QueryCompiler, adapter: DialectAdapter): Dialect => ({
+  createDriver: () => new DummyDriver(),
+  createQueryCompiler: () => compiler,
+  createAdapter: () => adapter,
+  createIntrospector: () => ({
+    getSchemas: async () => [],
+    getTables: async () => [],
+    getMetadata: async () => ({ tables: [] }),
+  }),
+})
diff --git a/packages/vquery/src/sql-builder/dialect/duckdb.ts b/packages/vquery/src/sql-builder/dialect/duckdb.ts
new file mode 100644
index 0000000000..8cacd61f31
--- /dev/null
+++ b/packages/vquery/src/sql-builder/dialect/duckdb.ts
@@ -0,0 +1,46 @@
+import { PostgresAdapter, PostgresQueryCompiler, sql } from 'kysely'
+import { compilationDialect } from './compilationDialect'
+import type { SQLStrategy } from '../types'
+import { createSource, normalizeValue } from './source'
+
+const formats: Record<string, string> = {
+  to_year: '%Y',
+  to_month: '%Y-%m',
+  to_day: '%Y-%m-%d',
+  to_week: '%Y-W%W',
+  to_hour: '%Y-%m-%d %H',
+  to_minute: '%Y-%m-%d %H:%M',
+  to_second: '%Y-%m-%d %H:%M:%S',
+}
+
+export const duckdbStrategy: SQLStrategy = {
+  dialect: compilationDialect(new PostgresQueryCompiler(), new PostgresAdapter()),
+  source: (db, table, columns) => createSource(db, table, columns, (field) => sql`${field} collate "binary"`),
+  prepare: (query) => ({ query, aggregate: duckdbStrategy.aggregate }),
+  value: (value) => sql.val(normalizeValue(value)),
+  limit: (value) => value,
+  aggregate: (name, aggr) => {
+    const field = sql.ref(name)
+    const { func } = aggr
+    if (formats[func]) return sql`strftime(CAST(${field} AS TIMESTAMP), ${formats[func]})`
+    switch (func) {
+      case 'to_quarter':
+        return sql`strftime(CAST(${field} AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST(${field} AS TIMESTAMP))`
+      case 'count':
+        return sql`count(${field})`
+      case 'count_distinct':
+        return sql`count(distinct ${field})`
+      case 'variance':
+        return sql`var_samp(${field})`
+      case 'variance_pop':
+        return sql`var_pop(${field})`
+      case 'quantile':
+        return sql`quantile(${field}, CAST(${aggr.quantile ?? 0.5} AS DOUBLE))`
+      default:
+        return sql`${sql.raw(func)}(${field})`
+    }
+  },
+  like: (field, value, negated, insensitive) =>
+    sql<boolean>`${field} ${sql.raw(`${negated ? 'not ' : ''}${insensitive ? 'ilike' : 'like'}`)} ${sql.val(normalizeValue(value))} escape ''`,
+  order: (field, direction, nulls) => [sql`${sql.ref(field)} ${sql.raw(direction)} nulls ${sql.raw(nulls)}`],
+}
diff --git a/packages/vquery/src/sql-builder/dialect/index.ts b/packages/vquery/src/sql-builder/dialect/index.ts
index 9ce5bf1fac..5b505d37c2 100644
--- a/packages/vquery/src/sql-builder/dialect/index.ts
+++ b/packages/vquery/src/sql-builder/dialect/index.ts
@@ -1 +1,2 @@
-export { PostgresDialect } from './postgresDialect'
+export { mysqlStrategy } from './mysql'
+export { duckdbStrategy } from './duckdb'
diff --git a/packages/vquery/src/sql-builder/dialect/mysql.ts b/packages/vquery/src/sql-builder/dialect/mysql.ts
new file mode 100644
index 0000000000..81fb56fecf
--- /dev/null
+++ b/packages/vquery/src/sql-builder/dialect/mysql.ts
@@ -0,0 +1,62 @@
+import { MysqlAdapter, MysqlQueryCompiler, sql } from 'kysely'
+import { compilationDialect } from './compilationDialect'
+import type { SQLStrategy } from '../types'
+import { createSource, normalizeValue } from './source'
+import { prepareMysqlStatistics } from './mysqlStatistics'
+
+const text = (field: ReturnType<typeof sql.ref>) => sql`convert(${field} using utf8mb4) collate utf8mb4_0900_bin`
+
+const formats: Record<string, string> = {
+  to_year: '%Y',
+  to_month: '%Y-%m',
+  to_day: '%Y-%m-%d',
+  to_hour: '%Y-%m-%d %H',
+  to_minute: '%Y-%m-%d %H:%i',
+  to_second: '%Y-%m-%d %H:%i:%s',
+}
+
+export const mysqlStrategy: SQLStrategy = {
+  dialect: compilationDialect(new MysqlQueryCompiler(), new MysqlAdapter()),
+  source: (db, table, columns) => createSource(db, table, columns, text),
+  prepare: (query, dsl, db, columns) => prepareMysqlStatistics(query, dsl, db, mysqlStrategy, columns),
+  value: (value) => {
+    const normalized = normalizeValue(value)
+    return typeof normalized === 'string' ? text(sql.val(normalized)) : sql.val(normalized)
+  },
+  // mysql2 encodes JS numbers as DOUBLE, which some MySQL versions reject
+  // for LIMIT placeholders. validateQuery has already checked this integer.
+  limit: (value) => sql.lit(value),
+  aggregate: (name, aggr) => {
+    const field = sql.ref(name)
+    const { func } = aggr
+    if (formats[func]) return sql`date_format(${field}, ${formats[func]})`
+    switch (func) {
+      case 'to_week':
+        return sql`concat(date_format(${field}, '%Y-W'), lpad(week(${field}, 5), 2, '0'))`
+      case 'to_quarter':
+        return sql`concat(date_format(${field}, '%Y'), '-Q', quarter(${field}))`
+      case 'count_distinct':
+        return sql`count(distinct ${field})`
+      case 'variance':
+        return sql`var_samp(${field})`
+      case 'variance_pop':
+        return sql`var_pop(${field})`
+      case 'stddev':
+        return sql`stddev_samp(${field})`
+      case 'median':
+      case 'quantile':
+        throw new Error('Statistical aggregates cannot be used as grouping keys')
+      default:
+        return sql`${sql.raw(func)}(${field})`
+    }
+  },
+  like: (field, value, negated, insensitive) => {
+    const left = text(field),
+      right = text(sql.val(normalizeValue(value)))
+    return sql<boolean>`${insensitive ? sql`lower(${left})` : left} ${sql.raw(negated ? 'not like' : 'like')} ${insensitive ? sql`lower(${right})` : right} escape ''`
+  },
+  order: (field, direction, nulls) => [
+    sql`${sql.ref(field)} is null ${sql.raw(nulls === 'last' ? 'asc' : 'desc')}`,
+    sql`${sql.ref(field)} ${sql.raw(direction)}`,
+  ],
+}
diff --git a/packages/vquery/src/sql-builder/dialect/mysqlStatistics.ts b/packages/vquery/src/sql-builder/dialect/mysqlStatistics.ts
new file mode 100644
index 0000000000..a2c3a62787
--- /dev/null
+++ b/packages/vquery/src/sql-builder/dialect/mysqlStatistics.ts
@@ -0,0 +1,83 @@
+import { sql } from 'kysely'
+import type { Kysely, SelectExpression } from 'kysely'
+import type { DatasetColumn, QueryDSL } from '../../types'
+import type { AggregateBuilder, QueryBuilder, SQLStrategy } from '../types'
+
+export function prepareMysqlStatistics(
+  query: QueryBuilder,
+  dsl: QueryDSL<Record<string, unknown>>,
+  db: Kysely<any>,
+  strategy: SQLStrategy,
+  columns: DatasetColumn[],
+) {
+  const fields = new Set<string>()
+  const statistical = new Set<string>()
+  const used = new Set<string>()
+  const selectItems = dsl.select.map((item) => (typeof item === 'string' ? { field: item } : item))
+  for (const item of selectItems) {
+    fields.add(item.field)
+    used.add(item.alias ?? item.field)
+    if (item.aggr?.func === 'median' || item.aggr?.func === 'quantile') statistical.add(item.field)
+  }
+  const visit = (node: any) => {
+    if ('conditions' in node) node.conditions.forEach(visit)
+    else {
+      fields.add(node.field)
+      if (node.aggr?.func === 'median' || node.aggr?.func === 'quantile') statistical.add(node.field)
+    }
+  }
+  if (dsl.having) visit(dsl.having)
+  if (!statistical.size) return { query, aggregate: strategy.aggregate }
+
+  const aliases = new Map(selectItems.map((item) => [item.alias ?? item.field, item]))
+  const sourceNames = new Set([...fields, ...columns.map((column) => column.name)])
+  const groups = (dsl.groupBy ?? []).map((name) => {
+    // Both engines resolve source columns before SELECT aliases in GROUP BY.
+    const item = sourceNames.has(name) ? undefined : aliases.get(name)
+    const field = item?.field ?? name
+    fields.add(field)
+    return item?.aggr ? strategy.aggregate(field, item.aggr) : sql.ref(field)
+  })
+  for (const item of dsl.orderBy ?? []) {
+    if (!aliases.has(item.field)) fields.add(item.field)
+  }
+  for (const field of fields) used.add(field)
+  let serial = 0
+  const unique = () => {
+    let name: string
+    do name = `__vquery_stat_${serial++}`
+    while (used.has(name))
+    used.add(name)
+    return name
+  }
+  const partition = groups.length ? sql`partition by ${sql.join(groups)}` : sql``
+  const windows = new Map<string, { rank: string; count: string }>()
+  const projections: SelectExpression<any, any>[] = [...fields]
+  for (const field of statistical) {
+    const rank = unique(),
+      count = unique()
+    windows.set(field, { rank, count })
+    projections.push(
+      sql`row_number() over (${partition} order by ${sql.ref(field)} is null, ${sql.ref(field)})`.as(rank),
+      sql`count(${sql.ref(field)}) over (${partition})`.as(count),
+    )
+  }
+  const ranked = query.select(projections).as('__vquery_ranked')
+  const aggregate: AggregateBuilder = (field, aggr) => {
+    if (aggr.func !== 'median' && aggr.func !== 'quantile') return strategy.aggregate(field, aggr)
+    const names = windows.get(field)!
+    const rank = sql.ref(names.rank),
+      count = sql.ref(names.count),
+      value = sql.ref(field)
+    if (aggr.func === 'median') {
+      return sql`avg(case when ${rank} in (floor((${count} + 1) / 2), floor((${count} + 2) / 2)) then ${value} end)`
+    }
+    const quantile = aggr.quantile ?? 0.5
+    // Match DuckDB's DOUBLE discrete-quantile index, including rounding at
+    // integer boundaries: n - floor(n - n*q), rather than ceil(n*q).
+    const position = sql`greatest(${count} - floor(${count} - ${count} * CAST(${Math.abs(quantile)} AS DOUBLE)), 1)`
+    const target = quantile < 0 ? sql`${count} + 1 - ${position}` : position
+    return sql`max(case when ${rank} = ${target} then ${value} end)`
+  }
+  return { query: db.selectFrom(ranked), aggregate }
+}
diff --git a/packages/vquery/src/sql-builder/dialect/postgresDialect.ts b/packages/vquery/src/sql-builder/dialect/postgresDialect.ts
deleted file mode 100644
index 4e09c12085..0000000000
--- a/packages/vquery/src/sql-builder/dialect/postgresDialect.ts
+++ /dev/null
@@ -1,41 +0,0 @@
-import { DummyDriver } from 'kysely'
-import { PostgresQueryCompiler } from 'kysely'
-import { PostgresAdapter } from 'kysely'
-import type { Kysely } from 'kysely'
-import type {
-  DatabaseIntrospector,
-  SchemaMetadata,
-  TableMetadata,
-  DatabaseMetadata,
-  DatabaseMetadataOptions,
-  Dialect,
-} from 'kysely'
-
-export class PostgresDialect implements Dialect {
-  createDriver() {
-    return new DummyDriver()
-  }
-  createQueryCompiler() {
-    return new PostgresQueryCompiler()
-  }
-  createAdapter() {
-    return new PostgresAdapter()
-  }
-  createIntrospector<DB = unknown>(db: Kysely<DB>): DatabaseIntrospector {
-    void db
-    class NullIntrospector implements DatabaseIntrospector {
-      async getSchemas(): Promise<SchemaMetadata[]> {
-        return []
-      }
-      async getTables(options?: DatabaseMetadataOptions): Promise<TableMetadata[]> {
-        void options?.withInternalKyselyTables
-        return []
-      }
-      async getMetadata(options?: DatabaseMetadataOptions): Promise<DatabaseMetadata> {
-        void options?.withInternalKyselyTables
-        return { tables: [] }
-      }
-    }
-    return new NullIntrospector()
-  }
-}
diff --git a/packages/vquery/src/sql-builder/dialect/source.ts b/packages/vquery/src/sql-builder/dialect/source.ts
new file mode 100644
index 0000000000..50bf5c4837
--- /dev/null
+++ b/packages/vquery/src/sql-builder/dialect/source.ts
@@ -0,0 +1,27 @@
+import { sql } from 'kysely'
+import type { Kysely, RawBuilder } from 'kysely'
+import type { DatasetColumn } from '../../types'
+import type { QueryTable } from '../types'
+
+export const createSource = (
+  db: Kysely<any>,
+  table: QueryTable,
+  columns: DatasetColumn[],
+  text: (field: RawBuilder<unknown>) => RawBuilder<unknown>,
+) => {
+  const query = typeof table === 'string' ? db.selectFrom(table) : db.withSchema(table.database).selectFrom(table.name)
+  if (!columns.some((column) => column.type === 'string')) return query
+  // Collation is part of the logical string type, not a database/table default.
+  const source = query
+    .select(
+      columns.map((column) => {
+        const field = sql.ref(column.name)
+        return (column.type === 'string' ? text(field) : field).as(column.name)
+      }),
+    )
+    .as('__vquery_source')
+  return db.selectFrom(source)
+}
+
+export const normalizeValue = (value: unknown): unknown =>
+  value instanceof Date ? value.toISOString().slice(0, 23).replace('T', ' ') : value
diff --git a/packages/vquery/src/sql-builder/dslToSQL.ts b/packages/vquery/src/sql-builder/dslToSQL.ts
deleted file mode 100644
index f79a5ccea5..0000000000
--- a/packages/vquery/src/sql-builder/dslToSQL.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-import type { QueryDSL, VQueryDSL } from '../types'
-import { Kysely } from 'kysely'
-import { PostgresDialect } from './dialect'
-import { inlineParameters } from './compile'
-import { applyWhere, applyGroupBy, applyHaving, applyLimit, applyOrder, applySelect } from './builders'
-
-export const convertDSLToSQL = <T, TableName extends string>(
-  dsl: QueryDSL<T> | VQueryDSL<T>,
-  tableName: TableName,
-): string => {
-  const db = new Kysely<Record<TableName, T>>({
-    dialect: new PostgresDialect(),
-  })
-
-  let qb = db.selectFrom(tableName)
-
-  qb = applySelect(qb, dsl.select)
-  qb = applyWhere(qb, dsl.where)
-  qb = applyGroupBy(qb, dsl.groupBy as string[] | undefined)
-  qb = applyHaving(qb, dsl.having)
-  qb = applyOrder(qb, dsl.orderBy as Array<{ field: string; order?: 'asc' | 'desc' }> | undefined)
-  qb = applyLimit(qb, dsl.limit)
-
-  const { sql, parameters } = qb.compile()
-  return inlineParameters(sql, parameters)
-}
diff --git a/packages/vquery/src/sql-builder/index.ts b/packages/vquery/src/sql-builder/index.ts
index 39b51dbea4..7a20b4802c 100644
--- a/packages/vquery/src/sql-builder/index.ts
+++ b/packages/vquery/src/sql-builder/index.ts
@@ -1 +1,4 @@
-export { convertDSLToSQL } from './dslToSQL'
+export { compileQuery } from './compileQuery'
+export { convertDSLToMysql } from './convertDSLToMysql'
+export { convertDSLToDuckDB } from './convertDSLToDuckDB'
+export type { CompileQueryOptions, CompiledQuery, QueryDialect, QueryTable } from './types'
diff --git a/packages/vquery/src/sql-builder/types.ts b/packages/vquery/src/sql-builder/types.ts
new file mode 100644
index 0000000000..dff8a87512
--- /dev/null
+++ b/packages/vquery/src/sql-builder/types.ts
@@ -0,0 +1,36 @@
+import type { Dialect, RawBuilder, Kysely, SelectQueryBuilder } from 'kysely'
+import type { DatasetColumn, QueryDSL } from '../types'
+import type { SelectItem } from '../types/dsl/Select'
+
+export type QueryDialect = 'mysql' | 'duckdb'
+export type QueryTable = string | { database: string; name: string }
+export type QueryBuilder = SelectQueryBuilder<any, any, any>
+export type AggregateBuilder = (field: string, aggr: NonNullable<SelectItem<unknown>['aggr']>) => RawBuilder<unknown>
+
+export interface CompileQueryOptions {
+  table: QueryTable
+  dialect?: QueryDialect
+  /** Full logical source schema, used to align string collations across engines. */
+  columns?: DatasetColumn[]
+}
+
+export interface CompiledQuery {
+  sql: string
+  parameters: readonly unknown[]
+}
+
+export interface SQLStrategy {
+  dialect: Dialect
+  source: (db: Kysely<any>, table: QueryTable, columns: DatasetColumn[]) => QueryBuilder
+  prepare: (
+    query: QueryBuilder,
+    dsl: QueryDSL<any>,
+    db: Kysely<any>,
+    columns: DatasetColumn[],
+  ) => { query: QueryBuilder; aggregate: AggregateBuilder }
+  limit: (value: number) => number | RawBuilder<number>
+  aggregate: AggregateBuilder
+  value: (value: unknown) => RawBuilder<unknown>
+  like: (field: RawBuilder<unknown>, value: unknown, negated: boolean, insensitive: boolean) => RawBuilder<boolean>
+  order: (field: string, direction: 'asc' | 'desc', nulls: 'first' | 'last') => RawBuilder<unknown>[]
+}
diff --git a/packages/vquery/src/sql-builder/validateQuery.ts b/packages/vquery/src/sql-builder/validateQuery.ts
new file mode 100644
index 0000000000..50b50e3c17
--- /dev/null
+++ b/packages/vquery/src/sql-builder/validateQuery.ts
@@ -0,0 +1,176 @@
+import type { DatasetColumn } from '../types'
+import { toSqlOperator } from './utils'
+
+const aggregates = [
+  'count',
+  'count_distinct',
+  'sum',
+  'avg',
+  'min',
+  'max',
+  'variance',
+  'variance_pop',
+  'stddev',
+  'median',
+  'quantile',
+]
+const dates = ['to_year', 'to_quarter', 'to_month', 'to_week', 'to_day', 'to_hour', 'to_minute', 'to_second']
+const operators = [
+  '=',
+  '!=',
+  '>',
+  '>=',
+  '<',
+  '<=',
+  'like',
+  'not like',
+  'ilike',
+  'not ilike',
+  'in',
+  'not in',
+  'between',
+  'not between',
+  'is null',
+  'is not null',
+]
+
+function assert(ok: unknown, message: string): asserts ok {
+  if (!ok) throw new Error(message)
+}
+const object = (value: unknown): value is Record<string, any> =>
+  value !== null && typeof value === 'object' && !Array.isArray(value)
+const name = (value: unknown) => typeof value === 'string' && value.length > 0
+const scalar = (value: unknown) =>
+  (value instanceof Date && Number.isFinite(value.getTime())) ||
+  value === null ||
+  typeof value === 'string' ||
+  typeof value === 'boolean' ||
+  (typeof value === 'number' && Number.isFinite(value))
+
+function aggregate(value: unknown, allowDates: boolean) {
+  assert(object(value), 'Invalid aggregate')
+  assert(aggregates.includes(value.func) || (allowDates && dates.includes(value.func)), 'Unsupported aggregate')
+  assert(
+    Object.keys(value).every((key) => key === 'func' || key === 'quantile'),
+    'Unsupported aggregate property',
+  )
+  if (value.quantile !== undefined) {
+    assert(
+      value.func === 'quantile' &&
+        typeof value.quantile === 'number' &&
+        Number.isFinite(value.quantile) &&
+        value.quantile >= -1 &&
+        value.quantile <= 1,
+      'Invalid quantile',
+    )
+  }
+}
+
+function predicate(node: unknown, having: boolean) {
+  assert(object(node), 'Invalid filter')
+  if ('conditions' in node) {
+    assert((node.op === 'and' || node.op === 'or') && Array.isArray(node.conditions), 'Invalid filter group')
+    node.conditions.forEach((child: unknown) => predicate(child, having))
+    return
+  }
+  assert(name(node.field), 'Invalid filter field')
+  if (having) {
+    assert(
+      object(node.aggr) && aggregates.includes(node.aggr.func),
+      `Invalid having clause for field "${node.field}": aggr.func is required`,
+    )
+    aggregate(node.aggr, false)
+  }
+  assert(typeof node.op === 'string', 'Invalid filter operator')
+  const op = toSqlOperator(having ? node.op.trim().toLowerCase() : node.op)
+  assert(operators.includes(op) && (!having || !op.includes('like')), 'Unsupported filter operator')
+  if (op === 'is null' || op === 'is not null') return
+  if (op === 'in' || op === 'not in') {
+    assert(
+      Array.isArray(node.value) ? node.value.length > 0 && node.value.every(scalar) : scalar(node.value),
+      'Invalid IN values',
+    )
+  } else if (op === 'between' || op === 'not between') {
+    assert(Array.isArray(node.value) && node.value.length === 2 && node.value.every(scalar), 'Invalid range')
+  } else assert(scalar(node.value), 'Invalid filter value')
+}
+
+export function validateQuery(dsl: unknown, columns: DatasetColumn[] = []) {
+  assert(object(dsl), 'Expected query DSL')
+  assert(
+    Object.keys(dsl).every((key) => ['select', 'where', 'groupBy', 'having', 'orderBy', 'limit'].includes(key)),
+    'Unsupported query property',
+  )
+  const numeric = ['sum', 'avg', 'variance', 'variance_pop', 'stddev', 'median', 'quantile']
+  const columnTypes = new Map(columns.map((column) => [column.name, column.type]))
+  const checkType = (field: string, aggr: { func: string }) => {
+    if (columns.length && numeric.includes(aggr.func)) {
+      assert(columnTypes.get(field) === 'number', `${aggr.func} requires a number column: ${field}`)
+    }
+  }
+  const checkValues = (node: any, having: boolean) => {
+    if ('conditions' in node) {
+      node.conditions.forEach((child: unknown) => checkValues(child, having))
+      return
+    }
+    const fieldType = columnTypes.get(node.field)
+    const type = having && node.aggr.func !== 'min' && node.aggr.func !== 'max' ? 'number' : fieldType
+    const op = node.op.trim().toLowerCase()
+    if (op.includes('like'))
+      assert(node.value === null || typeof node.value === 'string', 'LIKE requires a string pattern')
+    if (columns.length && op.includes('like')) assert(type === 'string', `LIKE requires a string column: ${node.field}`)
+    if (!columns.length || !type || op.includes('like') || op === 'is null' || op === 'is not null') return
+    const values = Array.isArray(node.value) ? node.value : [node.value]
+    assert(
+      values.every(
+        (value: unknown) =>
+          value === null ||
+          (type === 'number'
+            ? typeof value === 'number'
+            : type === 'string'
+              ? typeof value === 'string'
+              : typeof value === 'string' || value instanceof Date),
+      ),
+      `Filter value does not match ${type} column: ${node.field}`,
+    )
+  }
+  assert(Array.isArray(dsl.select), 'Invalid select')
+  for (const item of dsl.select) {
+    if (typeof item === 'string') assert(name(item), 'Invalid select field')
+    else {
+      assert(object(item) && name(item.field), 'Invalid select item')
+      assert(item.alias === undefined || name(item.alias), 'Invalid select alias')
+      if (item.aggr !== undefined) {
+        aggregate(item.aggr, true)
+        checkType(item.field, item.aggr)
+      }
+    }
+  }
+  if (dsl.where !== undefined) {
+    predicate(dsl.where, false)
+    checkValues(dsl.where, false)
+  }
+  if (dsl.having !== undefined) {
+    predicate(dsl.having, true)
+    checkValues(dsl.having, true)
+    const checkHaving = (node: any) => {
+      if ('conditions' in node) node.conditions.forEach(checkHaving)
+      else checkType(node.field, node.aggr)
+    }
+    checkHaving(dsl.having)
+  }
+  if (dsl.groupBy !== undefined) assert(Array.isArray(dsl.groupBy) && dsl.groupBy.every(name), 'Invalid groupBy')
+  if (dsl.orderBy !== undefined)
+    assert(
+      Array.isArray(dsl.orderBy) &&
+        dsl.orderBy.every(
+          (item: unknown) =>
+            object(item) &&
+            name(item.field) &&
+            (item.order === undefined || item.order === 'asc' || item.order === 'desc') &&
+            (item.nulls === undefined || item.nulls === 'first' || item.nulls === 'last'),
+        ),
+      'Invalid orderBy',
+    )
+  if (dsl.limit !== undefined) assert(Number.isSafeInteger(dsl.limit) && dsl.limit >= 0, 'Invalid limit')
+}
diff --git a/packages/vquery/src/types/VQueryOptions.ts b/packages/vquery/src/types/VQueryOptions.ts
new file mode 100644
index 0000000000..ef65f6e570
--- /dev/null
+++ b/packages/vquery/src/types/VQueryOptions.ts
@@ -0,0 +1,4 @@
+export interface VQueryOptions {
+  /** Persist datasets in IndexedDB. Defaults to false; requires IndexedDB support. */
+  enableIndexedDB?: boolean
+}
diff --git a/packages/vquery/src/types/dsl/OrderBy.ts b/packages/vquery/src/types/dsl/OrderBy.ts
index e71646f8d3..96df7ca6f3 100644
--- a/packages/vquery/src/types/dsl/OrderBy.ts
+++ b/packages/vquery/src/types/dsl/OrderBy.ts
@@ -1,4 +1,6 @@
 export type OrderBy<T> = Array<{
   field: string | keyof T
   order?: 'asc' | 'desc'
+  /** NULL values sort last by default, independent of direction. */
+  nulls?: 'first' | 'last'
 }>
diff --git a/packages/vquery/src/types/index.ts b/packages/vquery/src/types/index.ts
index 6acecc8732..34f3ee1f4f 100644
--- a/packages/vquery/src/types/index.ts
+++ b/packages/vquery/src/types/index.ts
@@ -3,3 +3,4 @@ export * from './DataSource'
 export * from './QueryResult'
 export * from './dsl'
 export * from './adapters'
+export * from './VQueryOptions'
diff --git a/packages/vquery/src/vquery-browser.ts b/packages/vquery/src/vquery-browser.ts
index 2c2c483ed3..833cad1130 100644
--- a/packages/vquery/src/vquery-browser.ts
+++ b/packages/vquery/src/vquery-browser.ts
@@ -1,9 +1,10 @@
-import { IndexedDBAdapter } from './adapters'
+import { IndexedDBAdapter, InMemoryAdapter } from './adapters'
 import { InMemoryQueryAdapter } from './adapters/query-adapter/inMemoryQueryAdapter'
 import { VQuery as VQueryCore } from './VQuery'
+import type { VQueryOptions } from './types'
 
 export class VQuery extends VQueryCore {
-  constructor() {
-    super(new InMemoryQueryAdapter(), new IndexedDBAdapter())
+  constructor({ enableIndexedDB = false }: VQueryOptions = {}) {
+    super(new InMemoryQueryAdapter(), enableIndexedDB ? new IndexedDBAdapter() : new InMemoryAdapter())
   }
 }
diff --git a/packages/vquery/src/vquery-node.ts b/packages/vquery/src/vquery-node.ts
index 0f1d9160b9..833cad1130 100644
--- a/packages/vquery/src/vquery-node.ts
+++ b/packages/vquery/src/vquery-node.ts
@@ -1,9 +1,10 @@
-import { InMemoryAdapter } from './adapters'
+import { IndexedDBAdapter, InMemoryAdapter } from './adapters'
 import { InMemoryQueryAdapter } from './adapters/query-adapter/inMemoryQueryAdapter'
 import { VQuery as VQueryCore } from './VQuery'
+import type { VQueryOptions } from './types'
 
 export class VQuery extends VQueryCore {
-  constructor() {
-    super(new InMemoryQueryAdapter(), new InMemoryAdapter())
+  constructor({ enableIndexedDB = false }: VQueryOptions = {}) {
+    super(new InMemoryQueryAdapter(), enableIndexedDB ? new IndexedDBAdapter() : new InMemoryAdapter())
   }
 }
diff --git a/packages/vquery/tests/dialects/__snapshots__/compile.test.ts.snap b/packages/vquery/tests/dialects/__snapshots__/compile.test.ts.snap
new file mode 100644
index 0000000000..41ab3b0fcb
--- /dev/null
+++ b/packages/vquery/tests/dialects/__snapshots__/compile.test.ts.snap
@@ -0,0 +1,10946 @@
+// Rstest Snapshot v1
+
+exports[`DSL to SQL snapshots > GROUP BY source takes precedence over a colliding alias 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select sum("profit") as "product_type", median("sales") as "median" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "product_type"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select sum(\`profit\`) as \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`median\` from (select \`profit\`, \`sales\`, \`product_type\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\` group by \`product_type\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > GROUP BY source takes precedence over a selected aggregate without alias 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select sum("profit") as "profit", median("sales") as "median" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "profit"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select sum(\`profit\`) as \`profit\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`median\` from (select \`profit\`, \`sales\`, row_number() over (partition by \`profit\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`profit\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\` group by \`profit\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      4,
+    ],
+    "sql": "select sum("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having (sum("sales") between $1 and $2)",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      4,
+    ],
+    "sql": "select sum(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having (sum(\`sales\`) between ? and ?)",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select sum("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having (sum("sales") is not null)",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select sum(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having (sum(\`sales\`) is not null)",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select sum("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having (sum("sales") is null)",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select sum(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having (sum(\`sales\`) is null)",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (avg("sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (avg(\`sales\`) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix avg not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not avg("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (not avg(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count("sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(\`sales\`) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not count("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (not count(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (count(distinct "sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (count(distinct \`sales\`) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix count_distinct not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not count(distinct "sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (not count(distinct \`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (max("sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (max(\`sales\`) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix max not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not max("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (not max(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (median("sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix median not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not median("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (not avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (min("sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (min(\`sales\`) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix min not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not min("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (not min(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) != $8 or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) < $8 or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) <= $8 or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) = $8 or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) > $8 or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) >= $8 or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) between $8 and $9 or (count("id") > $10 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $11",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) in ($8, $9, $10) or (count("id") > $11 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $12",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) is not null or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) is null or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (quantile("sales", CAST($7 AS DOUBLE)) not between $8 and $9 or (count("id") > $10 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $11",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix quantile not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $2 and $3 or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (not quantile("sales", CAST($7 AS DOUBLE)) in ($8, $9, $10) or (count("id") > $11 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $12",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (not max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (stddev("sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (stddev_samp(\`sales\`) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix stddev not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not stddev("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (not stddev_samp(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (sum(\`sales\`) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix sum not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not sum("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (not sum(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_samp("sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_samp(\`sales\`) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not var_samp("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (not var_samp(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") != $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) != ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") < $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) < ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") <= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) <= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") = $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) = ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") > $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) > ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") >= $6 or (count("id") > $7 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) >= ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") is not null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) is not null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") is null or (count("id") > $6 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) is null or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (var_pop("sales") not between $6 and $7 or (count("id") > $8 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      -10,
+      100,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (var_pop(\`sales\`) not between ? and ? or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING matrix variance_pop not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (not var_pop("sales") in ($6, $7, $8) or (count("id") > $9 and max("profit") is not null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      1,
+      1,
+      "技术",
+      "家具",
+      0,
+      10,
+      null,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`product_type\`, \`product_name\` having (not var_pop(\`sales\`) in (?, ?, ?) or (count(\`id\`) > ? and max(\`profit\`) is not null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      4,
+    ],
+    "sql": "select sum("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having (sum("sales") not between $1 and $2)",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      4,
+    ],
+    "sql": "select sum(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having (sum(\`sales\`) not between ? and ?)",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING only median and nested count 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0,
+      1,
+    ],
+    "sql": "select count("id") as "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having ((median("sales") > $1) and count("id") >= $2)",
+  },
+  "mysql": {
+    "parameters": [
+      0,
+      1,
+    ],
+    "sql": "select count(\`id\`) as \`id\` from (select \`id\`, \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\` having ((avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) > ?) and count(\`id\`) >= ?)",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > HAVING statistics absent from projection 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2.5,
+      -0.5,
+      5,
+    ],
+    "sql": "select "product_type", sum("sales") as "sum" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "product_type" having (median("sales") >= $1 or quantile("profit", CAST($2 AS DOUBLE)) > $3) order by "product_type" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      2.5,
+      0.5,
+      5,
+    ],
+    "sql": "select \`product_type\`, sum(\`sales\`) as \`sum\` from (select \`product_type\`, \`sales\`, \`profit\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\`, row_number() over (partition by \`product_type\` order by \`profit\` is null, \`profit\`) as \`__vquery_stat_2\`, count(\`profit\`) over (partition by \`product_type\`) as \`__vquery_stat_3\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\` group by \`product_type\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? or max(case when \`__vquery_stat_2\` = \`__vquery_stat_3\` + 1 - greatest(\`__vquery_stat_3\` - floor(\`__vquery_stat_3\` - \`__vquery_stat_3\` * CAST(? AS DOUBLE)), 1) then \`profit\` end) > ?) order by \`product_type\` is null asc, \`product_type\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > LIKE NULL 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      null,
+    ],
+    "sql": "select "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" like $1 escape '')",
+  },
+  "mysql": {
+    "parameters": [
+      null,
+    ],
+    "sql": "select \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '')",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > NULL ordering asc default 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      8,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" order by "sales" asc nulls last, "id" asc nulls last limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` order by \`sales\` is null asc, \`sales\` asc, \`id\` is null asc, \`id\` asc limit 8",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > NULL ordering asc first 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      8,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" order by "sales" asc nulls first, "id" asc nulls last limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` order by \`sales\` is null desc, \`sales\` asc, \`id\` is null asc, \`id\` asc limit 8",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > NULL ordering asc last 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      8,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" order by "sales" asc nulls last, "id" asc nulls last limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` order by \`sales\` is null asc, \`sales\` asc, \`id\` is null asc, \`id\` asc limit 8",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > NULL ordering desc default 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      8,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" order by "sales" desc nulls last, "id" asc nulls last limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` order by \`sales\` is null asc, \`sales\` desc, \`id\` is null asc, \`id\` asc limit 8",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > NULL ordering desc first 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      8,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" order by "sales" desc nulls first, "id" asc nulls last limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` order by \`sales\` is null desc, \`sales\` desc, \`id\` is null asc, \`id\` asc limit 8",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > NULL ordering desc last 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      8,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" order by "sales" desc nulls last, "id" asc nulls last limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` order by \`sales\` is null asc, \`sales\` desc, \`id\` is null asc, \`id\` asc limit 8",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > all fields and zero limit 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0,
+    ],
+    "sql": "select * from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select * from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` limit 0",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > all-column detail with nested filters and no limit 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "Fiskars%",
+      1,
+      "技术",
+      "家具",
+    ],
+    "sql": "select * from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) order by "order_date" desc nulls first, "id" desc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "Fiskars%",
+      1,
+      "技术",
+      "家具",
+    ],
+    "sql": "select * from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) order by \`order_date\` is null desc, \`order_date\` desc, \`id\` is null asc, \`id\` desc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > array HAVING in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      2,
+    ],
+    "sql": "select sum("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having (sum("sales") in ($1, $2))",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      2,
+    ],
+    "sql": "select sum(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having (sum(\`sales\`) in (?, ?))",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > array HAVING not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      2,
+    ],
+    "sql": "select sum("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having (not sum("sales") in ($1, $2))",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      2,
+    ],
+    "sql": "select sum(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having (not sum(\`sales\`) in (?, ?))",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day avg 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (avg("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (avg(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day count 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day count_distinct 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count(distinct "sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(distinct \`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day max 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (max("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (max(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day median 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (median("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "%Y-%m-%d",
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day min 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (min("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (min(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day quantile 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", quantile("sales", CAST($2 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $3 and $4 and (("sales" >= $5 and "profit" is null) or ("id" >= $6 and not "product_type" in ($7, $8)))) group by "order_period", "product_type" having (quantile("sales", CAST($9 AS DOUBLE)) >= $10 and (count("id") > $11 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $12",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      0.5,
+      "%Y-%m-%d",
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day stddev 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (stddev("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (stddev_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day sum 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (sum("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (sum(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day variance 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_samp("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_day variance_pop 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_pop("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_pop(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour avg 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (avg("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (avg(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour count 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour count_distinct 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count(distinct "sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(distinct \`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour max 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (max("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (max(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour median 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (median("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "%Y-%m-%d %H",
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour min 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (min("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (min(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour quantile 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", quantile("sales", CAST($2 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $3 and $4 and (("sales" >= $5 and "profit" is null) or ("id" >= $6 and not "product_type" in ($7, $8)))) group by "order_period", "product_type" having (quantile("sales", CAST($9 AS DOUBLE)) >= $10 and (count("id") > $11 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $12",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      0.5,
+      "%Y-%m-%d %H",
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour stddev 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (stddev("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (stddev_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour sum 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (sum("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (sum(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour variance 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_samp("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_hour variance_pop 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_pop("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_pop(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute avg 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (avg("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (avg(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute count 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute count_distinct 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count(distinct "sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(distinct \`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute max 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (max("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (max(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute median 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (median("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "%Y-%m-%d %H:%i",
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute min 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (min("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (min(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute quantile 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", quantile("sales", CAST($2 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $3 and $4 and (("sales" >= $5 and "profit" is null) or ("id" >= $6 and not "product_type" in ($7, $8)))) group by "order_period", "product_type" having (quantile("sales", CAST($9 AS DOUBLE)) >= $10 and (count("id") > $11 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $12",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      0.5,
+      "%Y-%m-%d %H:%i",
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute stddev 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (stddev("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (stddev_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute sum 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (sum("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (sum(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute variance 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_samp("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_minute variance_pop 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_pop("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_pop(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month avg 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (avg("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (avg(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month count 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month count_distinct 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count(distinct "sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(distinct \`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month max 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (max("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (max(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month median 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (median("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "%Y-%m",
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month min 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (min("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (min(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month quantile 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", quantile("sales", CAST($2 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $3 and $4 and (("sales" >= $5 and "profit" is null) or ("id" >= $6 and not "product_type" in ($7, $8)))) group by "order_period", "product_type" having (quantile("sales", CAST($9 AS DOUBLE)) >= $10 and (count("id") > $11 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $12",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      0.5,
+      "%Y-%m",
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month stddev 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (stddev("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (stddev_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month sum 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (sum("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (sum(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month variance 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_samp("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_month variance_pop 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_pop("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_pop(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter avg 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (avg("sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (avg(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter count 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (count("sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter count_distinct 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (count(distinct "sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(distinct \`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter max 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (max("sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (max(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter median 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (median("sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter min 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (min("sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (min(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter quantile 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (quantile("sales", CAST($8 AS DOUBLE)) >= $9 and (count("id") > $10 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $11",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter stddev 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (stddev("sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (stddev_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter sum 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (sum("sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (sum(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter variance 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (var_samp("sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_quarter variance_pop 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", "product_type", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("sales" >= $3 and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6)))) group by "order_period", "product_type" having (var_pop("sales") >= $7 and (count("id") > $8 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, \`product_type\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_pop(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second avg 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (avg("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (avg(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second count 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second count_distinct 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count(distinct "sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(distinct \`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second max 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (max("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (max(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second median 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (median("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "%Y-%m-%d %H:%i:%s",
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second min 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (min("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (min(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second quantile 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", quantile("sales", CAST($2 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $3 and $4 and (("sales" >= $5 and "profit" is null) or ("id" >= $6 and not "product_type" in ($7, $8)))) group by "order_period", "product_type" having (quantile("sales", CAST($9 AS DOUBLE)) >= $10 and (count("id") > $11 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $12",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      0.5,
+      "%Y-%m-%d %H:%i:%s",
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second stddev 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (stddev("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (stddev_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second sum 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (sum("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (sum(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second variance 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_samp("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_second variance_pop 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_pop("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_pop(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week avg 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (avg("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (avg(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week count 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week count_distinct 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count(distinct "sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(distinct \`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week max 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (max("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (max(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week median 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (median("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week min 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (min("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (min(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week quantile 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", quantile("sales", CAST($2 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $3 and $4 and (("sales" >= $5 and "profit" is null) or ("id" >= $6 and not "product_type" in ($7, $8)))) group by "order_period", "product_type" having (quantile("sales", CAST($9 AS DOUBLE)) >= $10 and (count("id") > $11 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $12",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week stddev 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (stddev("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (stddev_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week sum 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (sum("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (sum(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week variance 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_samp("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_week variance_pop 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_pop("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" desc nulls first, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, \`product_type\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_pop(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null desc, \`sales_metric\` desc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year avg 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", avg("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (avg("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (avg(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year count 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year count_distinct 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", count(distinct "sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (count(distinct "sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, count(distinct \`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (count(distinct \`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year max 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", max("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (max("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (max(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year median 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (median("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "%Y",
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year min 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", min("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (min("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, min(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (min(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year quantile 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      0.5,
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", quantile("sales", CAST($2 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $3 and $4 and (("sales" >= $5 and "profit" is null) or ("id" >= $6 and not "product_type" in ($7, $8)))) group by "order_period", "product_type" having (quantile("sales", CAST($9 AS DOUBLE)) >= $10 and (count("id") > $11 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $12",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      0.5,
+      "%Y",
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`id\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))))) as \`__vquery_ranked\` group by \`order_period\`, \`product_type\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year stddev 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", stddev("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (stddev("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, stddev_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (stddev_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year sum 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (sum("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, sum(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (sum(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year variance 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_samp("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_samp("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_samp(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_samp(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date aggregate matrix to_year variance_pop 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      25,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", "product_type", var_pop("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and (("sales" >= $4 and "profit" is null) or ("id" >= $5 and not "product_type" in ($6, $7)))) group by "order_period", "product_type" having (var_pop("sales") >= $8 and (count("id") > $9 or min("profit") is null)) order by "sales_metric" asc nulls last, "order_period" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "2016-01-01",
+      "2019-12-30",
+      -10,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, \`product_type\`, var_pop(\`sales\`) as \`sales_metric\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) group by \`order_period\`, \`product_type\` having (var_pop(\`sales\`) >= ? and (count(\`id\`) > ? or min(\`profit\`) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` asc, \`order_period\` is null asc, \`order_period\` asc limit 25",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date grouping to_day 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "order_period" order by "order_period" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, count(\`sales\`) as \`count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` group by \`order_period\` order by \`order_period\` is null asc, \`order_period\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date grouping to_hour 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H",
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "order_period" order by "order_period" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H",
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, count(\`sales\`) as \`count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` group by \`order_period\` order by \`order_period\` is null asc, \`order_period\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date grouping to_minute 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M",
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "order_period" order by "order_period" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i",
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, count(\`sales\`) as \`count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` group by \`order_period\` order by \`order_period\` is null asc, \`order_period\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date grouping to_month 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "order_period" order by "order_period" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, count(\`sales\`) as \`count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` group by \`order_period\` order by \`order_period\` is null asc, \`order_period\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date grouping to_quarter 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("order_date" AS TIMESTAMP)) as "order_period", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "order_period" order by "order_period" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select concat(date_format(\`order_date\`, '%Y'), '-Q', quarter(\`order_date\`)) as \`order_period\`, count(\`sales\`) as \`count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` group by \`order_period\` order by \`order_period\` is null asc, \`order_period\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date grouping to_second 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d %H:%M:%S",
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "order_period" order by "order_period" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d %H:%i:%s",
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, count(\`sales\`) as \`count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` group by \`order_period\` order by \`order_period\` is null asc, \`order_period\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date grouping to_week 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-W%W",
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "order_period" order by "order_period" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select concat(date_format(\`order_date\`, '%Y-W'), lpad(week(\`order_date\`, 5), 2, '0')) as \`order_period\`, count(\`sales\`) as \`count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` group by \`order_period\` order by \`order_period\` is null asc, \`order_period\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date grouping to_year 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_period", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "order_period" order by "order_period" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_period\`, count(\`sales\`) as \`count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` group by \`order_period\` order by \`order_period\` is null asc, \`order_period\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > date grouping with statistical HAVING 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_year", median("sales") as "median" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "order_year" having (median("sales") is not null) order by "order_year" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "%Y",
+      "%Y",
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_year\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`median\` from (select \`order_date\`, \`sales\`, row_number() over (partition by date_format(\`order_date\`, ?) order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?)) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\` group by \`order_year\` having (avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is not null) order by \`order_year\` is null asc, \`order_year\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > deep mixed WHERE HAVING depth 2 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -10000000000,
+      10000000000,
+      "办公用品",
+      "%剪刀%",
+      0.5,
+      -10,
+      100,
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" between $2 and $3 or "product_type" = $4) and "product_name" like $5 escape '') group by "product_type", "product_name" having ((quantile("sales", CAST($6 AS DOUBLE)) between $7 and $8 or sum("sales") <= $9) and median("sales") >= $10) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $11",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -10000000000,
+      10000000000,
+      "办公用品",
+      "%剪刀%",
+      0.5,
+      -10,
+      100,
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` between ? and ? or \`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin) and convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '')) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having ((max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) between ? and ? or sum(\`sales\`) <= ?) and avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ?) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > deep mixed WHERE HAVING depth 4 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -10000000000,
+      10000000000,
+      "办公用品",
+      "%剪刀%",
+      "办公用品",
+      "%剪刀%",
+      0.5,
+      -10,
+      100,
+      1,
+      1,
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (((("sales" between $2 and $3 or "product_type" = $4) and "product_name" like $5 escape '') or "product_type" = $6) and "product_name" like $7 escape '') group by "product_type", "product_name" having ((((quantile("sales", CAST($8 AS DOUBLE)) between $9 and $10 or sum("sales") <= $11) and median("sales") >= $12) or sum("sales") <= $13) and median("sales") >= $14) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $15",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -10000000000,
+      10000000000,
+      "办公用品",
+      "%剪刀%",
+      "办公用品",
+      "%剪刀%",
+      0.5,
+      -10,
+      100,
+      1,
+      1,
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((((\`sales\` between ? and ? or \`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin) and convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') or \`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin) and convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '')) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having ((((max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) between ? and ? or sum(\`sales\`) <= ?) and avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ?) or sum(\`sales\`) <= ?) and avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ?) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > deep mixed WHERE HAVING depth 8 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -10000000000,
+      10000000000,
+      "办公用品",
+      "%剪刀%",
+      "办公用品",
+      "%剪刀%",
+      "办公用品",
+      "%剪刀%",
+      "办公用品",
+      "%剪刀%",
+      0.5,
+      -10,
+      100,
+      1,
+      1,
+      1,
+      1,
+      1,
+      1,
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (((((((("sales" between $2 and $3 or "product_type" = $4) and "product_name" like $5 escape '') or "product_type" = $6) and "product_name" like $7 escape '') or "product_type" = $8) and "product_name" like $9 escape '') or "product_type" = $10) and "product_name" like $11 escape '') group by "product_type", "product_name" having ((((((((quantile("sales", CAST($12 AS DOUBLE)) between $13 and $14 or sum("sales") <= $15) and median("sales") >= $16) or sum("sales") <= $17) and median("sales") >= $18) or sum("sales") <= $19) and median("sales") >= $20) or sum("sales") <= $21) and median("sales") >= $22) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $23",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      -10000000000,
+      10000000000,
+      "办公用品",
+      "%剪刀%",
+      "办公用品",
+      "%剪刀%",
+      "办公用品",
+      "%剪刀%",
+      "办公用品",
+      "%剪刀%",
+      0.5,
+      -10,
+      100,
+      1,
+      1,
+      1,
+      1,
+      1,
+      1,
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((((((((\`sales\` between ? and ? or \`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin) and convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') or \`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin) and convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') or \`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin) and convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') or \`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin) and convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '')) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having ((((((((max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) between ? and ? or sum(\`sales\`) <= ?) and avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ?) or sum(\`sales\`) <= ?) and avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ?) or sum(\`sales\`) <= ?) and avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ?) or sum(\`sales\`) <= ?) and avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) >= ?) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > discrete quantile -0.01 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -0.01,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "result" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      0.01,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = \`__vquery_stat_1\` + 1 - greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`result\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > discrete quantile -0.5 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -0.5,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "result" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = \`__vquery_stat_1\` + 1 - greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`result\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > discrete quantile -0.9 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -0.9,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "result" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      0.9,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = \`__vquery_stat_1\` + 1 - greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`result\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > discrete quantile -1 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "result" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = \`__vquery_stat_1\` + 1 - greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`result\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > discrete quantile 0 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "result" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      0,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`result\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > discrete quantile 0.01 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.01,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "result" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      0.01,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`result\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > discrete quantile 0.5 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "result" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`result\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > discrete quantile 0.9 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.9,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "result" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      0.9,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`result\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > discrete quantile 1 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "result" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`result\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > empty and group 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where 1=1 order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where 1=1 order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > empty grouping and ordering 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0,
+    ],
+    "sql": "select "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` limit 0",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > empty groups embedded in complete statistical query 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      50,
+    ],
+    "sql": "select "product_type", "product_name", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (0=1 and 1=1) group by "product_type", "product_name" having (1=1 or 0=1) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`product_type\`, \`product_name\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (0=1 and 1=1)) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (1=1 or 0=1) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > empty having and 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select count("id") as "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having 1=1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select count(\`id\`) as \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having 1=1",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > empty having or 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select count("id") as "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having 0=1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select count(\`id\`) as \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having 0=1",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > empty or group 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where 0=1 order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where 0=1 order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > empty where and 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select count("id") as "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where 1=1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select count(\`id\`) as \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where 1=1",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > empty where or 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select count("id") as "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where 0=1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select count(\`id\`) as \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where 0=1",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > filter before ranking and limit after aggregation 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.9,
+      2,
+      2,
+    ],
+    "sql": "select "product_type", median("sales") as "median", quantile("sales", CAST($1 AS DOUBLE)) as "q90" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("id" >= $2) group by "product_type" order by "median" desc nulls last, "product_type" asc nulls last limit $3",
+  },
+  "mysql": {
+    "parameters": [
+      0.9,
+      2,
+    ],
+    "sql": "select \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`median\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`q90\` from (select \`product_type\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`id\` >= ?)) as \`__vquery_ranked\` group by \`product_type\` order by \`median\` is null asc, \`median\` desc, \`product_type\` is null asc, \`product_type\` asc limit 2",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > filtered statistical aggregates 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      0,
+    ],
+    "sql": "select median("sales") as "median", quantile("sales", CAST($1 AS DOUBLE)) as "quantile", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("id" < $2)",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      0,
+    ],
+    "sql": "select avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`median\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`quantile\`, count(\`sales\`) as \`count\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`id\` < ?)) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > grouped statistics over multiple fields 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      -0.5,
+    ],
+    "sql": "select "product_type", median("sales") as "median", quantile("sales", CAST($1 AS DOUBLE)) as "quantile", quantile("sales", CAST($2 AS DOUBLE)) as "negative_quantile", median("profit") as "median_profit", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "product_type" order by "product_type" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      0.5,
+    ],
+    "sql": "select \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`median\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`quantile\`, max(case when \`__vquery_stat_0\` = \`__vquery_stat_1\` + 1 - greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`negative_quantile\`, avg(case when \`__vquery_stat_2\` in (floor((\`__vquery_stat_3\` + 1) / 2), floor((\`__vquery_stat_3\` + 2) / 2)) then \`profit\` end) as \`median_profit\`, count(\`sales\`) as \`count\` from (select \`product_type\`, \`sales\`, \`profit\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\`, row_number() over (partition by \`product_type\` order by \`profit\` is null, \`profit\`) as \`__vquery_stat_2\`, count(\`profit\`) over (partition by \`product_type\`) as \`__vquery_stat_3\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\` group by \`product_type\` order by \`product_type\` is null asc, \`product_type\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > internal statistical alias collision 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select median("sales") as "__vquery_stat_0" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select avg(case when \`__vquery_stat_1\` in (floor((\`__vquery_stat_2\` + 1) / 2), floor((\`__vquery_stat_2\` + 2) / 2)) then \`sales\` end) as \`__vquery_stat_0\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_1\`, count(\`sales\`) over () as \`__vquery_stat_2\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > multiple date grouping aliases with multiple statistical fields 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y",
+      "%Y-%m",
+      0.9,
+      -0.1,
+      "2016-01-01",
+      "2019-12-30",
+      1,
+      "技术",
+      "家具",
+      0.1,
+      1,
+      1,
+      1,
+      100,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_year", strftime(CAST("order_date" AS TIMESTAMP), $2) as "order_month", "product_type", median("sales") as "median_value", quantile("sales", CAST($3 AS DOUBLE)) as "q90", quantile("profit", CAST($4 AS DOUBLE)) as "q10_profit", sum("profit") as "total" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("order_date" between $5 and $6 or "profit" is null) and ("id" >= $7 and not "product_type" in ($8, $9))) group by "order_year", "order_month", "product_type" having (quantile("sales", CAST($10 AS DOUBLE)) > $11 or (median("profit") >= $12 and var_samp("sales") > $13)) order by "order_year" asc nulls last, "order_month" asc nulls last, "q90" desc nulls first limit $14",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y",
+      "%Y-%m",
+      0.9,
+      0.1,
+      "%Y",
+      "%Y-%m",
+      "%Y",
+      "%Y-%m",
+      "%Y",
+      "%Y-%m",
+      "%Y",
+      "%Y-%m",
+      "2016-01-01",
+      "2019-12-30",
+      1,
+      "技术",
+      "家具",
+      0.1,
+      1,
+      1,
+      1,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_year\`, date_format(\`order_date\`, ?) as \`order_month\`, \`product_type\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`median_value\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`q90\`, max(case when \`__vquery_stat_2\` = \`__vquery_stat_3\` + 1 - greatest(\`__vquery_stat_3\` - floor(\`__vquery_stat_3\` - \`__vquery_stat_3\` * CAST(? AS DOUBLE)), 1) then \`profit\` end) as \`q10_profit\`, sum(\`profit\`) as \`total\` from (select \`order_date\`, \`product_type\`, \`sales\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), date_format(\`order_date\`, ?), \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_1\`, row_number() over (partition by date_format(\`order_date\`, ?), date_format(\`order_date\`, ?), \`product_type\` order by \`profit\` is null, \`profit\`) as \`__vquery_stat_2\`, count(\`profit\`) over (partition by date_format(\`order_date\`, ?), date_format(\`order_date\`, ?), \`product_type\`) as \`__vquery_stat_3\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`order_year\`, \`order_month\`, \`product_type\` having (max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ? or (avg(case when \`__vquery_stat_2\` in (floor((\`__vquery_stat_3\` + 1) / 2), floor((\`__vquery_stat_3\` + 2) / 2)) then \`profit\` end) >= ? and var_samp(\`sales\`) > ?)) order by \`order_year\` is null asc, \`order_year\` asc, \`order_month\` is null asc, \`order_month\` asc, \`q90\` is null desc, \`q90\` desc limit 100",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" != $1 or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` != ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" < $1 or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` < ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" <= $1 or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` <= ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" = $1 or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` = ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" > $1 or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` > ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" >= $1 or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` >= ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      100,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") > $6 and (count("id") > $7 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      100,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and ilike 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0,
+      1,
+      null,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" in ($1, $2, $3) or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (sum("sales") > $7 and (count("id") > $8 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      0,
+      1,
+      null,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` in (?, ?, ?) or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" is not null or "profit" is null) and ("id" >= $1 and not "product_type" in ($2, $3))) group by "product_type", "product_name" having (sum("sales") > $4 and (count("id") > $5 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $6",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` is not null or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" is null or "profit" is null) and ("id" >= $1 and not "product_type" in ($2, $3))) group by "product_type", "product_name" having (sum("sales") > $4 and (count("id") > $5 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $6",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` is null or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and like 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" like $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      100,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") > $6 and (count("id") > $7 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      100,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and not ilike 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0,
+      1,
+      null,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ((not "sales" in ($1, $2, $3) or "profit" is null) and ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (sum("sales") > $7 and (count("id") > $8 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      0,
+      1,
+      null,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((not \`sales\` in (?, ?, ?) or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix and not like 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not like $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin not like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" != $1 and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` != ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" < $1 and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` < ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" <= $1 and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` <= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" = $1 and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` = ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" > $1 and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` > ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" >= $1 and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` >= ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      100,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" between $1 and $2 and "profit" is null) or ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") > $6 and (count("id") > $7 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      100,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` between ? and ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or ilike 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" ilike $1 escape '' and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0,
+      1,
+      null,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" in ($1, $2, $3) and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (sum("sales") > $7 and (count("id") > $8 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      0,
+      1,
+      null,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` in (?, ?, ?) and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" is not null and "profit" is null) or ("id" >= $1 and not "product_type" in ($2, $3))) group by "product_type", "product_name" having (sum("sales") > $4 and (count("id") > $5 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $6",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` is not null and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" is null and "profit" is null) or ("id" >= $1 and not "product_type" in ($2, $3))) group by "product_type", "product_name" having (sum("sales") > $4 and (count("id") > $5 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $6",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` is null and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or like 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" like $1 escape '' and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '' and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      -1,
+      100,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" not between $1 and $2 and "profit" is null) or ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "product_name" having (sum("sales") > $6 and (count("id") > $7 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      -1,
+      100,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` not between ? and ? and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or not ilike 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0,
+      1,
+      null,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ((not "sales" in ($1, $2, $3) and "profit" is null) or ("id" >= $4 and not "product_type" in ($5, $6))) group by "product_type", "product_name" having (sum("sales") > $7 and (count("id") > $8 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      0,
+      1,
+      null,
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((not \`sales\` in (?, ?, ?) and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > nested WHERE matrix or not like 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+      50,
+    ],
+    "sql": "select "product_type", "product_name", sum("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not like $1 escape '' and "profit" is null) or ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type", "product_name" having (sum("sales") > $5 and (count("id") > $6 or median("sales") is null)) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%打印机%",
+      1,
+      "技术",
+      "家具",
+      1,
+      1,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, sum(\`sales\`) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, \`id\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin not like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '' and \`profit\` is null) or (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (sum(\`sales\`) > ? and (count(\`id\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) is null)) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric aggregates 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+    ],
+    "sql": "select sum("sales") as "sum", avg("sales") as "avg", min("sales") as "min", max("sales") as "max", count("sales") as "count", count(distinct "sales") as "count_distinct", var_samp("sales") as "variance", var_pop("sales") as "variance_pop", stddev("sales") as "stddev", median("sales") as "median", quantile("sales", CAST($1 AS DOUBLE)) as "quantile" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+    ],
+    "sql": "select sum(\`sales\`) as \`sum\`, avg(\`sales\`) as \`avg\`, min(\`sales\`) as \`min\`, max(\`sales\`) as \`max\`, count(\`sales\`) as \`count\`, count(distinct \`sales\`) as \`count_distinct\`, var_samp(\`sales\`) as \`variance\`, var_pop(\`sales\`) as \`variance_pop\`, stddev_samp(\`sales\`) as \`stddev\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`median\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`quantile\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" != $1) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` != ?) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" < $1) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` < ?) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" <= $1) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` <= ?) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" = $1) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` = ?) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" > $1) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` > ?) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" >= $1) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      2,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` >= ?) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      4,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" between $1 and $2) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      4,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` between ? and ?) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      null,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" in ($1, $2)) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      null,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` in (?, ?)) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" is not null) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` is not null) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" is null) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` is null) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      4,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("sales" not between $1 and $2) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      4,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`sales\` not between ? and ?) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > numeric filter not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+      null,
+    ],
+    "sql": "select "id", "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (not "sales" in ($1, $2)) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+      null,
+    ],
+    "sql": "select \`id\`, \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (not \`sales\` in (?, ?)) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > order date ISO parameter 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2019-04-27",
+    ],
+    "sql": "select "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" >= $1) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "2019-04-27",
+    ],
+    "sql": "select \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` >= convert(? using utf8mb4) collate utf8mb4_0900_bin) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name BETWEEN uses codepoint order 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" between $1 and $2) order by "product_name" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`product_name\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin) order by \`product_name\` is null asc, \`product_name\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name IN is exact 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "Fiskars 剪刀, 蓝色",
+      "柯尼卡 打印机, 红色",
+    ],
+    "sql": "select "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" in ($1, $2)) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "Fiskars 剪刀, 蓝色",
+      "柯尼卡 打印机, 红色",
+    ],
+    "sql": "select \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`product_name\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name comparison is exact 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "Fiskars 剪刀, 蓝色",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" = $1) order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "Fiskars 剪刀, 蓝色",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`product_name\` = convert(? using utf8mb4) collate utf8mb4_0900_bin) order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name distinct and extrema 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select count(distinct "product_name") as "distinct_product_names", min("product_name") as "first_product_name", max("product_name") as "last_product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select count(distinct \`product_name\`) as \`distinct_product_names\`, min(\`product_name\`) as \`first_product_name\`, max(\`product_name\`) as \`last_product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name grouping 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select "product_name", count("sales") as "count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "product_name" order by "product_name" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`product_name\`, count(\`sales\`) as \`count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` group by \`product_name\` order by \`product_name\` is null asc, \`product_name\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name ilike %信封% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%信封%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" ilike $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%信封%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name ilike %孔加固材料% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%孔加固材料%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" ilike $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%孔加固材料%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name ilike Fiskars% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "Fiskars%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" ilike $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "Fiskars%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name ilike 柯尼卡% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "柯尼卡%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" ilike $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "柯尼卡%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name like %信封% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%信封%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" like $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%信封%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name like %孔加固材料% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%孔加固材料%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" like $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%孔加固材料%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name like Fiskars% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "Fiskars%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" like $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "Fiskars%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name like 柯尼卡% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "柯尼卡%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" like $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "柯尼卡%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name not ilike %信封% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%信封%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" not ilike $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%信封%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name not ilike %孔加固材料% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%孔加固材料%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" not ilike $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%孔加固材料%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name not ilike Fiskars% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "Fiskars%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" not ilike $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "Fiskars%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name not ilike 柯尼卡% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "柯尼卡%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" not ilike $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "柯尼卡%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name not like %信封% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%信封%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" not like $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%信封%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin not like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name not like %孔加固材料% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%孔加固材料%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" not like $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "%孔加固材料%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin not like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name not like Fiskars% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "Fiskars%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" not like $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "Fiskars%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin not like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > product name not like 柯尼卡% 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "柯尼卡%",
+    ],
+    "sql": "select "id", "product_name" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("product_name" not like $1 escape '') order by "id" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [
+      "柯尼卡%",
+    ],
+    "sql": "select \`id\`, \`product_name\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin not like convert(? using utf8mb4) collate utf8mb4_0900_bin escape '') order by \`id\` is null asc, \`id\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > projection without alias 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+    ],
+    "sql": "select "sales" as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" order by "id" asc nulls last limit $1",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`sales\` as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` order by \`id\` is null asc, \`id\` asc limit 1",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > raw grouping alias 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select "product_type" as "product_category", median("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "product_category" order by "product_category" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select \`product_type\` as \`product_category\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales\` from (select \`product_type\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\` group by \`product_category\` order by \`product_category\` is null asc, \`product_category\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > signed fractional and scientific notation parameters with zero result limit 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+      0,
+      0.125,
+      -123.456,
+      1e-12,
+      1000000000000,
+      null,
+      1,
+      "技术",
+      "家具",
+      -0.14,
+      -1000000,
+      1000000,
+      0,
+    ],
+    "sql": "select "product_type", "product_name", quantile("sales", CAST($1 AS DOUBLE)) as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" in ($2, $3, $4, $5, $6, $7) or "profit" is null) and ("id" >= $8 and not "product_type" in ($9, $10))) group by "product_type", "product_name" having (quantile("sales", CAST($11 AS DOUBLE)) between $12 and $13) order by "sales_metric" desc nulls last, "product_type" asc nulls last limit $14",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+      0,
+      0.125,
+      -123.456,
+      1e-12,
+      1000000000000,
+      null,
+      1,
+      "技术",
+      "家具",
+      0.14,
+      -1000000,
+      1000000,
+    ],
+    "sql": "select \`product_type\`, \`product_name\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` in (?, ?, ?, ?, ?, ?) or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`product_name\` having (max(case when \`__vquery_stat_0\` = \`__vquery_stat_1\` + 1 - greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) between ? and ?) order by \`sales_metric\` is null asc, \`sales_metric\` desc, \`product_type\` is null asc, \`product_type\` asc limit 0",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > single HAVING in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      10,
+    ],
+    "sql": "select count("id") as "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having (count("id") in ($1))",
+  },
+  "mysql": {
+    "parameters": [
+      10,
+    ],
+    "sql": "select count(\`id\`) as \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having (count(\`id\`) in (?))",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > single HAVING not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      10,
+    ],
+    "sql": "select count("id") as "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" having (not count("id") in ($1))",
+  },
+  "mysql": {
+    "parameters": [
+      10,
+    ],
+    "sql": "select count(\`id\`) as \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` having (not count(\`id\`) in (?))",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > single WHERE in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+    ],
+    "sql": "select "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("id" in ($1))",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+    ],
+    "sql": "select \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`id\` in (?))",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > single WHERE not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      1,
+    ],
+    "sql": "select "id" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (not "id" in ($1))",
+  },
+  "mysql": {
+    "parameters": [
+      1,
+    ],
+    "sql": "select \`id\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (not \`id\` in (?))",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > statistical aliases collide with internal rank names and source names 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.9,
+      0,
+      1,
+      "技术",
+      "家具",
+      1,
+      0.5,
+      1,
+      5,
+    ],
+    "sql": "select "product_type", sum("profit") as "sales", median("sales") as "__vquery_stat_0", quantile("profit", CAST($1 AS DOUBLE)) as "__vquery_stat_1" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("sales" >= $2 or "profit" is null) and ("id" >= $3 and not "product_type" in ($4, $5))) group by "product_type", "sales" having (median("sales") >= $6 and quantile("profit", CAST($7 AS DOUBLE)) <= $8) order by "__vquery_stat_0" desc nulls last, "product_type" asc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      0.9,
+      0,
+      1,
+      "技术",
+      "家具",
+      1,
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, sum(\`profit\`) as \`sales\`, avg(case when \`__vquery_stat_2\` in (floor((\`__vquery_stat_3\` + 1) / 2), floor((\`__vquery_stat_3\` + 2) / 2)) then \`sales\` end) as \`__vquery_stat_0\`, max(case when \`__vquery_stat_4\` = greatest(\`__vquery_stat_5\` - floor(\`__vquery_stat_5\` - \`__vquery_stat_5\` * CAST(? AS DOUBLE)), 1) then \`profit\` end) as \`__vquery_stat_1\` from (select \`product_type\`, \`profit\`, \`sales\`, row_number() over (partition by \`product_type\`, \`sales\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_2\`, count(\`sales\`) over (partition by \`product_type\`, \`sales\`) as \`__vquery_stat_3\`, row_number() over (partition by \`product_type\`, \`sales\` order by \`profit\` is null, \`profit\`) as \`__vquery_stat_4\`, count(\`profit\`) over (partition by \`product_type\`, \`sales\`) as \`__vquery_stat_5\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((\`sales\` >= ? or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\`, \`sales\` having (avg(case when \`__vquery_stat_2\` in (floor((\`__vquery_stat_3\` + 1) / 2), floor((\`__vquery_stat_3\` + 2) / 2)) then \`sales\` end) >= ? and max(case when \`__vquery_stat_4\` = greatest(\`__vquery_stat_5\` - floor(\`__vquery_stat_5\` - \`__vquery_stat_5\` * CAST(? AS DOUBLE)), 1) then \`profit\` end) <= ?) order by \`__vquery_stat_0\` is null asc, \`__vquery_stat_0\` desc, \`product_type\` is null asc, \`product_type\` asc limit 5",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") != $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) != convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") < $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) < convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") <= $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) <= convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") = $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) = convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") > $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) > convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") >= $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) >= convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") between $5 and $6 or quantile("sales", CAST($7 AS DOUBLE)) > $8) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      "GlobeWeis 搭扣信封, 红色",
+      null,
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") in ($5, $6, $7) or quantile("sales", CAST($8 AS DOUBLE)) > $9) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      "GlobeWeis 搭扣信封, 红色",
+      null,
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin, ?) or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") is not null or quantile("sales", CAST($5 AS DOUBLE)) > $6) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) is not null or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") is null or quantile("sales", CAST($5 AS DOUBLE)) > $6) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) is null or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (max("product_name") not between $5 and $6 or quantile("sales", CAST($7 AS DOUBLE)) > $8) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (max(\`product_name\`) not between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix max not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      "GlobeWeis 搭扣信封, 红色",
+      null,
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", max("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (not max("product_name") in ($5, $6, $7) or quantile("sales", CAST($8 AS DOUBLE)) > $9) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      "GlobeWeis 搭扣信封, 红色",
+      null,
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, max(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (not max(\`product_name\`) in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin, ?) or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min != 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") != $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) != convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min < 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") < $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) < convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min <= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") <= $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) <= convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min = 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") = $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) = convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min > 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") > $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) > convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min >= 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") >= $5 or quantile("sales", CAST($6 AS DOUBLE)) > $7) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) >= convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") between $5 and $6 or quantile("sales", CAST($7 AS DOUBLE)) > $8) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      "GlobeWeis 搭扣信封, 红色",
+      null,
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") in ($5, $6, $7) or quantile("sales", CAST($8 AS DOUBLE)) > $9) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      "GlobeWeis 搭扣信封, 红色",
+      null,
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin, ?) or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min is not null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") is not null or quantile("sales", CAST($5 AS DOUBLE)) > $6) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) is not null or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min is null 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") is null or quantile("sales", CAST($5 AS DOUBLE)) > $6) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) is null or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min not between 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (min("product_name") not between $5 and $6 or quantile("sales", CAST($7 AS DOUBLE)) > $8) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $9",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Acco 孔加固材料, 回收",
+      "贝尔金 鼠标, 耐用",
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (min(\`product_name\`) not between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > string HAVING matrix min not in 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      "GlobeWeis 搭扣信封, 红色",
+      null,
+      0.5,
+      1,
+      10,
+    ],
+    "sql": "select "product_type", min("product_name") as "product_name_boundary", median("sales") as "sales_metric" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where (("product_name" not ilike $1 escape '' or "profit" is null) and ("id" >= $2 and not "product_type" in ($3, $4))) group by "product_type" having (not min("product_name") in ($5, $6, $7) or quantile("sales", CAST($8 AS DOUBLE)) > $9) order by "product_name_boundary" asc nulls last, "sales_metric" desc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "%开信刀%",
+      1,
+      "技术",
+      "家具",
+      "Fiskars 剪刀, 蓝色",
+      "GlobeWeis 搭扣信封, 红色",
+      null,
+      0.5,
+      1,
+    ],
+    "sql": "select \`product_type\`, min(\`product_name\`) as \`product_name_boundary\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales_metric\` from (select \`product_type\`, \`product_name\`, \`sales\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where ((lower(convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin) not like lower(convert(? using utf8mb4) collate utf8mb4_0900_bin) escape '' or \`profit\` is null) and (\`id\` >= ? and not \`product_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)))) as \`__vquery_ranked\` group by \`product_type\` having (not min(\`product_name\`) in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin, ?) or max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) > ?) order by \`product_name_boundary\` is null asc, \`product_name_boundary\` asc, \`sales_metric\` is null asc, \`sales_metric\` desc limit 10",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased avg 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select avg("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select avg(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased count 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select count("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select count(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased count_distinct 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select count(distinct "sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select count(distinct \`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased max 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select max("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select max(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased median 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select median("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased min 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select min("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select min(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased quantile 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.5,
+    ],
+    "sql": "select quantile("sales", CAST($1 AS DOUBLE)) as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [
+      0.5,
+    ],
+    "sql": "select max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales\` from (select \`sales\`, row_number() over ( order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over () as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased stddev 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select stddev("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select stddev_samp(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased sum 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select sum("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select sum(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased variance 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select var_samp("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select var_samp(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unaliased variance_pop 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select var_pop("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source"",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select var_pop(\`sales\`) as \`sales\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > unprojected grouping and sorting 1`] = `
+{
+  "duckdb": {
+    "parameters": [],
+    "sql": "select median("sales") as "sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" group by "product_type" order by "product_type" asc nulls last",
+  },
+  "mysql": {
+    "parameters": [],
+    "sql": "select avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`sales\` from (select \`sales\`, \`product_type\`, row_number() over (partition by \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\`) as \`__vquery_ranked\` group by \`product_type\` order by \`product_type\` is null asc, \`product_type\` asc",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 下单日和配送日双日期分组 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m-%d",
+      "%Y-%m-%d",
+      "2019-01-01",
+      "2019-12-30",
+      "当日",
+      500,
+      0,
+      100,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_day", strftime(CAST("delivery_date" AS TIMESTAMP), $2) as "delivery_day", "delivery_method", sum("sales") as "total_sales", median("profit") as "median_profit" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $3 and $4 and "delivery_method" = $5) group by "order_day", "delivery_day", "delivery_method" having (sum("sales") > $6 or median("profit") < $7) order by "order_day" asc nulls last, "delivery_day" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m-%d",
+      "%Y-%m-%d",
+      "%Y-%m-%d",
+      "%Y-%m-%d",
+      "%Y-%m-%d",
+      "%Y-%m-%d",
+      "2019-01-01",
+      "2019-12-30",
+      "当日",
+      500,
+      0,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_day\`, date_format(\`delivery_date\`, ?) as \`delivery_day\`, \`delivery_method\`, sum(\`sales\`) as \`total_sales\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`profit\` end) as \`median_profit\` from (select \`order_date\`, \`delivery_date\`, \`delivery_method\`, \`sales\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), date_format(\`delivery_date\`, ?), \`delivery_method\` order by \`profit\` is null, \`profit\`) as \`__vquery_stat_0\`, count(\`profit\`) over (partition by date_format(\`order_date\`, ?), date_format(\`delivery_date\`, ?), \`delivery_method\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and \`delivery_method\` = convert(? using utf8mb4) collate utf8mb4_0900_bin)) as \`__vquery_ranked\` group by \`order_day\`, \`delivery_day\`, \`delivery_method\` having (sum(\`sales\`) > ? or avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`profit\` end) < ?) order by \`order_day\` is null asc, \`order_day\` asc, \`delivery_day\` is null asc, \`delivery_day\` asc limit 100",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 全部真实字段的订单明细 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      "浙江",
+      "四川",
+      100,
+    ],
+    "sql": "select "id", "order_id", "order_date", "delivery_date", "delivery_method", "customer_id", "customer_name", "customer_type", "city", "province", "country_or_region", "area", "product_id", "product_type", "product_sub_type", "product_name", "sales", "amount", "discount", "profit" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and ("province" = $3 or "province" = $4)) order by "order_date" desc nulls last, "id" desc nulls last limit $5",
+  },
+  "mysql": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      "浙江",
+      "四川",
+    ],
+    "sql": "select \`id\`, \`order_id\`, \`order_date\`, \`delivery_date\`, \`delivery_method\`, \`customer_id\`, \`customer_name\`, \`customer_type\`, \`city\`, \`province\`, \`country_or_region\`, \`area\`, \`product_id\`, \`product_type\`, \`product_sub_type\`, \`product_name\`, \`sales\`, \`amount\`, \`discount\`, \`profit\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and (\`province\` = convert(? using utf8mb4) collate utf8mb4_0900_bin or \`province\` = convert(? using utf8mb4) collate utf8mb4_0900_bin)) order by \`order_date\` is null asc, \`order_date\` desc, \`id\` is null asc, \`id\` desc limit 100",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 商品层级利润与销售分位数 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      0.9,
+      "2019-01-01",
+      "2019-12-30",
+      "办公用品",
+      "%打印机%",
+      10,
+      50,
+    ],
+    "sql": "select "product_type", "product_sub_type", "product_id", "product_name", sum("profit") as "total_profit", quantile("sales", CAST($1 AS DOUBLE)) as "sales_q90", sum("amount") as "total_amount", avg("discount") as "average_discount" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and ("product_type" = $4 or "product_name" like $5 escape '')) group by "product_type", "product_sub_type", "product_id", "product_name" having (sum("amount") >= $6) order by "total_profit" desc nulls last, "product_id" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      0.9,
+      "2019-01-01",
+      "2019-12-30",
+      "办公用品",
+      "%打印机%",
+      10,
+    ],
+    "sql": "select \`product_type\`, \`product_sub_type\`, \`product_id\`, \`product_name\`, sum(\`profit\`) as \`total_profit\`, max(case when \`__vquery_stat_0\` = greatest(\`__vquery_stat_1\` - floor(\`__vquery_stat_1\` - \`__vquery_stat_1\` * CAST(? AS DOUBLE)), 1) then \`sales\` end) as \`sales_q90\`, sum(\`amount\`) as \`total_amount\`, avg(\`discount\`) as \`average_discount\` from (select \`product_type\`, \`product_sub_type\`, \`product_id\`, \`product_name\`, \`profit\`, \`sales\`, \`amount\`, \`discount\`, row_number() over (partition by \`product_type\`, \`product_sub_type\`, \`product_id\`, \`product_name\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by \`product_type\`, \`product_sub_type\`, \`product_id\`, \`product_name\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and (\`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin or convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin like convert(? using utf8mb4) collate utf8mb4_0900_bin escape ''))) as \`__vquery_ranked\` group by \`product_type\`, \`product_sub_type\`, \`product_id\`, \`product_name\` having (sum(\`amount\`) >= ?) order by \`total_profit\` is null asc, \`total_profit\` desc, \`product_id\` is null asc, \`product_id\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 客户商品地域交叉销售分析 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      "公司",
+      "技术",
+      "消费者",
+      "家具",
+      1000,
+      0,
+      5,
+      50,
+    ],
+    "sql": "select "customer_type", "product_type", "area", sum("sales") as "total_sales", sum("profit") as "total_profit", count(distinct "order_id") as "order_count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and (("customer_type" = $3 and "product_type" = $4) or ("customer_type" = $5 and "product_type" = $6))) group by "customer_type", "product_type", "area" having (sum("sales") >= $7 and (sum("profit") > $8 or count(distinct "order_id") > $9)) order by "total_sales" desc nulls last, "area" asc nulls last limit $10",
+  },
+  "mysql": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      "公司",
+      "技术",
+      "消费者",
+      "家具",
+      1000,
+      0,
+      5,
+    ],
+    "sql": "select \`customer_type\`, \`product_type\`, \`area\`, sum(\`sales\`) as \`total_sales\`, sum(\`profit\`) as \`total_profit\`, count(distinct \`order_id\`) as \`order_count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and ((\`customer_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin and \`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin) or (\`customer_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin and \`product_type\` = convert(? using utf8mb4) collate utf8mb4_0900_bin))) group by \`customer_type\`, \`product_type\`, \`area\` having (sum(\`sales\`) >= ? and (sum(\`profit\`) > ? or count(distinct \`order_id\`) > ?)) order by \`total_sales\` is null asc, \`total_sales\` desc, \`area\` is null asc, \`area\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 客户类型与复购客户贡献 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      "公司",
+      "消费者",
+      "小型企业",
+      1,
+      50,
+    ],
+    "sql": "select "customer_type", "customer_id", "customer_name", count(distinct "order_id") as "order_count", sum("sales") as "total_sales", sum("profit") as "total_profit" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and "customer_type" in ($3, $4, $5)) group by "customer_type", "customer_id", "customer_name" having (count(distinct "order_id") > $6) order by "total_sales" desc nulls last, "customer_id" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      "公司",
+      "消费者",
+      "小型企业",
+      1,
+    ],
+    "sql": "select \`customer_type\`, \`customer_id\`, \`customer_name\`, count(distinct \`order_id\`) as \`order_count\`, sum(\`sales\`) as \`total_sales\`, sum(\`profit\`) as \`total_profit\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and \`customer_type\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)) group by \`customer_type\`, \`customer_id\`, \`customer_name\` having (count(distinct \`order_id\`) > ?) order by \`total_sales\` is null asc, \`total_sales\` desc, \`customer_id\` is null asc, \`customer_id\` asc limit 50",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 折扣亏损商品在地域中的分布 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      0,
+      0,
+      0,
+      100,
+    ],
+    "sql": "select "country_or_region", "area", "province", "product_type", sum("profit") as "total_loss", sum("sales") as "total_sales", avg("discount") as "average_discount" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and "profit" < $3 and "discount" > $4) group by "country_or_region", "area", "province", "product_type" having (sum("profit") < $5) order by "total_loss" asc nulls last, "province" asc nulls last limit $6",
+  },
+  "mysql": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      0,
+      0,
+      0,
+    ],
+    "sql": "select \`country_or_region\`, \`area\`, \`province\`, \`product_type\`, sum(\`profit\`) as \`total_loss\`, sum(\`sales\`) as \`total_sales\`, avg(\`discount\`) as \`average_discount\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and \`profit\` < ? and \`discount\` > ?) group by \`country_or_region\`, \`area\`, \`province\`, \`product_type\` having (sum(\`profit\`) < ?) order by \`total_loss\` is null asc, \`total_loss\` asc, \`province\` is null asc, \`province\` asc limit 100",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 按月份省份和商品类别比较销售利润 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2019-01-01",
+      "2019-12-30",
+      "华东",
+      "中南",
+      1000,
+      0,
+      100,
+    ],
+    "sql": "select strftime(CAST("order_date" AS TIMESTAMP), $1) as "order_month", "province", "product_type", sum("sales") as "total_sales", sum("profit") as "total_profit", median("sales") as "median_sales" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and "area" in ($4, $5)) group by "order_month", "province", "product_type" having (sum("sales") >= $6 and sum("profit") > $7) order by "order_month" asc nulls last, "total_sales" desc nulls last, "province" asc nulls last limit $8",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "%Y-%m",
+      "%Y-%m",
+      "2019-01-01",
+      "2019-12-30",
+      "华东",
+      "中南",
+      1000,
+      0,
+    ],
+    "sql": "select date_format(\`order_date\`, ?) as \`order_month\`, \`province\`, \`product_type\`, sum(\`sales\`) as \`total_sales\`, sum(\`profit\`) as \`total_profit\`, avg(case when \`__vquery_stat_0\` in (floor((\`__vquery_stat_1\` + 1) / 2), floor((\`__vquery_stat_1\` + 2) / 2)) then \`sales\` end) as \`median_sales\` from (select \`order_date\`, \`province\`, \`product_type\`, \`sales\`, \`profit\`, row_number() over (partition by date_format(\`order_date\`, ?), \`province\`, \`product_type\` order by \`sales\` is null, \`sales\`) as \`__vquery_stat_0\`, count(\`sales\`) over (partition by date_format(\`order_date\`, ?), \`province\`, \`product_type\`) as \`__vquery_stat_1\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and \`area\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin))) as \`__vquery_ranked\` group by \`order_month\`, \`province\`, \`product_type\` having (sum(\`sales\`) >= ? and sum(\`profit\`) > ?) order by \`order_month\` is null asc, \`order_month\` asc, \`total_sales\` is null asc, \`total_sales\` desc, \`province\` is null asc, \`province\` asc limit 100",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 浙江城市销售排行榜 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      "浙江",
+      "标准级",
+      "二级",
+      5,
+      20,
+    ],
+    "sql": "select "province", "city", sum("sales") as "total_sales", count(distinct "order_id") as "order_count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2 and "province" = $3 and "delivery_method" in ($4, $5)) group by "province", "city" having (count(distinct "order_id") >= $6) order by "total_sales" desc nulls last, "city" asc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+      "浙江",
+      "标准级",
+      "二级",
+      5,
+    ],
+    "sql": "select \`province\`, \`city\`, sum(\`sales\`) as \`total_sales\`, count(distinct \`order_id\`) as \`order_count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and \`province\` = convert(? using utf8mb4) collate utf8mb4_0900_bin and \`delivery_method\` in (convert(? using utf8mb4) collate utf8mb4_0900_bin, convert(? using utf8mb4) collate utf8mb4_0900_bin)) group by \`province\`, \`city\` having (count(distinct \`order_id\`) >= ?) order by \`total_sales\` is null asc, \`total_sales\` desc, \`city\` is null asc, \`city\` asc limit 20",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 超市全年经营汇总 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+    ],
+    "sql": "select count(distinct "order_id") as "order_count", count(distinct "customer_id") as "customer_count", count(distinct "product_id") as "product_count", sum("sales") as "total_sales", sum("profit") as "total_profit", sum("amount") as "total_amount", avg("discount") as "average_discount" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $1 and $2)",
+  },
+  "mysql": {
+    "parameters": [
+      "2019-01-01",
+      "2019-12-30",
+    ],
+    "sql": "select count(distinct \`order_id\`) as \`order_count\`, count(distinct \`customer_id\`) as \`customer_count\`, count(distinct \`product_id\`) as \`product_count\`, sum(\`sales\`) as \`total_sales\`, sum(\`profit\`) as \`total_profit\`, sum(\`amount\`) as \`total_amount\`, avg(\`discount\`) as \`average_discount\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin)",
+  },
+}
+`;
+
+exports[`DSL to SQL snapshots > 配送月份与配送方式销售构成 1`] = `
+{
+  "duckdb": {
+    "parameters": [
+      "%Y-%m",
+      "2019-01-01",
+      "2019-12-30",
+      "2019-01-01",
+      "2020-01-10",
+      0,
+      100,
+    ],
+    "sql": "select strftime(CAST("delivery_date" AS TIMESTAMP), $1) as "delivery_month", "delivery_method", sum("sales") as "total_sales", count(distinct "order_id") as "order_count" from (select "id" as "id", "order_id" collate "binary" as "order_id", "order_date" as "order_date", "delivery_date" as "delivery_date", "delivery_method" collate "binary" as "delivery_method", "customer_id" collate "binary" as "customer_id", "customer_name" collate "binary" as "customer_name", "customer_type" collate "binary" as "customer_type", "city" collate "binary" as "city", "province" collate "binary" as "province", "country_or_region" collate "binary" as "country_or_region", "area" collate "binary" as "area", "product_id" collate "binary" as "product_id", "product_type" collate "binary" as "product_type", "product_sub_type" collate "binary" as "product_sub_type", "product_name" collate "binary" as "product_name", "sales" as "sales", "amount" as "amount", "discount" as "discount", "profit" as "profit" from "supermarket") as "__vquery_source" where ("order_date" between $2 and $3 and "delivery_date" between $4 and $5) group by "delivery_month", "delivery_method" having (sum("sales") > $6) order by "delivery_month" asc nulls last, "total_sales" desc nulls last limit $7",
+  },
+  "mysql": {
+    "parameters": [
+      "%Y-%m",
+      "2019-01-01",
+      "2019-12-30",
+      "2019-01-01",
+      "2020-01-10",
+      0,
+    ],
+    "sql": "select date_format(\`delivery_date\`, ?) as \`delivery_month\`, \`delivery_method\`, sum(\`sales\`) as \`total_sales\`, count(distinct \`order_id\`) as \`order_count\` from (select \`id\` as \`id\`, convert(\`order_id\` using utf8mb4) collate utf8mb4_0900_bin as \`order_id\`, \`order_date\` as \`order_date\`, \`delivery_date\` as \`delivery_date\`, convert(\`delivery_method\` using utf8mb4) collate utf8mb4_0900_bin as \`delivery_method\`, convert(\`customer_id\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_id\`, convert(\`customer_name\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_name\`, convert(\`customer_type\` using utf8mb4) collate utf8mb4_0900_bin as \`customer_type\`, convert(\`city\` using utf8mb4) collate utf8mb4_0900_bin as \`city\`, convert(\`province\` using utf8mb4) collate utf8mb4_0900_bin as \`province\`, convert(\`country_or_region\` using utf8mb4) collate utf8mb4_0900_bin as \`country_or_region\`, convert(\`area\` using utf8mb4) collate utf8mb4_0900_bin as \`area\`, convert(\`product_id\` using utf8mb4) collate utf8mb4_0900_bin as \`product_id\`, convert(\`product_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_type\`, convert(\`product_sub_type\` using utf8mb4) collate utf8mb4_0900_bin as \`product_sub_type\`, convert(\`product_name\` using utf8mb4) collate utf8mb4_0900_bin as \`product_name\`, \`sales\` as \`sales\`, \`amount\` as \`amount\`, \`discount\` as \`discount\`, \`profit\` as \`profit\` from \`supermarket\`) as \`__vquery_source\` where (\`order_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin and \`delivery_date\` between convert(? using utf8mb4) collate utf8mb4_0900_bin and convert(? using utf8mb4) collate utf8mb4_0900_bin) group by \`delivery_month\`, \`delivery_method\` having (sum(\`sales\`) > ?) order by \`delivery_month\` is null asc, \`delivery_month\` asc, \`total_sales\` is null asc, \`total_sales\` desc limit 100",
+  },
+}
+`;
diff --git a/packages/vquery/tests/dialects/compile.test.ts b/packages/vquery/tests/dialects/compile.test.ts
new file mode 100644
index 0000000000..91f5a97bb4
--- /dev/null
+++ b/packages/vquery/tests/dialects/compile.test.ts
@@ -0,0 +1,167 @@
+import { compileQuery } from '@visactor/vquery'
+import { readFileSync } from 'node:fs'
+import { resolve } from 'node:path'
+import type { DatasetColumn, HavingOperator, VQueryDSL, WhereLeaf } from '@visactor/vquery'
+import type { BaseAggregateFunction, DateAggregateFunction } from '../../src/types/dsl/Select'
+import catalog from './queries.json'
+
+type QueryCase = {
+  name: string
+  description?: string
+  dsl: VQueryDSL
+}
+const { source, table, columns, queries } = catalog as {
+  source: string
+  table: string
+  columns: DatasetColumn[]
+  queries: QueryCase[]
+}
+
+// Exhaustive records make new DSL functions/operators require fixture updates.
+const aggregateFunctions: Record<BaseAggregateFunction, true> = {
+  count: true,
+  count_distinct: true,
+  sum: true,
+  avg: true,
+  min: true,
+  max: true,
+  variance: true,
+  variance_pop: true,
+  stddev: true,
+  median: true,
+  quantile: true,
+}
+const dateFunctions: Record<DateAggregateFunction, true> = {
+  to_year: true,
+  to_quarter: true,
+  to_month: true,
+  to_week: true,
+  to_day: true,
+  to_hour: true,
+  to_minute: true,
+  to_second: true,
+}
+const whereOperators: Record<WhereLeaf<Record<string, unknown>>['op'], true> = {
+  '=': true,
+  '!=': true,
+  '>': true,
+  '>=': true,
+  '<': true,
+  '<=': true,
+  in: true,
+  'not in': true,
+  between: true,
+  'not between': true,
+  'is null': true,
+  'is not null': true,
+  like: true,
+  'not like': true,
+  ilike: true,
+  'not ilike': true,
+}
+const havingOperators: Record<HavingOperator, true> = {
+  '=': true,
+  '!=': true,
+  '>': true,
+  '>=': true,
+  '<': true,
+  '<=': true,
+  in: true,
+  'not in': true,
+  between: true,
+  'not between': true,
+  'is null': true,
+  'is not null': true,
+}
+type FilterNode = { field?: string; op: string; aggr?: { func: string }; conditions?: FilterNode[] }
+function leaves(node?: FilterNode): FilterNode[] {
+  if (!node) return []
+  return node.conditions ? node.conditions.flatMap((child) => leaves(child)) : [node]
+}
+
+it('uses the real supermarket CSV schema', () => {
+  const header = readFileSync(resolve(__dirname, '../..', source), 'utf8')
+    .split(/\r?\n/, 1)[0]
+    .replace(/^\uFEFF/, '')
+    .split(',')
+  expect(table).toBe('supermarket')
+  expect(columns.map((column) => column.name)).toEqual(header)
+})
+
+it('keeps fixture names unique and covers the declared DSL feature combinations', () => {
+  expect(new Set(queries.map((entry) => entry.name)).size).toBe(queries.length)
+  const selected = new Set<string>()
+  const where = new Set<string>()
+  const having = new Set<string>()
+  const dates = new Set<string>()
+  const ordering = new Set<string>()
+  for (const { dsl } of queries) {
+    const aggregates = dsl.select.flatMap((item) =>
+      typeof item === 'string' || !item.aggr ? [] : [{ ...item, aggr: item.aggr }],
+    )
+    for (const item of aggregates) {
+      const func = item.aggr.func
+      selected.add(func)
+      const sort = dsl.orderBy?.find((sort) => sort.field === (item.alias ?? item.field))
+      if (sort) ordering.add(`${func}:${sort.order ?? 'asc'}:${sort.nulls ?? 'default'}`)
+    }
+    if (dsl.where?.conditions?.some((child) => 'conditions' in child))
+      for (const leaf of leaves(dsl.where)) where.add(`${dsl.where.op}:${leaf.op}`)
+    for (const leaf of leaves(dsl.having)) having.add(`${leaf.aggr!.func}:${leaf.op}`)
+    for (const date of aggregates.filter(
+      (item) => item.aggr.func.startsWith('to_') && dsl.groupBy?.includes(item.alias ?? item.field),
+    ))
+      for (const numeric of aggregates.filter((item) => !item.aggr.func.startsWith('to_')))
+        dates.add(`${date.aggr.func}:${numeric.aggr.func}`)
+  }
+  expect(selected).toEqual(new Set([...Object.keys(aggregateFunctions), ...Object.keys(dateFunctions)]))
+  expect(queries.some(({ dsl }) => dsl.select.length === 0)).toBe(true)
+  expect(queries.some(({ dsl }) => dsl.select.some((item) => typeof item === 'string'))).toBe(true)
+  expect(
+    queries.some(({ dsl }) => dsl.select.some((item) => typeof item !== 'string' && item.alias && !item.aggr)),
+  ).toBe(true)
+  for (const func of Object.keys(aggregateFunctions)) {
+    for (const op of Object.keys(havingOperators)) expect([...having]).toContain(`${func}:${op}`)
+    for (const date of Object.keys(dateFunctions)) expect([...dates]).toContain(`${date}:${func}`)
+    for (const order of ['asc', 'desc'])
+      for (const nulls of ['default', 'first', 'last']) expect([...ordering]).toContain(`${func}:${order}:${nulls}`)
+  }
+  for (const op of Object.keys(whereOperators))
+    for (const root of ['and', 'or']) expect([...where]).toContain(`${root}:${op}`)
+  expect(
+    queries.some(({ dsl }) =>
+      ['select', 'where', 'groupBy', 'having', 'orderBy', 'limit'].every((clause) => clause in dsl),
+    ),
+  ).toBe(true)
+})
+
+describe('DSL to SQL snapshots', () => {
+  for (const entry of queries)
+    it(entry.name, () => {
+      const { dsl } = entry
+      const fields = columns.map((column) => column.name)
+      const selected = dsl.select.map((item) => (typeof item === 'string' ? { field: item } : item))
+      const aliases = new Map(selected.map((item) => [item.alias ?? item.field, item]))
+      for (const item of selected) expect(fields).toContain(item.field)
+      for (const item of [...leaves(dsl.where), ...leaves(dsl.having)]) expect(fields).toContain(item.field)
+      for (const field of [...(dsl.groupBy ?? []), ...(dsl.orderBy ?? []).map((item) => item.field)])
+        expect([...fields, ...aliases.keys()]).toContain(field)
+      for (const field of dsl.groupBy ?? []) {
+        const item = aliases.get(field)
+        if (!fields.includes(field) && item?.aggr) expect(item.aggr.func.startsWith('to_')).toBe(true)
+      }
+      const grouped =
+        selected.some((item) => item.aggr && !item.aggr.func.startsWith('to_')) || leaves(dsl.having).length
+      if (grouped) {
+        for (const item of selected.filter((item) => !item.aggr || item.aggr.func.startsWith('to_')))
+          expect(
+            (dsl.groupBy ?? []).includes(item.field) || (dsl.groupBy ?? []).includes(item.alias ?? item.field),
+          ).toBe(true)
+      }
+      expect(
+        Object.fromEntries(
+          (['mysql', 'duckdb'] as const).map((dialect) => [dialect, compileQuery(dsl, { table, columns, dialect })]),
+        ),
+      ).toMatchSnapshot()
+    })
+})
diff --git a/packages/vquery/tests/dialects/queries.json b/packages/vquery/tests/dialects/queries.json
new file mode 100644
index 0000000000..40d6133f06
--- /dev/null
+++ b/packages/vquery/tests/dialects/queries.json
@@ -0,0 +1,31566 @@
+{
+  "source": "tests/unit/assets/supermarket.csv",
+  "table": "supermarket",
+  "columns": [
+    {
+      "name": "id",
+      "type": "number"
+    },
+    {
+      "name": "order_id",
+      "type": "string"
+    },
+    {
+      "name": "order_date",
+      "type": "date"
+    },
+    {
+      "name": "delivery_date",
+      "type": "date"
+    },
+    {
+      "name": "delivery_method",
+      "type": "string"
+    },
+    {
+      "name": "customer_id",
+      "type": "string"
+    },
+    {
+      "name": "customer_name",
+      "type": "string"
+    },
+    {
+      "name": "customer_type",
+      "type": "string"
+    },
+    {
+      "name": "city",
+      "type": "string"
+    },
+    {
+      "name": "province",
+      "type": "string"
+    },
+    {
+      "name": "country_or_region",
+      "type": "string"
+    },
+    {
+      "name": "area",
+      "type": "string"
+    },
+    {
+      "name": "product_id",
+      "type": "string"
+    },
+    {
+      "name": "product_type",
+      "type": "string"
+    },
+    {
+      "name": "product_sub_type",
+      "type": "string"
+    },
+    {
+      "name": "product_name",
+      "type": "string"
+    },
+    {
+      "name": "sales",
+      "type": "number"
+    },
+    {
+      "name": "amount",
+      "type": "number"
+    },
+    {
+      "name": "discount",
+      "type": "number"
+    },
+    {
+      "name": "profit",
+      "type": "number"
+    }
+  ],
+  "queries": [
+    {
+      "name": "numeric aggregates",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "sum",
+            "aggr": {
+              "func": "sum"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "avg",
+            "aggr": {
+              "func": "avg"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "min",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "max",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count_distinct",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "variance",
+            "aggr": {
+              "func": "variance"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "variance_pop",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "stddev",
+            "aggr": {
+              "func": "stddev"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "median",
+            "aggr": {
+              "func": "median"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "quantile",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "grouped statistics over multiple fields",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "median",
+            "aggr": {
+              "func": "median"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "quantile",
+            "aggr": {
+              "func": "quantile"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "negative_quantile",
+            "aggr": {
+              "func": "quantile",
+              "quantile": -0.5
+            }
+          },
+          {
+            "field": "profit",
+            "alias": "median_profit",
+            "aggr": {
+              "func": "median"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["product_type"],
+        "orderBy": [
+          {
+            "field": "product_type"
+          }
+        ]
+      }
+    },
+    {
+      "name": "filter before ranking and limit after aggregation",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "median",
+            "aggr": {
+              "func": "median"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "q90",
+            "aggr": {
+              "func": "quantile",
+              "quantile": 0.9
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "id",
+              "op": ">=",
+              "value": 2
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "orderBy": [
+          {
+            "field": "median",
+            "order": "desc"
+          },
+          {
+            "field": "product_type"
+          }
+        ],
+        "limit": 2
+      }
+    },
+    {
+      "name": "HAVING statistics absent from projection",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sum",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 2.5
+            },
+            {
+              "field": "profit",
+              "aggr": {
+                "func": "quantile",
+                "quantile": -0.5
+              },
+              "op": ">",
+              "value": 5
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_type"
+          }
+        ]
+      }
+    },
+    {
+      "name": "filtered statistical aggregates",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "median",
+            "aggr": {
+              "func": "median"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "quantile",
+            "aggr": {
+              "func": "quantile"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "id",
+              "op": "<",
+              "value": 0
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "product name distinct and extrema",
+      "dsl": {
+        "select": [
+          {
+            "field": "product_name",
+            "alias": "distinct_product_names",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          },
+          {
+            "field": "product_name",
+            "alias": "first_product_name",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "product_name",
+            "alias": "last_product_name",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name grouping",
+      "dsl": {
+        "select": [
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["product_name"],
+        "orderBy": [
+          {
+            "field": "product_name"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name comparison is exact",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "=",
+              "value": "Fiskars 剪刀, 蓝色"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name IN is exact",
+      "dsl": {
+        "select": ["id"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "in",
+              "value": ["Fiskars 剪刀, 蓝色", "柯尼卡 打印机, 红色"]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name BETWEEN uses codepoint order",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "between",
+              "value": ["Acco 孔加固材料, 回收", "贝尔金 鼠标, 耐用"]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name"
+          }
+        ]
+      }
+    },
+    {
+      "name": "order date ISO parameter",
+      "dsl": {
+        "select": ["id"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": ">=",
+              "value": "2019-04-27"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "date grouping with statistical HAVING",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_year",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "median",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "groupBy": ["order_year"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": "is not null"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "order_year"
+          }
+        ]
+      }
+    },
+    {
+      "name": "discrete quantile -1",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "result",
+            "aggr": {
+              "func": "quantile",
+              "quantile": -1
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "discrete quantile -0.9",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "result",
+            "aggr": {
+              "func": "quantile",
+              "quantile": -0.9
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "discrete quantile -0.5",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "result",
+            "aggr": {
+              "func": "quantile",
+              "quantile": -0.5
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "discrete quantile -0.01",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "result",
+            "aggr": {
+              "func": "quantile",
+              "quantile": -0.01
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "discrete quantile 0",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "result",
+            "aggr": {
+              "func": "quantile",
+              "quantile": 0
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "discrete quantile 0.01",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "result",
+            "aggr": {
+              "func": "quantile",
+              "quantile": 0.01
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "discrete quantile 0.5",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "result",
+            "aggr": {
+              "func": "quantile",
+              "quantile": 0.5
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "discrete quantile 0.9",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "result",
+            "aggr": {
+              "func": "quantile",
+              "quantile": 0.9
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "discrete quantile 1",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "result",
+            "aggr": {
+              "func": "quantile",
+              "quantile": 1
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "date grouping to_year",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["order_period"],
+        "orderBy": [
+          {
+            "field": "order_period"
+          }
+        ]
+      }
+    },
+    {
+      "name": "date grouping to_quarter",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["order_period"],
+        "orderBy": [
+          {
+            "field": "order_period"
+          }
+        ]
+      }
+    },
+    {
+      "name": "date grouping to_month",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["order_period"],
+        "orderBy": [
+          {
+            "field": "order_period"
+          }
+        ]
+      }
+    },
+    {
+      "name": "date grouping to_week",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["order_period"],
+        "orderBy": [
+          {
+            "field": "order_period"
+          }
+        ]
+      }
+    },
+    {
+      "name": "date grouping to_day",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["order_period"],
+        "orderBy": [
+          {
+            "field": "order_period"
+          }
+        ]
+      }
+    },
+    {
+      "name": "date grouping to_hour",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["order_period"],
+        "orderBy": [
+          {
+            "field": "order_period"
+          }
+        ]
+      }
+    },
+    {
+      "name": "date grouping to_minute",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["order_period"],
+        "orderBy": [
+          {
+            "field": "order_period"
+          }
+        ]
+      }
+    },
+    {
+      "name": "date grouping to_second",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "count",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "groupBy": ["order_period"],
+        "orderBy": [
+          {
+            "field": "order_period"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name like Fiskars%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "like",
+              "value": "Fiskars%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name like %孔加固材料%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "like",
+              "value": "%孔加固材料%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name like %信封%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "like",
+              "value": "%信封%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name like 柯尼卡%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "like",
+              "value": "柯尼卡%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name not like Fiskars%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "not like",
+              "value": "Fiskars%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name not like %孔加固材料%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "not like",
+              "value": "%孔加固材料%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name not like %信封%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "not like",
+              "value": "%信封%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name not like 柯尼卡%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "not like",
+              "value": "柯尼卡%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name ilike Fiskars%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "ilike",
+              "value": "Fiskars%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name ilike %孔加固材料%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "ilike",
+              "value": "%孔加固材料%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name ilike %信封%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "ilike",
+              "value": "%信封%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name ilike 柯尼卡%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "ilike",
+              "value": "柯尼卡%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name not ilike Fiskars%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "not ilike",
+              "value": "Fiskars%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name not ilike %孔加固材料%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "not ilike",
+              "value": "%孔加固材料%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name not ilike %信封%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "not ilike",
+              "value": "%信封%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "product name not ilike 柯尼卡%",
+      "dsl": {
+        "select": ["id", "product_name"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "not ilike",
+              "value": "柯尼卡%"
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "NULL ordering asc default",
+      "dsl": {
+        "select": ["id", "sales"],
+        "orderBy": [
+          {
+            "field": "sales",
+            "order": "asc"
+          },
+          {
+            "field": "id"
+          }
+        ],
+        "limit": 8
+      }
+    },
+    {
+      "name": "NULL ordering asc first",
+      "dsl": {
+        "select": ["id", "sales"],
+        "orderBy": [
+          {
+            "field": "sales",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "id"
+          }
+        ],
+        "limit": 8
+      }
+    },
+    {
+      "name": "NULL ordering asc last",
+      "dsl": {
+        "select": ["id", "sales"],
+        "orderBy": [
+          {
+            "field": "sales",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "id"
+          }
+        ],
+        "limit": 8
+      }
+    },
+    {
+      "name": "NULL ordering desc default",
+      "dsl": {
+        "select": ["id", "sales"],
+        "orderBy": [
+          {
+            "field": "sales",
+            "order": "desc"
+          },
+          {
+            "field": "id"
+          }
+        ],
+        "limit": 8
+      }
+    },
+    {
+      "name": "NULL ordering desc first",
+      "dsl": {
+        "select": ["id", "sales"],
+        "orderBy": [
+          {
+            "field": "sales",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "id"
+          }
+        ],
+        "limit": 8
+      }
+    },
+    {
+      "name": "NULL ordering desc last",
+      "dsl": {
+        "select": ["id", "sales"],
+        "orderBy": [
+          {
+            "field": "sales",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "id"
+          }
+        ],
+        "limit": 8
+      }
+    },
+    {
+      "name": "numeric filter =",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "=",
+              "value": 2
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter !=",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "!=",
+              "value": 2
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter >",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": ">",
+              "value": 2
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter >=",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": ">=",
+              "value": 2
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter <",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "<",
+              "value": 2
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter <=",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "<=",
+              "value": 2
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter in",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "in",
+              "value": [1, null]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter not in",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "not in",
+              "value": [1, null]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter between",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "between",
+              "value": [1, 4]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter not between",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "not between",
+              "value": [1, 4]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter is null",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "is null",
+              "value": 2
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "numeric filter is not null",
+      "dsl": {
+        "select": ["id", "sales"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": "is not null",
+              "value": 2
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "empty and group",
+      "dsl": {
+        "select": ["id"],
+        "where": {
+          "op": "and",
+          "conditions": []
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "empty or group",
+      "dsl": {
+        "select": ["id"],
+        "where": {
+          "op": "or",
+          "conditions": []
+        },
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ]
+      }
+    },
+    {
+      "name": "internal statistical alias collision",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "alias": "__vquery_stat_0",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "GROUP BY source takes precedence over a selected aggregate without alias",
+      "dsl": {
+        "select": [
+          {
+            "field": "profit",
+            "alias": "profit",
+            "aggr": {
+              "func": "sum"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "median",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "groupBy": ["profit"]
+      }
+    },
+    {
+      "name": "GROUP BY source takes precedence over a colliding alias",
+      "dsl": {
+        "select": [
+          {
+            "field": "profit",
+            "alias": "product_type",
+            "aggr": {
+              "func": "sum"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "median",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "groupBy": ["product_type"]
+      }
+    },
+    {
+      "name": "projection without alias",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales"
+          }
+        ],
+        "orderBy": [
+          {
+            "field": "id"
+          }
+        ],
+        "limit": 1
+      }
+    },
+    {
+      "name": "all fields and zero limit",
+      "dsl": {
+        "select": [],
+        "limit": 0
+      }
+    },
+    {
+      "name": "empty grouping and ordering",
+      "dsl": {
+        "select": ["id"],
+        "groupBy": [],
+        "orderBy": [],
+        "limit": 0
+      }
+    },
+    {
+      "name": "raw grouping alias",
+      "dsl": {
+        "select": [
+          {
+            "field": "product_type",
+            "alias": "product_category"
+          },
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "groupBy": ["product_category"],
+        "orderBy": [
+          {
+            "field": "product_category"
+          }
+        ]
+      }
+    },
+    {
+      "name": "unprojected grouping and sorting",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "groupBy": ["product_type"],
+        "orderBy": [
+          {
+            "field": "product_type"
+          }
+        ]
+      }
+    },
+    {
+      "name": "HAVING only median and nested count",
+      "dsl": {
+        "select": [
+          {
+            "field": "id",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": ">",
+                  "value": 0
+                }
+              ]
+            },
+            {
+              "field": "id",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "LIKE NULL",
+      "dsl": {
+        "select": ["id"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "product_name",
+              "op": "like",
+              "value": null
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "unaliased count",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased count_distinct",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased sum",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased avg",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "avg"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased min",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased max",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased variance",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased variance_pop",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased stddev",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased median",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "unaliased quantile",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "empty where and",
+      "dsl": {
+        "select": [
+          {
+            "field": "id",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": []
+        }
+      }
+    },
+    {
+      "name": "empty where or",
+      "dsl": {
+        "select": [
+          {
+            "field": "id",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "or",
+          "conditions": []
+        }
+      }
+    },
+    {
+      "name": "empty having and",
+      "dsl": {
+        "select": [
+          {
+            "field": "id",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": []
+        }
+      }
+    },
+    {
+      "name": "empty having or",
+      "dsl": {
+        "select": [
+          {
+            "field": "id",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "having": {
+          "op": "or",
+          "conditions": []
+        }
+      }
+    },
+    {
+      "name": "single WHERE in",
+      "dsl": {
+        "select": ["id"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "id",
+              "op": "in",
+              "value": 1
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "array HAVING in",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": "in",
+              "value": [1, 2]
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "single HAVING in",
+      "dsl": {
+        "select": [
+          {
+            "field": "id",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "id",
+              "aggr": {
+                "func": "count"
+              },
+              "op": "in",
+              "value": 10
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "single WHERE not in",
+      "dsl": {
+        "select": ["id"],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "id",
+              "op": "not in",
+              "value": 1
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "array HAVING not in",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": "not in",
+              "value": [1, 2]
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "single HAVING not in",
+      "dsl": {
+        "select": [
+          {
+            "field": "id",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "id",
+              "aggr": {
+                "func": "count"
+              },
+              "op": "not in",
+              "value": 10
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "HAVING between",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": "between",
+              "value": [1, 4]
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "HAVING not between",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": "not between",
+              "value": [1, 4]
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "HAVING is null",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": "is null"
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "HAVING is not null",
+      "dsl": {
+        "select": [
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": "is not null"
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "HAVING matrix count =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": "=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": "!=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": "<",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": "<=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": "in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
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+                  "value": ["技术", "家具"]
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+            "order": "asc"
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+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count between",
+      "dsl": {
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "value": ["技术", "家具"]
+                }
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+            "order": "asc"
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+      }
+    },
+    {
+      "name": "HAVING matrix count not between",
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+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "value": ["技术", "家具"]
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+            "order": "asc"
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+        ],
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+      }
+    },
+    {
+      "name": "HAVING matrix count is null",
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+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "value": ["技术", "家具"]
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+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
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+            "order": "asc"
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+        ],
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+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct !=",
+      "dsl": {
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+              ]
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+            "order": "asc"
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+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct >",
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+              ]
+            }
+          ]
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+          ]
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+          {
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+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+            }
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+              ]
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+            {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
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+                {
+                  "field": "profit",
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+                  "op": "is not null"
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+              ]
+            }
+          ]
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
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+                  "value": [-1, 1]
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+                {
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+                  "op": "is null"
+                }
+              ]
+            },
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+                  "value": 1
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+                {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "op": "<",
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+            },
+            {
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+                  "op": ">",
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+                },
+                {
+                  "field": "profit",
+                  "aggr": {
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ],
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+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
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+              "conditions": [
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+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
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+              "op": "<=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "op": ">",
+                  "value": 1
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+                {
+                  "field": "profit",
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
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+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
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+                  "value": ["技术", "家具"]
+                }
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+            }
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+            "order": "asc"
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+        ],
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+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct not in",
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+            }
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+                {
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+                  "op": "is null"
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+                  "value": 1
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
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+              "value": [0, 10, null]
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+            "order": "asc"
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+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct between",
+      "dsl": {
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
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+                  "op": "is null"
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
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+            "order": "asc"
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+        ],
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+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct not between",
+      "dsl": {
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
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+            "order": "asc"
+          }
+        ],
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+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct is null",
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
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+            "order": "asc"
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+        ],
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+      }
+    },
+    {
+      "name": "HAVING matrix count_distinct is not null",
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+              "op": "is not null"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix sum =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
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+            "nulls": "last"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix sum !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
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+            {
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+                  "op": "is not null"
+                }
+              ]
+            }
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+            "order": "desc",
+            "nulls": "last"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix sum >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+              ]
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+            {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+                {
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+                  "op": "is not null"
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+              ]
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+            "order": "desc",
+            "nulls": "last"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix sum >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+            {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
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+                {
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+                  "op": "is not null"
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+              ]
+            }
+          ]
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix sum <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
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+              ]
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+                  "value": 1
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
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+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix sum <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
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+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
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+            "order": "asc"
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+        ],
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+      }
+    },
+    {
+      "name": "HAVING matrix sum in",
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+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+            "order": "asc"
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+      }
+    },
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+      "name": "HAVING matrix sum not in",
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
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+            "order": "asc"
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+        ],
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+      "name": "HAVING matrix sum between",
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+          "product_type",
+          "product_name",
+          {
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+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+            "order": "asc"
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+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix sum not between",
+      "dsl": {
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "value": ["技术", "家具"]
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+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix sum is null",
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+          {
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+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix sum is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
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+                  "field": "id",
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+                {
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix avg =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "avg"
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+          }
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+          "conditions": [
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+                {
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+                  "op": "is null"
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+              ]
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+            {
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+                  "field": "id",
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+                {
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix avg !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+          }
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+                  "op": "is null"
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+                  "value": 1
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+            },
+            {
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+                {
+                  "field": "profit",
+                  "aggr": {
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+                  "op": "is not null"
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+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix avg >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+            }
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+        ],
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+              ]
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+                {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
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+                {
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix avg >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+          }
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+                },
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+              ]
+            },
+            {
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+              "conditions": [
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+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
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+                  "op": ">=",
+                  "value": 1
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+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+        "having": {
+          "op": "or",
+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "min"
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+              "op": ">",
+              "value": 1
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+            {
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+                  "field": "id",
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+                  "op": ">",
+                  "value": 1
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+                {
+                  "field": "profit",
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+            "order": "desc",
+            "nulls": "last"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix min >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
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+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
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+                {
+                  "field": "profit",
+                  "op": "is null"
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+                  "op": ">=",
+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "value": 1
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+            "order": "desc",
+            "nulls": "last"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix min <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
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+          "conditions": [
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+              "op": "or",
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+                  "op": "not between",
+                  "value": [-1, 1]
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+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
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+                  "op": ">=",
+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "field": "sales",
+              "aggr": {
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+            {
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+                  "field": "id",
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix min <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
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+          "conditions": [
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+              "conditions": [
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+                  "op": "not between",
+                  "value": [-1, 1]
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+                {
+                  "field": "profit",
+                  "op": "is null"
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+              ]
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+            {
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "field": "sales",
+              "aggr": {
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+              "op": "<=",
+              "value": 1
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+                  "field": "id",
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+                {
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+            "order": "desc",
+            "nulls": "last"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix min in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
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+                  "value": [-1, 1]
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+                {
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+                  "value": 1
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+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
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+              "op": "in",
+              "value": [0, 10, null]
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+            {
+              "op": "and",
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+                  "field": "id",
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+                  "op": ">",
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+                {
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix min not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
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+                  "op": "not between",
+                  "value": [-1, 1]
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+                {
+                  "field": "profit",
+                  "op": "is null"
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+              ]
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+                  "field": "id",
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+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "field": "sales",
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+              "value": [0, 10, null]
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+                  "value": 1
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+                {
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+                  "op": "is not null"
+                }
+              ]
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+          ]
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix min between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
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+              "conditions": [
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+                  "value": [-1, 1]
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+                {
+                  "field": "profit",
+                  "op": "is null"
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+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "field": "sales",
+              "aggr": {
+                "func": "min"
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+              "op": "between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
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+                  "op": ">",
+                  "value": 1
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+                {
+                  "field": "profit",
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix min not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
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+          "conditions": [
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+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
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+            {
+              "op": "and",
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+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "field": "sales",
+              "aggr": {
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+              "op": "not between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
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+                  "op": ">",
+                  "value": 1
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+                  "field": "profit",
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix min is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
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+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
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+              "field": "sales",
+              "aggr": {
+                "func": "min"
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+              "op": "is null"
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix min is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
+        "where": {
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+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
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+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "is not null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
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+              "op": "or",
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+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
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+            {
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
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+              "field": "sales",
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+              "value": 1
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+            {
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+                  "op": ">",
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+                {
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+              ]
+            }
+          ]
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
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+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
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+            {
+              "op": "and",
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
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+              "field": "sales",
+              "aggr": {
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+              "value": 1
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+                  "op": ">",
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+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
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+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
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+            {
+              "op": "and",
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "field": "sales",
+              "aggr": {
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+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "and",
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+                  "field": "id",
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+                  "value": 1
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+                {
+                  "field": "profit",
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+                  "op": "is not null"
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
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+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "max"
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+              "op": "<",
+              "value": 1
+            },
+            {
+              "op": "and",
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+                  "field": "id",
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+                    "func": "count"
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+                  "op": ">",
+                  "value": 1
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+                {
+                  "field": "profit",
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+                  "op": "is not null"
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+              ]
+            }
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
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+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "max"
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+              "op": "<=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
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+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
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+          "conditions": [
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+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
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+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
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+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
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+              "field": "sales",
+              "aggr": {
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+              "op": "in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
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+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
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+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
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+          "conditions": [
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+                  "value": [-1, 1]
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+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
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+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
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+              "field": "sales",
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+              "op": "not in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
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+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
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+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
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+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
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+          "conditions": [
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+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
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+              "field": "sales",
+              "aggr": {
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+              "op": "between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
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+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
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+          "conditions": [
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+              "conditions": [
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+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
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+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "not between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
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+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "is null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix max is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "is not null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "value": [-1, 1]
+                },
+                {
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+                  "op": "is null"
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+              ]
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+                  "value": 1
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
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+          "conditions": [
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+              "aggr": {
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+              "value": 1
+            },
+            {
+              "op": "and",
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+                  "field": "id",
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+                {
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
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+                  "op": "not between",
+                  "value": [-1, 1]
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+                {
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+                  "op": "is null"
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+              ]
+            },
+            {
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+                  "op": ">=",
+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
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+          "conditions": [
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+              "field": "sales",
+              "aggr": {
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+              "op": "!=",
+              "value": 1
+            },
+            {
+              "op": "and",
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+                  "field": "id",
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+                  "op": ">",
+                  "value": 1
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+                {
+                  "field": "profit",
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+                  "op": "is not null"
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+              ]
+            }
+          ]
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
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+              "op": "and",
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+                  "op": ">=",
+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+        "having": {
+          "op": "or",
+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "variance"
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+              "value": 1
+            },
+            {
+              "op": "and",
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+                  "field": "id",
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+                    "func": "count"
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+                  "op": ">",
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+                {
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+                  "op": "is not null"
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+              ]
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
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+          "op": "and",
+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "op": "not between",
+                  "value": [-1, 1]
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+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
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+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+        "having": {
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+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "variance"
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+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "and",
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+                  "field": "id",
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+                    "func": "count"
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+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
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+                    "func": "max"
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": "<",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
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+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
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+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
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+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": "<=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": "in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": "not in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
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+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
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+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": "between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
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+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": "not between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
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+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": "is null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
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+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": "is not null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "!=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "<",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "<=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "not in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "not between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "is null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix variance_pop is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": "is not null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": "=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": "!=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": "<",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": "<=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": "in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": "not in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
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+                  "field": "sales",
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+                  "value": [-1, 1]
+                },
+                {
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+                  "op": "is null"
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+              ]
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+                  "op": ">=",
+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+          "conditions": [
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+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": "between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
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+                  "op": ">",
+                  "value": 1
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+                {
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+            "order": "desc",
+            "nulls": "last"
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
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+              "conditions": [
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+                  "value": [-1, 1]
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+                {
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+                  "op": "is null"
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+                  "op": ">=",
+                  "value": 1
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+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+            "order": "desc",
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
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+                  "op": "is null"
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+                  "op": ">=",
+                  "value": 1
+                },
+                {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
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+                "func": "stddev"
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+              ]
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix stddev is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
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+          ]
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+                {
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+              ]
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
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+                {
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+                  "op": "is null"
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+            },
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+                  "value": 1
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+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+            {
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+              ]
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+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "value": ["技术", "家具"]
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+              ]
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+                  "op": "is not null"
+                }
+              ]
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+          ]
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+            }
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+                  "value": ["技术", "家具"]
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+              ]
+            }
+          ]
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+                {
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+              ]
+            }
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+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
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+              "value": 1
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+                {
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
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+          "conditions": [
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+                },
+                {
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+                  "op": "is null"
+                }
+              ]
+            },
+            {
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+                  "value": 1
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+            {
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+                {
+                  "field": "profit",
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
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+                {
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+                  "op": "is null"
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+              ]
+            },
+            {
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+                  "value": 1
+                },
+                {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "value": 1
+            },
+            {
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+                {
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
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+            }
+          }
+        ],
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+                {
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+                  "op": "is null"
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+              ]
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+                  "value": 1
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+                {
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+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
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+              "value": [0, 10, null]
+            },
+            {
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+                {
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+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
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+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
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+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": "not in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": "between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": "not between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": "is null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix median is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": "is not null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "!=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "<",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "<=",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "not in",
+              "value": [0, 10, null]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "not between",
+              "value": [-10, 100]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "is null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "HAVING matrix quantile is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "not between",
+                  "value": [-1, 1]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "is not null"
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "max"
+                  },
+                  "op": "is not null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year count",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year count_distinct",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count_distinct"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year sum",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year avg",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "avg"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "avg"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year min",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year max",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year variance",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year variance_pop",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year stddev",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year median",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_year quantile",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter count",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter count_distinct",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count_distinct"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter sum",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter avg",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "avg"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "avg"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter min",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter max",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter variance",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter variance_pop",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter stddev",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter median",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_quarter quantile",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_quarter"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month count",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month count_distinct",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count_distinct"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month sum",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month avg",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "avg"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "avg"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month min",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month max",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month variance",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month variance_pop",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month stddev",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month median",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_month quantile",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week count",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week count_distinct",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count_distinct"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week sum",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week avg",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "avg"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "avg"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week min",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week max",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week variance",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week variance_pop",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week stddev",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week median",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_week quantile",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_week"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day count",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day count_distinct",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count_distinct"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day sum",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day avg",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "avg"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "avg"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day min",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day max",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day variance",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day variance_pop",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day stddev",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day median",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_day quantile",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_day"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour count",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour count_distinct",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count_distinct"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour sum",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour avg",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "avg"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "avg"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour min",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour max",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour variance",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour variance_pop",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour stddev",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour median",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_hour quantile",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_hour"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute count",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute count_distinct",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count_distinct"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute sum",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute avg",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "avg"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "avg"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute min",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute max",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute variance",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute variance_pop",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute stddev",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute median",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_minute quantile",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_minute"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "asc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second count",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second count_distinct",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "count_distinct"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "count_distinct"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second sum",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second avg",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "avg"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "avg"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second min",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "min"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second max",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "max"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second variance",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second variance_pop",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "variance_pop"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "variance_pop"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second stddev",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "stddev"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "stddev"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second median",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2016-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "sales",
+                      "op": ">=",
+                      "value": -10
+                    },
+                    {
+                      "field": "profit",
+                      "op": "is null"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "id",
+                      "op": ">=",
+                      "value": 1
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "not in",
+                      "value": ["技术", "家具"]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_period", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "min"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          },
+          {
+            "field": "order_period",
+            "order": "asc"
+          }
+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "date aggregate matrix to_second quantile",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_period",
+            "aggr": {
+              "func": "to_second"
+            }
+          },
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+            "field": "order_period",
+            "order": "asc"
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+        ],
+        "limit": 25
+      }
+    },
+    {
+      "name": "nested WHERE matrix and =",
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+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and !=",
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+          "product_name",
+          {
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+            "order": "asc"
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+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and >",
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+                  "value": ["技术", "家具"]
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+            "field": "product_type",
+            "order": "asc"
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+    },
+    {
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+            "field": "product_type",
+            "order": "asc"
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+        ],
+        "limit": 50
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+    },
+    {
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+    {
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+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and not in",
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+          "product_name",
+          {
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+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+              ]
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+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and between",
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+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
+                }
+              ]
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+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "is null"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "is not null"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and like",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "like",
+                  "value": "%打印机%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and not like",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not like",
+                  "value": "%打印机%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and ilike",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "ilike",
+                  "value": "%打印机%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix and not ilike",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%打印机%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix or =",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "or",
+          "conditions": [
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "=",
+                  "value": 2
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix or !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "or",
+          "conditions": [
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "!=",
+                  "value": 2
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix or >",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "or",
+          "conditions": [
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": ">",
+                  "value": 2
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix or >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "or",
+          "conditions": [
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": ">=",
+                  "value": 2
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix or <",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "or",
+          "conditions": [
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "<",
+                  "value": 2
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "sum"
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
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+            "order": "asc"
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+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix or <=",
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+          "product_type",
+          "product_name",
+          {
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+            "alias": "sales_metric",
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+                  "value": ["技术", "家具"]
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+            "order": "asc"
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+            "order": "asc"
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+    {
+      "name": "nested WHERE matrix or not in",
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+                  "value": ["技术", "家具"]
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+            "field": "product_type",
+            "order": "asc"
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+      "name": "nested WHERE matrix or between",
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+            "order": "asc"
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+      "name": "nested WHERE matrix or not between",
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+            "field": "product_type",
+            "order": "asc"
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+    {
+      "name": "nested WHERE matrix or is null",
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+            "field": "product_type",
+            "order": "asc"
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+        "limit": 50
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+    {
+      "name": "nested WHERE matrix or is not null",
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+          "product_name",
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+                  "value": ["技术", "家具"]
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+            "field": "product_type",
+            "order": "asc"
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+        "limit": 50
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+    {
+      "name": "nested WHERE matrix or like",
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+                  "field": "product_name",
+                  "op": "like",
+                  "value": "%打印机%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
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+                  "value": ["技术", "家具"]
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+            "field": "product_type",
+            "order": "asc"
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+    {
+      "name": "nested WHERE matrix or not like",
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+                  "field": "product_name",
+                  "op": "not like",
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+                {
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+                  "op": "is null"
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+              ]
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+                  "value": ["技术", "家具"]
+                }
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+            "field": "product_type",
+            "order": "asc"
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+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix or ilike",
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+          "product_type",
+          "product_name",
+          {
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+            "alias": "sales_metric",
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+                  "field": "product_name",
+                  "op": "ilike",
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+                {
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+                  "value": ["技术", "家具"]
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+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "nested WHERE matrix or not ilike",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
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+                  "op": "not ilike",
+                  "value": "%打印机%"
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+                {
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+                  "op": "is null"
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+                  "value": ["技术", "家具"]
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+              ]
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+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "id",
+                  "aggr": {
+                    "func": "count"
+                  },
+                  "op": ">",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": "is null"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "string HAVING matrix min =",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "=",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "!=",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min >",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": ">=",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min <",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "<",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "<=",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min in",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "in",
+              "value": ["Fiskars 剪刀, 蓝色", "GlobeWeis 搭扣信封, 红色", null]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "not in",
+              "value": ["Fiskars 剪刀, 蓝色", "GlobeWeis 搭扣信封, 红色", null]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min between",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "between",
+              "value": ["Acco 孔加固材料, 回收", "贝尔金 鼠标, 耐用"]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "not between",
+              "value": ["Acco 孔加固材料, 回收", "贝尔金 鼠标, 耐用"]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "is null"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix min is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "min"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "min"
+              },
+              "op": "is not null"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max =",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "=",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max !=",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "!=",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max >",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max >=",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": ">=",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max <",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "<",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max <=",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "<=",
+              "value": "Fiskars 剪刀, 蓝色"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max in",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "in",
+              "value": ["Fiskars 剪刀, 蓝色", "GlobeWeis 搭扣信封, 红色", null]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max not in",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "not in",
+              "value": ["Fiskars 剪刀, 蓝色", "GlobeWeis 搭扣信封, 红色", null]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max between",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "between",
+              "value": ["Acco 孔加固材料, 回收", "贝尔金 鼠标, 耐用"]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max not between",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "not between",
+              "value": ["Acco 孔加固材料, 回收", "贝尔金 鼠标, 耐用"]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max is null",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "is null"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "string HAVING matrix max is not null",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "product_name",
+            "alias": "product_name_boundary",
+            "aggr": {
+              "func": "max"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "not ilike",
+                  "value": "%开信刀%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "product_name",
+              "aggr": {
+                "func": "max"
+              },
+              "op": "is not null"
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": ">",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "product_name_boundary",
+            "order": "asc",
+            "nulls": "last"
+          },
+          {
+            "field": "sales_metric",
+            "order": "desc"
+          }
+        ],
+        "limit": 10
+      }
+    },
+    {
+      "name": "deep mixed WHERE HAVING depth 2",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "between",
+                  "value": [-10000000000, 10000000000]
+                },
+                {
+                  "field": "product_type",
+                  "op": "=",
+                  "value": "办公用品"
+                }
+              ]
+            },
+            {
+              "field": "product_name",
+              "op": "like",
+              "value": "%剪刀%"
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "quantile"
+                  },
+                  "op": "between",
+                  "value": [-10, 100]
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "sum"
+                  },
+                  "op": "<=",
+                  "value": 1
+                }
+              ]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "deep mixed WHERE HAVING depth 4",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "op": "or",
+                      "conditions": [
+                        {
+                          "field": "sales",
+                          "op": "between",
+                          "value": [-10000000000, 10000000000]
+                        },
+                        {
+                          "field": "product_type",
+                          "op": "=",
+                          "value": "办公用品"
+                        }
+                      ]
+                    },
+                    {
+                      "field": "product_name",
+                      "op": "like",
+                      "value": "%剪刀%"
+                    }
+                  ]
+                },
+                {
+                  "field": "product_type",
+                  "op": "=",
+                  "value": "办公用品"
+                }
+              ]
+            },
+            {
+              "field": "product_name",
+              "op": "like",
+              "value": "%剪刀%"
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "op": "or",
+                      "conditions": [
+                        {
+                          "field": "sales",
+                          "aggr": {
+                            "func": "quantile"
+                          },
+                          "op": "between",
+                          "value": [-10, 100]
+                        },
+                        {
+                          "field": "sales",
+                          "aggr": {
+                            "func": "sum"
+                          },
+                          "op": "<=",
+                          "value": 1
+                        }
+                      ]
+                    },
+                    {
+                      "field": "sales",
+                      "aggr": {
+                        "func": "median"
+                      },
+                      "op": ">=",
+                      "value": 1
+                    }
+                  ]
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "sum"
+                  },
+                  "op": "<=",
+                  "value": 1
+                }
+              ]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "deep mixed WHERE HAVING depth 8",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "op": "or",
+                      "conditions": [
+                        {
+                          "op": "and",
+                          "conditions": [
+                            {
+                              "op": "or",
+                              "conditions": [
+                                {
+                                  "op": "and",
+                                  "conditions": [
+                                    {
+                                      "op": "or",
+                                      "conditions": [
+                                        {
+                                          "field": "sales",
+                                          "op": "between",
+                                          "value": [-10000000000, 10000000000]
+                                        },
+                                        {
+                                          "field": "product_type",
+                                          "op": "=",
+                                          "value": "办公用品"
+                                        }
+                                      ]
+                                    },
+                                    {
+                                      "field": "product_name",
+                                      "op": "like",
+                                      "value": "%剪刀%"
+                                    }
+                                  ]
+                                },
+                                {
+                                  "field": "product_type",
+                                  "op": "=",
+                                  "value": "办公用品"
+                                }
+                              ]
+                            },
+                            {
+                              "field": "product_name",
+                              "op": "like",
+                              "value": "%剪刀%"
+                            }
+                          ]
+                        },
+                        {
+                          "field": "product_type",
+                          "op": "=",
+                          "value": "办公用品"
+                        }
+                      ]
+                    },
+                    {
+                      "field": "product_name",
+                      "op": "like",
+                      "value": "%剪刀%"
+                    }
+                  ]
+                },
+                {
+                  "field": "product_type",
+                  "op": "=",
+                  "value": "办公用品"
+                }
+              ]
+            },
+            {
+              "field": "product_name",
+              "op": "like",
+              "value": "%剪刀%"
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "op": "or",
+                      "conditions": [
+                        {
+                          "op": "and",
+                          "conditions": [
+                            {
+                              "op": "or",
+                              "conditions": [
+                                {
+                                  "op": "and",
+                                  "conditions": [
+                                    {
+                                      "op": "or",
+                                      "conditions": [
+                                        {
+                                          "field": "sales",
+                                          "aggr": {
+                                            "func": "quantile"
+                                          },
+                                          "op": "between",
+                                          "value": [-10, 100]
+                                        },
+                                        {
+                                          "field": "sales",
+                                          "aggr": {
+                                            "func": "sum"
+                                          },
+                                          "op": "<=",
+                                          "value": 1
+                                        }
+                                      ]
+                                    },
+                                    {
+                                      "field": "sales",
+                                      "aggr": {
+                                        "func": "median"
+                                      },
+                                      "op": ">=",
+                                      "value": 1
+                                    }
+                                  ]
+                                },
+                                {
+                                  "field": "sales",
+                                  "aggr": {
+                                    "func": "sum"
+                                  },
+                                  "op": "<=",
+                                  "value": 1
+                                }
+                              ]
+                            },
+                            {
+                              "field": "sales",
+                              "aggr": {
+                                "func": "median"
+                              },
+                              "op": ">=",
+                              "value": 1
+                            }
+                          ]
+                        },
+                        {
+                          "field": "sales",
+                          "aggr": {
+                            "func": "sum"
+                          },
+                          "op": "<=",
+                          "value": 1
+                        }
+                      ]
+                    },
+                    {
+                      "field": "sales",
+                      "aggr": {
+                        "func": "median"
+                      },
+                      "op": ">=",
+                      "value": 1
+                    }
+                  ]
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "sum"
+                  },
+                  "op": "<=",
+                  "value": 1
+                }
+              ]
+            },
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "multiple date grouping aliases with multiple statistical fields",
+      "description": "同时分组两个日期别名与字符串维度，两个字段的 median/quantile 共用各自窗口，HAVING 包含未投影统计。",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "alias": "order_year",
+            "aggr": {
+              "func": "to_year"
+            }
+          },
+          {
+            "field": "order_date",
+            "alias": "order_month",
+            "aggr": {
+              "func": "to_month"
+            }
+          },
+          "product_type",
+          {
+            "field": "sales",
+            "alias": "median_value",
+            "aggr": {
+              "func": "median"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "q90",
+            "aggr": {
+              "func": "quantile",
+              "quantile": 0.9
+            }
+          },
+          {
+            "field": "profit",
+            "alias": "q10_profit",
+            "aggr": {
+              "func": "quantile",
+              "quantile": -0.1
+            }
+          },
+          {
+            "field": "profit",
+            "alias": "total",
+            "aggr": {
+              "func": "sum"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "order_date",
+                  "op": "between",
+                  "value": ["2016-01-01", "2019-12-30"]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["order_year", "order_month", "product_type"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile",
+                "quantile": 0.1
+              },
+              "op": ">",
+              "value": 1
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "profit",
+                  "aggr": {
+                    "func": "median"
+                  },
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "sales",
+                  "aggr": {
+                    "func": "variance"
+                  },
+                  "op": ">",
+                  "value": 1
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "order_year"
+          },
+          {
+            "field": "order_month"
+          },
+          {
+            "field": "q90",
+            "order": "desc",
+            "nulls": "first"
+          }
+        ],
+        "limit": 100
+      }
+    },
+    {
+      "name": "statistical aliases collide with internal rank names and source names",
+      "description": "业务别名占用内部窗口名称，源字段与 SELECT 别名冲突时 GROUP BY 优先解析源字段。",
+      "dsl": {
+        "select": [
+          "product_type",
+          {
+            "field": "profit",
+            "alias": "sales",
+            "aggr": {
+              "func": "sum"
+            }
+          },
+          {
+            "field": "sales",
+            "alias": "__vquery_stat_0",
+            "aggr": {
+              "func": "median"
+            }
+          },
+          {
+            "field": "profit",
+            "alias": "__vquery_stat_1",
+            "aggr": {
+              "func": "quantile",
+              "quantile": 0.9
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": ">=",
+                  "value": 0
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "sales"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "median"
+              },
+              "op": ">=",
+              "value": 1
+            },
+            {
+              "field": "profit",
+              "aggr": {
+                "func": "quantile"
+              },
+              "op": "<=",
+              "value": 1
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "__vquery_stat_0",
+            "order": "desc"
+          },
+          {
+            "field": "product_type"
+          }
+        ],
+        "limit": 5
+      }
+    },
+    {
+      "name": "all-column detail with nested filters and no limit",
+      "dsl": {
+        "select": [],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_name",
+                  "op": "ilike",
+                  "value": "Fiskars%"
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "order_date",
+            "order": "desc",
+            "nulls": "first"
+          },
+          {
+            "field": "id",
+            "order": "desc"
+          }
+        ]
+      }
+    },
+    {
+      "name": "empty groups embedded in complete statistical query",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "median"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": []
+            },
+            {
+              "op": "and",
+              "conditions": []
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "op": "and",
+              "conditions": []
+            },
+            {
+              "op": "or",
+              "conditions": []
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "signed fractional and scientific notation parameters with zero result limit",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_name",
+          {
+            "field": "sales",
+            "alias": "sales_metric",
+            "aggr": {
+              "func": "quantile"
+            }
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "sales",
+                  "op": "in",
+                  "value": [0, 0.125, -123.456, 1e-12, 1000000000000, null]
+                },
+                {
+                  "field": "profit",
+                  "op": "is null"
+                }
+              ]
+            },
+            {
+              "op": "and",
+              "conditions": [
+                {
+                  "field": "id",
+                  "op": ">=",
+                  "value": 1
+                },
+                {
+                  "field": "product_type",
+                  "op": "not in",
+                  "value": ["技术", "家具"]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "aggr": {
+                "func": "quantile",
+                "quantile": -0.14
+              },
+              "op": "between",
+              "value": [-1000000, 1000000]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "sales_metric",
+            "order": "desc",
+            "nulls": "last"
+          },
+          {
+            "field": "product_type",
+            "order": "asc"
+          }
+        ],
+        "limit": 0
+      }
+    },
+    {
+      "name": "超市全年经营汇总",
+      "description": "订单号、客户和商品按真实标识去重，汇总销售额、利润、件数与折扣。",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_id",
+            "aggr": {
+              "func": "count_distinct"
+            },
+            "alias": "order_count"
+          },
+          {
+            "field": "customer_id",
+            "aggr": {
+              "func": "count_distinct"
+            },
+            "alias": "customer_count"
+          },
+          {
+            "field": "product_id",
+            "aggr": {
+              "func": "count_distinct"
+            },
+            "alias": "product_count"
+          },
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_sales"
+          },
+          {
+            "field": "profit",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_profit"
+          },
+          {
+            "field": "amount",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_amount"
+          },
+          {
+            "field": "discount",
+            "aggr": {
+              "func": "avg"
+            },
+            "alias": "average_discount"
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            }
+          ]
+        }
+      }
+    },
+    {
+      "name": "按月份省份和商品类别比较销售利润",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "aggr": {
+              "func": "to_month"
+            },
+            "alias": "order_month"
+          },
+          "province",
+          "product_type",
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_sales"
+          },
+          {
+            "field": "profit",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_profit"
+          },
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "median"
+            },
+            "alias": "median_sales"
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            },
+            {
+              "field": "area",
+              "op": "in",
+              "value": ["华东", "中南"]
+            }
+          ]
+        },
+        "groupBy": ["order_month", "province", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": ">=",
+              "value": 1000,
+              "aggr": {
+                "func": "sum"
+              }
+            },
+            {
+              "field": "profit",
+              "op": ">",
+              "value": 0,
+              "aggr": {
+                "func": "sum"
+              }
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "order_month"
+          },
+          {
+            "field": "total_sales",
+            "order": "desc"
+          },
+          {
+            "field": "province"
+          }
+        ],
+        "limit": 100
+      }
+    },
+    {
+      "name": "浙江城市销售排行榜",
+      "dsl": {
+        "select": [
+          "province",
+          "city",
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_sales"
+          },
+          {
+            "field": "order_id",
+            "aggr": {
+              "func": "count_distinct"
+            },
+            "alias": "order_count"
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            },
+            {
+              "field": "province",
+              "op": "=",
+              "value": "浙江"
+            },
+            {
+              "field": "delivery_method",
+              "op": "in",
+              "value": ["标准级", "二级"]
+            }
+          ]
+        },
+        "groupBy": ["province", "city"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_id",
+              "op": ">=",
+              "value": 5,
+              "aggr": {
+                "func": "count_distinct"
+              }
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "total_sales",
+            "order": "desc"
+          },
+          {
+            "field": "city"
+          }
+        ],
+        "limit": 20
+      }
+    },
+    {
+      "name": "客户类型与复购客户贡献",
+      "dsl": {
+        "select": [
+          "customer_type",
+          "customer_id",
+          "customer_name",
+          {
+            "field": "order_id",
+            "aggr": {
+              "func": "count_distinct"
+            },
+            "alias": "order_count"
+          },
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_sales"
+          },
+          {
+            "field": "profit",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_profit"
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            },
+            {
+              "field": "customer_type",
+              "op": "in",
+              "value": ["公司", "消费者", "小型企业"]
+            }
+          ]
+        },
+        "groupBy": ["customer_type", "customer_id", "customer_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_id",
+              "op": ">",
+              "value": 1,
+              "aggr": {
+                "func": "count_distinct"
+              }
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "total_sales",
+            "order": "desc"
+          },
+          {
+            "field": "customer_id"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "商品层级利润与销售分位数",
+      "dsl": {
+        "select": [
+          "product_type",
+          "product_sub_type",
+          "product_id",
+          "product_name",
+          {
+            "field": "profit",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_profit"
+          },
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "quantile",
+              "quantile": 0.9
+            },
+            "alias": "sales_q90"
+          },
+          {
+            "field": "amount",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_amount"
+          },
+          {
+            "field": "discount",
+            "aggr": {
+              "func": "avg"
+            },
+            "alias": "average_discount"
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "product_type",
+                  "op": "=",
+                  "value": "办公用品"
+                },
+                {
+                  "field": "product_name",
+                  "op": "like",
+                  "value": "%打印机%"
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["product_type", "product_sub_type", "product_id", "product_name"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "amount",
+              "op": ">=",
+              "value": 10,
+              "aggr": {
+                "func": "sum"
+              }
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "total_profit",
+            "order": "desc"
+          },
+          {
+            "field": "product_id"
+          }
+        ],
+        "limit": 50
+      }
+    },
+    {
+      "name": "配送月份与配送方式销售构成",
+      "dsl": {
+        "select": [
+          {
+            "field": "delivery_date",
+            "aggr": {
+              "func": "to_month"
+            },
+            "alias": "delivery_month"
+          },
+          "delivery_method",
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_sales"
+          },
+          {
+            "field": "order_id",
+            "aggr": {
+              "func": "count_distinct"
+            },
+            "alias": "order_count"
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            },
+            {
+              "field": "delivery_date",
+              "op": "between",
+              "value": ["2019-01-01", "2020-01-10"]
+            }
+          ]
+        },
+        "groupBy": ["delivery_month", "delivery_method"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": ">",
+              "value": 0,
+              "aggr": {
+                "func": "sum"
+              }
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "delivery_month"
+          },
+          {
+            "field": "total_sales",
+            "order": "desc"
+          }
+        ],
+        "limit": 100
+      }
+    },
+    {
+      "name": "折扣亏损商品在地域中的分布",
+      "dsl": {
+        "select": [
+          "country_or_region",
+          "area",
+          "province",
+          "product_type",
+          {
+            "field": "profit",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_loss"
+          },
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_sales"
+          },
+          {
+            "field": "discount",
+            "aggr": {
+              "func": "avg"
+            },
+            "alias": "average_discount"
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            },
+            {
+              "field": "profit",
+              "op": "<",
+              "value": 0
+            },
+            {
+              "field": "discount",
+              "op": ">",
+              "value": 0
+            }
+          ]
+        },
+        "groupBy": ["country_or_region", "area", "province", "product_type"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "profit",
+              "op": "<",
+              "value": 0,
+              "aggr": {
+                "func": "sum"
+              }
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "total_loss",
+            "order": "asc"
+          },
+          {
+            "field": "province"
+          }
+        ],
+        "limit": 100
+      }
+    },
+    {
+      "name": "全部真实字段的订单明细",
+      "description": "明细投影完整覆盖 CSV 的 20 列，不引入合成源字段。",
+      "dsl": {
+        "select": [
+          "id",
+          "order_id",
+          "order_date",
+          "delivery_date",
+          "delivery_method",
+          "customer_id",
+          "customer_name",
+          "customer_type",
+          "city",
+          "province",
+          "country_or_region",
+          "area",
+          "product_id",
+          "product_type",
+          "product_sub_type",
+          "product_name",
+          "sales",
+          "amount",
+          "discount",
+          "profit"
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "province",
+                  "op": "=",
+                  "value": "浙江"
+                },
+                {
+                  "field": "province",
+                  "op": "=",
+                  "value": "四川"
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "order_date",
+            "order": "desc"
+          },
+          {
+            "field": "id",
+            "order": "desc"
+          }
+        ],
+        "limit": 100
+      }
+    },
+    {
+      "name": "下单日和配送日双日期分组",
+      "dsl": {
+        "select": [
+          {
+            "field": "order_date",
+            "aggr": {
+              "func": "to_day"
+            },
+            "alias": "order_day"
+          },
+          {
+            "field": "delivery_date",
+            "aggr": {
+              "func": "to_day"
+            },
+            "alias": "delivery_day"
+          },
+          "delivery_method",
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_sales"
+          },
+          {
+            "field": "profit",
+            "aggr": {
+              "func": "median"
+            },
+            "alias": "median_profit"
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            },
+            {
+              "field": "delivery_method",
+              "op": "=",
+              "value": "当日"
+            }
+          ]
+        },
+        "groupBy": ["order_day", "delivery_day", "delivery_method"],
+        "having": {
+          "op": "or",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": ">",
+              "value": 500,
+              "aggr": {
+                "func": "sum"
+              }
+            },
+            {
+              "field": "profit",
+              "op": "<",
+              "value": 0,
+              "aggr": {
+                "func": "median"
+              }
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "order_day"
+          },
+          {
+            "field": "delivery_day"
+          }
+        ],
+        "limit": 100
+      }
+    },
+    {
+      "name": "客户商品地域交叉销售分析",
+      "dsl": {
+        "select": [
+          "customer_type",
+          "product_type",
+          "area",
+          {
+            "field": "sales",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_sales"
+          },
+          {
+            "field": "profit",
+            "aggr": {
+              "func": "sum"
+            },
+            "alias": "total_profit"
+          },
+          {
+            "field": "order_id",
+            "aggr": {
+              "func": "count_distinct"
+            },
+            "alias": "order_count"
+          }
+        ],
+        "where": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "order_date",
+              "op": "between",
+              "value": ["2019-01-01", "2019-12-30"]
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "customer_type",
+                      "op": "=",
+                      "value": "公司"
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "=",
+                      "value": "技术"
+                    }
+                  ]
+                },
+                {
+                  "op": "and",
+                  "conditions": [
+                    {
+                      "field": "customer_type",
+                      "op": "=",
+                      "value": "消费者"
+                    },
+                    {
+                      "field": "product_type",
+                      "op": "=",
+                      "value": "家具"
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        "groupBy": ["customer_type", "product_type", "area"],
+        "having": {
+          "op": "and",
+          "conditions": [
+            {
+              "field": "sales",
+              "op": ">=",
+              "value": 1000,
+              "aggr": {
+                "func": "sum"
+              }
+            },
+            {
+              "op": "or",
+              "conditions": [
+                {
+                  "field": "profit",
+                  "op": ">",
+                  "value": 0,
+                  "aggr": {
+                    "func": "sum"
+                  }
+                },
+                {
+                  "field": "order_id",
+                  "op": ">",
+                  "value": 5,
+                  "aggr": {
+                    "func": "count_distinct"
+                  }
+                }
+              ]
+            }
+          ]
+        },
+        "orderBy": [
+          {
+            "field": "total_sales",
+            "order": "desc"
+          },
+          {
+            "field": "area"
+          }
+        ],
+        "limit": 50
+      }
+    }
+  ]
+}
diff --git a/packages/vquery/tests/examples/groupBy/basic.test.ts b/packages/vquery/tests/examples/groupBy/basic.test.ts
index d382956407..73227dd032 100644
--- a/packages/vquery/tests/examples/groupBy/basic.test.ts
+++ b/packages/vquery/tests/examples/groupBy/basic.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './basic.json'
 
 describe('Basic GroupBy Example', () => {
@@ -17,7 +17,7 @@ describe('Basic GroupBy Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", avg("salary") as "Average Salary" from "groupBy-basic" group by "department""`,
diff --git a/packages/vquery/tests/examples/having/basic.test.ts b/packages/vquery/tests/examples/having/basic.test.ts
index f6575cb714..1c5bed5615 100644
--- a/packages/vquery/tests/examples/having/basic.test.ts
+++ b/packages/vquery/tests/examples/having/basic.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './basic.json'
 
 describe('Basic Having Example - filter aggregated results', () => {
@@ -17,7 +17,7 @@ describe('Basic Having Example - filter aggregated results', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-basic" group by "department" having (sum("salary") > 5000)"`,
diff --git a/packages/vquery/tests/examples/having/between.test.ts b/packages/vquery/tests/examples/having/between.test.ts
index ab4c628ad7..f85c0d4eb3 100644
--- a/packages/vquery/tests/examples/having/between.test.ts
+++ b/packages/vquery/tests/examples/having/between.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './between.json'
 
 describe('Having Example with BETWEEN operator', () => {
@@ -17,7 +17,7 @@ describe('Having Example with BETWEEN operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-between" group by "department" having (sum("salary") between 5000 and 12000)"`,
diff --git a/packages/vquery/tests/examples/having/in-array.test.ts b/packages/vquery/tests/examples/having/in-array.test.ts
index ebca81b089..3d9552fed0 100644
--- a/packages/vquery/tests/examples/having/in-array.test.ts
+++ b/packages/vquery/tests/examples/having/in-array.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './in-array.json'
 
 describe('Having Example with array IN operator', () => {
@@ -17,7 +17,7 @@ describe('Having Example with array IN operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-in-array" group by "department" having (min("department") in ('HR', 'Engineering'))"`,
diff --git a/packages/vquery/tests/examples/having/in-single.test.ts b/packages/vquery/tests/examples/having/in-single.test.ts
index 9104e9c14e..d380406b1e 100644
--- a/packages/vquery/tests/examples/having/in-single.test.ts
+++ b/packages/vquery/tests/examples/having/in-single.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './in-single.json'
 
 describe('Having Example with single value IN operator', () => {
@@ -17,7 +17,7 @@ describe('Having Example with single value IN operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-in-single" group by "department" having (min("department") in ('HR'))"`,
diff --git a/packages/vquery/tests/examples/having/is-not-null.test.ts b/packages/vquery/tests/examples/having/is-not-null.test.ts
index 3129d5b9fa..52958c3280 100644
--- a/packages/vquery/tests/examples/having/is-not-null.test.ts
+++ b/packages/vquery/tests/examples/having/is-not-null.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './is-not-null.json'
 
 describe('Having Example with IS NOT NULL operator', () => {
@@ -17,7 +17,7 @@ describe('Having Example with IS NOT NULL operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-is-not-null" group by "department" having (sum("salary") is not null)"`,
diff --git a/packages/vquery/tests/examples/having/is-null.test.ts b/packages/vquery/tests/examples/having/is-null.test.ts
index f6b55ceffe..838e5d8e57 100644
--- a/packages/vquery/tests/examples/having/is-null.test.ts
+++ b/packages/vquery/tests/examples/having/is-null.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './is-null.json'
 
 describe('Having Example with IS NULL operator', () => {
@@ -17,7 +17,7 @@ describe('Having Example with IS NULL operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-is-null" group by "department" having (sum("salary") is null)"`,
diff --git a/packages/vquery/tests/examples/having/max.test.ts b/packages/vquery/tests/examples/having/max.test.ts
index 98743ea7a3..0721deb0ad 100644
--- a/packages/vquery/tests/examples/having/max.test.ts
+++ b/packages/vquery/tests/examples/having/max.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './max.json'
 
 describe('Having Example with MAX aggregate function', () => {
@@ -17,7 +17,7 @@ describe('Having Example with MAX aggregate function', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", max("salary") as "Max Salary" from "having-max" group by "department" having (max("salary") = 9000)"`,
diff --git a/packages/vquery/tests/examples/having/min.test.ts b/packages/vquery/tests/examples/having/min.test.ts
index d665abdeb0..5487c80599 100644
--- a/packages/vquery/tests/examples/having/min.test.ts
+++ b/packages/vquery/tests/examples/having/min.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './min.json'
 
 describe('Having Example with MIN aggregate function', () => {
@@ -17,7 +17,7 @@ describe('Having Example with MIN aggregate function', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", min("salary") as "Min Salary" from "having-min" group by "department" having (min("salary") = 5000)"`,
diff --git a/packages/vquery/tests/examples/having/multi-conditions.test.ts b/packages/vquery/tests/examples/having/multi-conditions.test.ts
index e5947040c1..51d9220c39 100644
--- a/packages/vquery/tests/examples/having/multi-conditions.test.ts
+++ b/packages/vquery/tests/examples/having/multi-conditions.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './multi-conditions.json'
 
 describe('Having Example with multiple conditions (AND/OR)', () => {
@@ -17,10 +17,10 @@ describe('Having Example with multiple conditions (AND/OR)', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "department", sum("salary") as "Total Salary", CAST(count("salary") AS INTEGER) as "Count" from "having-multi" group by "department" having (sum("salary") > 5000 and count("salary") > 1)"`,
+      `"select "department", sum("salary") as "Total Salary", count("salary") as "Count" from "having-multi" group by "department" having (sum("salary") > 5000 and count("salary") > 1)"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/having/not-between.test.ts b/packages/vquery/tests/examples/having/not-between.test.ts
index 2b75aff5d5..7f6d1e9c89 100644
--- a/packages/vquery/tests/examples/having/not-between.test.ts
+++ b/packages/vquery/tests/examples/having/not-between.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './not-between.json'
 
 describe('Having Example with NOT BETWEEN operator', () => {
@@ -17,7 +17,7 @@ describe('Having Example with NOT BETWEEN operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-not-between" group by "department" having (sum("salary") not between 5000 and 12000)"`,
diff --git a/packages/vquery/tests/examples/having/not-in-single.test.ts b/packages/vquery/tests/examples/having/not-in-single.test.ts
index 57c1093004..e251b432f6 100644
--- a/packages/vquery/tests/examples/having/not-in-single.test.ts
+++ b/packages/vquery/tests/examples/having/not-in-single.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './not-in-single.json'
 
 describe('Having Example with single value NOT IN operator', () => {
@@ -17,7 +17,7 @@ describe('Having Example with single value NOT IN operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-not-in-single" group by "department" having (not min("department") in ('HR'))"`,
diff --git a/packages/vquery/tests/examples/having/not-in.test.ts b/packages/vquery/tests/examples/having/not-in.test.ts
index f3ea2e713b..21f6ebb645 100644
--- a/packages/vquery/tests/examples/having/not-in.test.ts
+++ b/packages/vquery/tests/examples/having/not-in.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './not-in.json'
 
 describe('Having Example with NOT IN operator', () => {
@@ -17,7 +17,7 @@ describe('Having Example with NOT IN operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-not-in" group by "department" having (not min("department") in ('Marketing'))"`,
diff --git a/packages/vquery/tests/examples/having/or-conditions.test.ts b/packages/vquery/tests/examples/having/or-conditions.test.ts
index f8a2445fbd..5dbee294e4 100644
--- a/packages/vquery/tests/examples/having/or-conditions.test.ts
+++ b/packages/vquery/tests/examples/having/or-conditions.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './or-conditions.json'
 
 describe('Having Example with OR conditions', () => {
@@ -17,7 +17,7 @@ describe('Having Example with OR conditions', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "department", sum("salary") as "Total Salary" from "having-or" group by "department" having (sum("salary") > 15000 or sum("salary") < 5000)"`,
diff --git a/packages/vquery/tests/examples/limit/basic.test.ts b/packages/vquery/tests/examples/limit/basic.test.ts
index 5e74adb1a2..16ddbfb969 100644
--- a/packages/vquery/tests/examples/limit/basic.test.ts
+++ b/packages/vquery/tests/examples/limit/basic.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './basic.json'
 
 describe('Basic Limit Example', () => {
@@ -17,7 +17,7 @@ describe('Basic Limit Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(`"select "name" from "limit-basic" limit 2"`)
 
diff --git a/packages/vquery/tests/examples/orderBy/basic.test.ts b/packages/vquery/tests/examples/orderBy/basic.test.ts
index 448cc94cba..d1db8fa82c 100644
--- a/packages/vquery/tests/examples/orderBy/basic.test.ts
+++ b/packages/vquery/tests/examples/orderBy/basic.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './basic.json'
 
 describe('Basic OrderBy Example', () => {
@@ -17,9 +17,11 @@ describe('Basic OrderBy Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
-    expect(sql).toMatchInlineSnapshot(`"select "name", "salary" from "orderBy-basic" order by "salary" desc"`)
+    expect(sql).toMatchInlineSnapshot(
+      `"select "name", "salary" from "orderBy-basic" order by "salary" desc nulls last"`,
+    )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
 
diff --git a/packages/vquery/tests/examples/select/aggregation/avg.test.ts b/packages/vquery/tests/examples/select/aggregation/avg.test.ts
index 94e5213d63..19f40a5e53 100644
--- a/packages/vquery/tests/examples/select/aggregation/avg.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/avg.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './avg.json'
 
 describe('Select Avg Example', () => {
@@ -17,7 +17,7 @@ describe('Select Avg Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(`"select avg("salary") as "avg_salary" from "select-aggregation-avg""`)
 
diff --git a/packages/vquery/tests/examples/select/aggregation/count.test.ts b/packages/vquery/tests/examples/select/aggregation/count.test.ts
index edc0c5ec6b..41f4741f14 100644
--- a/packages/vquery/tests/examples/select/aggregation/count.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/count.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './count.json'
 
 describe('Select Count Example', () => {
@@ -17,11 +17,9 @@ describe('Select Count Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
-    expect(sql).toMatchInlineSnapshot(
-      `"select CAST(count("id") AS INTEGER) as "total_count" from "select-aggregation-count""`,
-    )
+    expect(sql).toMatchInlineSnapshot(`"select count("id") as "total_count" from "select-aggregation-count""`)
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
 
diff --git a/packages/vquery/tests/examples/select/aggregation/count_distinct.test.ts b/packages/vquery/tests/examples/select/aggregation/count_distinct.test.ts
index f1385bf749..da1449a5ac 100644
--- a/packages/vquery/tests/examples/select/aggregation/count_distinct.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/count_distinct.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './count_distinct.json'
 
 describe('Select Count Distinct Example', () => {
@@ -17,10 +17,10 @@ describe('Select Count Distinct Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select CAST(count(distinct "department") AS INTEGER) as "distinct_departments" from "select-aggregation-count-distinct""`,
+      `"select count(distinct "department") as "distinct_departments" from "select-aggregation-count-distinct""`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/aggregation/max.test.ts b/packages/vquery/tests/examples/select/aggregation/max.test.ts
index fdbfa41c5b..c2e38bc6ac 100644
--- a/packages/vquery/tests/examples/select/aggregation/max.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/max.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './max.json'
 
 describe('Select Max Example', () => {
@@ -17,7 +17,7 @@ describe('Select Max Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(`"select max("salary") as "max_salary" from "select-aggregation-max""`)
 
diff --git a/packages/vquery/tests/examples/select/aggregation/median.test.ts b/packages/vquery/tests/examples/select/aggregation/median.test.ts
index 378820b6fe..2d464cdca0 100644
--- a/packages/vquery/tests/examples/select/aggregation/median.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/median.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './median.json'
 
 describe('Select Median Example', () => {
@@ -17,7 +17,7 @@ describe('Select Median Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(`"select median("salary") as "salary_median" from "select-aggregation-median""`)
 
diff --git a/packages/vquery/tests/examples/select/aggregation/min.test.ts b/packages/vquery/tests/examples/select/aggregation/min.test.ts
index 098f95fdc3..eae7b380ed 100644
--- a/packages/vquery/tests/examples/select/aggregation/min.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/min.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './min.json'
 
 describe('Select Min Example', () => {
@@ -17,7 +17,7 @@ describe('Select Min Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(`"select min("salary") as "min_salary" from "select-aggregation-min""`)
 
diff --git a/packages/vquery/tests/examples/select/aggregation/quantile.test.ts b/packages/vquery/tests/examples/select/aggregation/quantile.test.ts
index a048d9b5fc..aa85567db2 100644
--- a/packages/vquery/tests/examples/select/aggregation/quantile.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/quantile.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './quantile.json'
 
 describe('Select Quantile Example', () => {
@@ -17,10 +17,10 @@ describe('Select Quantile Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select quantile("salary", 0.9) as "salary_quantile" from "select-aggregation-quantile""`,
+      `"select quantile("salary", CAST(0.9 AS DOUBLE)) as "salary_quantile" from "select-aggregation-quantile""`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/aggregation/stddev.test.ts b/packages/vquery/tests/examples/select/aggregation/stddev.test.ts
index fe0cd0d52c..dcdd53172f 100644
--- a/packages/vquery/tests/examples/select/aggregation/stddev.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/stddev.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './stddev.json'
 
 describe('Select Stddev Example', () => {
@@ -17,7 +17,7 @@ describe('Select Stddev Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(`"select stddev("salary") as "salary_stddev" from "select-aggregation-stddev""`)
 
diff --git a/packages/vquery/tests/examples/select/aggregation/sum.test.ts b/packages/vquery/tests/examples/select/aggregation/sum.test.ts
index e570ba7f20..6d9d955788 100644
--- a/packages/vquery/tests/examples/select/aggregation/sum.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/sum.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './sum.json'
 
 describe('Select Sum Example', () => {
@@ -17,7 +17,7 @@ describe('Select Sum Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(`"select sum("salary") as "total_salary" from "select-aggregation-sum""`)
 
diff --git a/packages/vquery/tests/examples/select/aggregation/variance.test.ts b/packages/vquery/tests/examples/select/aggregation/variance.test.ts
index a2957e5c25..c6fafceff4 100644
--- a/packages/vquery/tests/examples/select/aggregation/variance.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/variance.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './variance.json'
 
 describe('Select Variance Example', () => {
@@ -17,7 +17,7 @@ describe('Select Variance Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select var_samp("salary") as "salary_variance" from "select-aggregation-variance""`,
diff --git a/packages/vquery/tests/examples/select/aggregation/variancePop.test.ts b/packages/vquery/tests/examples/select/aggregation/variancePop.test.ts
index 5ab7f8cf65..e46c8ae126 100644
--- a/packages/vquery/tests/examples/select/aggregation/variancePop.test.ts
+++ b/packages/vquery/tests/examples/select/aggregation/variancePop.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './variancePop.json'
 
 describe('Select VariancePop Example', () => {
@@ -17,7 +17,7 @@ describe('Select VariancePop Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select var_pop("salary") as "salary_variance_pop" from "select-aggregation-variance-pop""`,
diff --git a/packages/vquery/tests/examples/select/basic.test.ts b/packages/vquery/tests/examples/select/basic.test.ts
index 07cbf78a29..28057286a3 100644
--- a/packages/vquery/tests/examples/select/basic.test.ts
+++ b/packages/vquery/tests/examples/select/basic.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './basic.json'
 
 describe('Basic Select Example', () => {
@@ -17,7 +17,7 @@ describe('Basic Select Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(`"select "name", "department", "salary" as "Income" from "select-basic""`)
 
diff --git a/packages/vquery/tests/examples/select/date/toDay.test.ts b/packages/vquery/tests/examples/select/date/toDay.test.ts
index bbadc602ad..a0a1f4b9f3 100644
--- a/packages/vquery/tests/examples/select/date/toDay.test.ts
+++ b/packages/vquery/tests/examples/select/date/toDay.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './toDay.json'
 
 describe('toDay', () => {
@@ -17,10 +17,10 @@ describe('toDay', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m-%d') as "day" from "vquery-demo" group by "day", "area" order by "Sum(sales)" desc limit 100"`,
+      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m-%d') as "day" from "vquery-demo" group by "day", "area" order by "Sum(sales)" desc nulls last limit 100"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/date/toHour.test.ts b/packages/vquery/tests/examples/select/date/toHour.test.ts
index 6b8dd86624..fbe3f3fbfa 100644
--- a/packages/vquery/tests/examples/select/date/toHour.test.ts
+++ b/packages/vquery/tests/examples/select/date/toHour.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './toHour.json'
 
 describe('toHour', () => {
@@ -17,10 +17,10 @@ describe('toHour', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m-%d %H') as "hour" from "vquery-demo" group by "hour", "area" order by "Sum(sales)" desc limit 100"`,
+      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m-%d %H') as "hour" from "vquery-demo" group by "hour", "area" order by "Sum(sales)" desc nulls last limit 100"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/date/toMinute.test.ts b/packages/vquery/tests/examples/select/date/toMinute.test.ts
index a01538948b..ba31f8a8d6 100644
--- a/packages/vquery/tests/examples/select/date/toMinute.test.ts
+++ b/packages/vquery/tests/examples/select/date/toMinute.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './toMinute.json'
 
 describe('toMinute', () => {
@@ -17,10 +17,10 @@ describe('toMinute', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m-%d %H:%M') as "minute" from "vquery-demo" group by "minute", "area" order by "Sum(sales)" desc limit 100"`,
+      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m-%d %H:%M') as "minute" from "vquery-demo" group by "minute", "area" order by "Sum(sales)" desc nulls last limit 100"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/date/toMonth.test.ts b/packages/vquery/tests/examples/select/date/toMonth.test.ts
index ce3a3d7fe9..d3f8c040f4 100644
--- a/packages/vquery/tests/examples/select/date/toMonth.test.ts
+++ b/packages/vquery/tests/examples/select/date/toMonth.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './toMonth.json'
 
 describe('toMonth', () => {
@@ -17,10 +17,10 @@ describe('toMonth', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m') as "month" from "vquery-demo" where (("area" in ('BEI JING') or "sales" >= 400)) group by "month", "area" order by "Sum(sales)" desc limit 100"`,
+      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m') as "month" from "vquery-demo" where (("area" in ('BEI JING') or "sales" >= 400)) group by "month", "area" order by "Sum(sales)" desc nulls last limit 100"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/date/toMonth_with_date_filter.test.ts b/packages/vquery/tests/examples/select/date/toMonth_with_date_filter.test.ts
index 66ac4fd1b4..458fa3a6af 100644
--- a/packages/vquery/tests/examples/select/date/toMonth_with_date_filter.test.ts
+++ b/packages/vquery/tests/examples/select/date/toMonth_with_date_filter.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './toMonth_with_date_filter.json'
 
 describe('toMonth with DATE range filter', () => {
@@ -17,10 +17,10 @@ describe('toMonth with DATE range filter', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m') as "month" from "select-date-toMonth-with-filter" where ("date" between '2025-01-01' and '2025-01-31') group by "month", "area" order by "Sum(sales)" desc limit 100"`,
+      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m') as "month" from "select-date-toMonth-with-filter" where ("date" between '2025-01-01' and '2025-01-31') group by "month", "area" order by "Sum(sales)" desc nulls last limit 100"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/date/toQuarter.test.ts b/packages/vquery/tests/examples/select/date/toQuarter.test.ts
index 25f763ba20..5247f0e3ca 100644
--- a/packages/vquery/tests/examples/select/date/toQuarter.test.ts
+++ b/packages/vquery/tests/examples/select/date/toQuarter.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './toQuarter.json'
 
 describe('toQuarter', () => {
@@ -17,10 +17,10 @@ describe('toQuarter', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("date" AS TIMESTAMP)) as "quarter" from "vquery-demo" group by "quarter", "area" order by "Sum(sales)" desc limit 100"`,
+      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y') || '-Q' || date_part('quarter', CAST("date" AS TIMESTAMP)) as "quarter" from "vquery-demo" group by "quarter", "area" order by "Sum(sales)" desc nulls last limit 100"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/date/toSecond.test.ts b/packages/vquery/tests/examples/select/date/toSecond.test.ts
index 194c805d0c..ca8b519390 100644
--- a/packages/vquery/tests/examples/select/date/toSecond.test.ts
+++ b/packages/vquery/tests/examples/select/date/toSecond.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './toSecond.json'
 
 describe('toSecond', () => {
@@ -17,10 +17,10 @@ describe('toSecond', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m-%d %H:%M:%S') as "second" from "vquery-demo" group by "second", "area" order by "Sum(sales)" desc limit 100"`,
+      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-%m-%d %H:%M:%S') as "second" from "vquery-demo" group by "second", "area" order by "Sum(sales)" desc nulls last limit 100"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/date/toWeek.test.ts b/packages/vquery/tests/examples/select/date/toWeek.test.ts
index 353b3b45a9..f2330e3039 100644
--- a/packages/vquery/tests/examples/select/date/toWeek.test.ts
+++ b/packages/vquery/tests/examples/select/date/toWeek.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './toWeek.json'
 
 describe('toWeek', () => {
@@ -17,10 +17,10 @@ describe('toWeek', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-W%W') as "week" from "vquery-demo" group by "week", "area" order by "Sum(sales)" desc limit 100"`,
+      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y-W%W') as "week" from "vquery-demo" group by "week", "area" order by "Sum(sales)" desc nulls last limit 100"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/select/date/toYear.test.ts b/packages/vquery/tests/examples/select/date/toYear.test.ts
index 4f5cbc9bd1..7f6c964707 100644
--- a/packages/vquery/tests/examples/select/date/toYear.test.ts
+++ b/packages/vquery/tests/examples/select/date/toYear.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './toYear.json'
 
 describe('toYear', () => {
@@ -17,10 +17,10 @@ describe('toYear', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y') as "year" from "vquery-demo" where (("area" in ('BEI JING') or "sales" >= 400)) group by "year", "area" order by "Sum(sales)" desc limit 100"`,
+      `"select "area", sum("sales") as "Sum(sales)", strftime(CAST("date" AS TIMESTAMP), '%Y') as "year" from "vquery-demo" where (("area" in ('BEI JING') or "sales" >= 400)) group by "year", "area" order by "Sum(sales)" desc nulls last limit 100"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/where/basic.test.ts b/packages/vquery/tests/examples/where/basic.test.ts
index ab3f6f90f3..5220a659a2 100644
--- a/packages/vquery/tests/examples/where/basic.test.ts
+++ b/packages/vquery/tests/examples/where/basic.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './basic.json'
 
 describe('Basic Where Example', () => {
@@ -17,7 +17,7 @@ describe('Basic Where Example', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "name", "department", "salary" from "where-basic" where ("department" = 'Engineering')"`,
diff --git a/packages/vquery/tests/examples/where/between_operator.test.ts b/packages/vquery/tests/examples/where/between_operator.test.ts
index 6f801fb4e0..daa57281d6 100644
--- a/packages/vquery/tests/examples/where/between_operator.test.ts
+++ b/packages/vquery/tests/examples/where/between_operator.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './between_operator.json'
 
 describe('Where with BETWEEN / NOT BETWEEN Operator', () => {
@@ -17,7 +17,7 @@ describe('Where with BETWEEN / NOT BETWEEN Operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "id", "name", "age", "gender" from "where-between-operator" where (("age" between 18 and 30 and "gender" in ('male', 'female')) or ("age" not between 18 and 30 and not "gender" in ('male', 'female')))"`,
diff --git a/packages/vquery/tests/examples/where/complex_logic.test.ts b/packages/vquery/tests/examples/where/complex_logic.test.ts
index c7bb9c4f5e..57f0703a70 100644
--- a/packages/vquery/tests/examples/where/complex_logic.test.ts
+++ b/packages/vquery/tests/examples/where/complex_logic.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './complex_logic.json'
 
 describe('Where with Complex AND/OR Logic', () => {
@@ -17,7 +17,7 @@ describe('Where with Complex AND/OR Logic', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "id", "name", "age", "gender" from "where-complex-logic" where (("age" >= 18 and "gender" = 'male') or ("age" < 18 and "gender" = 'female'))"`,
diff --git a/packages/vquery/tests/examples/where/date_between.test.ts b/packages/vquery/tests/examples/where/date_between.test.ts
index 41f4a0ccca..24942c5627 100644
--- a/packages/vquery/tests/examples/where/date_between.test.ts
+++ b/packages/vquery/tests/examples/where/date_between.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './date_between.json'
 
 describe('Where with DATE field BETWEEN filter', () => {
@@ -17,10 +17,10 @@ describe('Where with DATE field BETWEEN filter', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "id", "area", "sales", strftime(CAST("order_date" AS TIMESTAMP), '%Y-%m-%d') as "order_day" from "where-date-between" where ("order_date" between '2024-01-01' and '2024-01-31') order by "order_day" asc"`,
+      `"select "id", "area", "sales", strftime(CAST("order_date" AS TIMESTAMP), '%Y-%m-%d') as "order_day" from "where-date-between" where ("order_date" between '2024-01-01' and '2024-01-31') order by "order_day" asc nulls last"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/examples/where/in_operator.test.ts b/packages/vquery/tests/examples/where/in_operator.test.ts
index 3b7cedaa2d..f5d12a8abb 100644
--- a/packages/vquery/tests/examples/where/in_operator.test.ts
+++ b/packages/vquery/tests/examples/where/in_operator.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './in_operator.json'
 
 describe('Where with IN / NOT IN Operator', () => {
@@ -17,7 +17,7 @@ describe('Where with IN / NOT IN Operator', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "id", "name", "age", "gender" from "where-in-operator" where (("age" >= 18 and "gender" in ('male', 'female')) or ("age" < 18 and not "gender" in ('male', 'female')))"`,
diff --git a/packages/vquery/tests/examples/where/null_check.test.ts b/packages/vquery/tests/examples/where/null_check.test.ts
index 7a0bad2dd5..077831d3d8 100644
--- a/packages/vquery/tests/examples/where/null_check.test.ts
+++ b/packages/vquery/tests/examples/where/null_check.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './null_check.json'
 
 describe('Where with IS NULL / IS NOT NULL', () => {
@@ -17,7 +17,7 @@ describe('Where with IS NULL / IS NOT NULL', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "id", "name", "age", "gender" from "where-null-check" where (("age" >= 18 and "gender" is null) or ("age" < 18 and "gender" is not null))"`,
diff --git a/packages/vquery/tests/examples/where/single_value_in.test.ts b/packages/vquery/tests/examples/where/single_value_in.test.ts
index af3d5f0927..a21e9aa3bc 100644
--- a/packages/vquery/tests/examples/where/single_value_in.test.ts
+++ b/packages/vquery/tests/examples/where/single_value_in.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './single_value_in.json'
 
 describe('Where with Single Value IN / NOT IN', () => {
@@ -17,7 +17,7 @@ describe('Where with Single Value IN / NOT IN', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
       `"select "id", "name", "age", "gender" from "where-single-value-in" where ("gender" in ('male') or not "gender" in ('female'))"`,
diff --git a/packages/vquery/tests/examples/where/timestamp_window.test.ts b/packages/vquery/tests/examples/where/timestamp_window.test.ts
index 3724520596..f3a4685bc0 100644
--- a/packages/vquery/tests/examples/where/timestamp_window.test.ts
+++ b/packages/vquery/tests/examples/where/timestamp_window.test.ts
@@ -1,5 +1,5 @@
 import type { DatasetColumn, VQueryDSL } from '@visactor/vquery'
-import { convertDSLToSQL, VQuery } from '@visactor/vquery'
+import { convertDSLToDuckDB, VQuery } from '@visactor/vquery'
 import vqueryConfig from './timestamp_window.json'
 
 describe('Where with TIMESTAMP comparison window', () => {
@@ -17,10 +17,10 @@ describe('Where with TIMESTAMP comparison window', () => {
 
     const dataset = await vquery.connectDataset(datasetId)
 
-    const sql = convertDSLToSQL(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
+    const sql = convertDSLToDuckDB(vqueryDSL as VQueryDSL<Record<string, string | number>>, datasetId)
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "id", "event", "amount", strftime(CAST("created_at" AS TIMESTAMP), '%Y-%m-%d %H:%M') as "created_minute" from "where-timestamp-window" where ("created_at" >= '2024-01-15 12:00:00' and "created_at" < '2024-02-01 00:00:00') order by "created_minute" asc"`,
+      `"select "id", "event", "amount", strftime(CAST("created_at" AS TIMESTAMP), '%Y-%m-%d %H:%M') as "created_minute" from "where-timestamp-window" where ("created_at" >= '2024-01-15 12:00:00' and "created_at" < '2024-02-01 00:00:00') order by "created_minute" asc nulls last"`,
     )
 
     const queryResult = await dataset.query(vqueryDSL as VQueryDSL<Record<string, string | number>>)
diff --git a/packages/vquery/tests/unit/sql-builder/builders/groupBy.test.ts b/packages/vquery/tests/unit/sql-builder/builders/groupBy.test.ts
index 8190078c0c..b84bf20a10 100644
--- a/packages/vquery/tests/unit/sql-builder/builders/groupBy.test.ts
+++ b/packages/vquery/tests/unit/sql-builder/builders/groupBy.test.ts
@@ -1,4 +1,4 @@
-import { convertDSLToSQL } from '@visactor/vquery'
+import { convertDSLToDuckDB } from '@visactor/vquery'
 
 describe('groupBy', () => {
   it('simple', () => {
@@ -10,7 +10,7 @@ describe('groupBy', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id', 'department'],
         limit: 100,
diff --git a/packages/vquery/tests/unit/sql-builder/builders/having.test.ts b/packages/vquery/tests/unit/sql-builder/builders/having.test.ts
index 44ae7f411b..37358e061a 100644
--- a/packages/vquery/tests/unit/sql-builder/builders/having.test.ts
+++ b/packages/vquery/tests/unit/sql-builder/builders/having.test.ts
@@ -1,4 +1,4 @@
-import { convertDSLToSQL } from '@visactor/vquery'
+import { convertDSLToDuckDB } from '@visactor/vquery'
 
 describe('having', () => {
   it('empty', () => {
@@ -8,7 +8,7 @@ describe('having', () => {
       department: string
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['department', { field: 'age', aggr: { func: 'sum' }, alias: 'TOTAL_AGE' }],
         groupBy: ['department'],
@@ -28,7 +28,7 @@ describe('having', () => {
       department: string
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: [
           'department',
@@ -54,7 +54,7 @@ describe('having', () => {
     )
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "department", sum("age") as "SUM_AGE", avg("age") as "AVG_AGE", CAST(count("id") AS INTEGER) as "CNT", min("age") as "MIN_AGE", max("age") as "MAX_AGE" from "orders" group by "department" having (sum("age") = 100 and avg("age") = 20 and count("id") = 2 and min("age") = 10 and max("age") = 80)"`,
+      `"select "department", sum("age") as "SUM_AGE", avg("age") as "AVG_AGE", count("id") as "CNT", min("age") as "MIN_AGE", max("age") as "MAX_AGE" from "orders" group by "department" having (sum("age") = 100 and avg("age") = 20 and count("id") = 2 and min("age") = 10 and max("age") = 80)"`,
     )
   })
 
@@ -65,7 +65,7 @@ describe('having', () => {
       department: string
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: [
           'department',
@@ -89,7 +89,7 @@ describe('having', () => {
     )
 
     expect(sql).toMatchInlineSnapshot(
-      `"select "department", CAST(count(distinct "id") AS INTEGER) as "DISTINCT_ID", var_samp("age") as "VAR_AGE", var_pop("age") as "VAR_POP_AGE", quantile("age", 0.9) as "Q90_AGE" from "orders" group by "department" having (count(distinct "id") > 1 and var_samp("age") >= 2 and var_pop("age") >= 1 and quantile("age", 0.5) > 20)"`,
+      `"select "department", count(distinct "id") as "DISTINCT_ID", var_samp("age") as "VAR_AGE", var_pop("age") as "VAR_POP_AGE", quantile("age", CAST(0.9 AS DOUBLE)) as "Q90_AGE" from "orders" group by "department" having (count(distinct "id") > 1 and var_samp("age") >= 2 and var_pop("age") >= 1 and quantile("age", CAST(0.5 AS DOUBLE)) > 20)"`,
     )
   })
 
@@ -101,7 +101,7 @@ describe('having', () => {
       province: string
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: [
           { field: 'province', alias: 'province', aggr: { func: 'count_distinct' } },
@@ -119,7 +119,7 @@ describe('having', () => {
     )
 
     expect(sql).toMatchInlineSnapshot(
-      `"select CAST(count(distinct "province") AS INTEGER) as "province", sum("profit") as "profit", "area" as "area" from "orders" group by "area" having (sum("sales") > 5) limit 1000"`,
+      `"select count(distinct "province") as "province", sum("profit") as "profit", "area" as "area" from "orders" group by "area" having (sum("sales") > 5) limit 1000"`,
     )
   })
 
@@ -130,7 +130,7 @@ describe('having', () => {
       department: string
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['department', { field: 'age', aggr: { func: 'avg' }, alias: 'AVG_AGE' }],
         groupBy: ['department'],
@@ -163,7 +163,7 @@ describe('having', () => {
       department: string | null
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['department', { field: 'age', aggr: { func: 'sum' }, alias: 'TOTAL_AGE' }],
         groupBy: ['department'],
@@ -190,7 +190,7 @@ describe('having', () => {
       department: string
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['department', { field: 'age', aggr: { func: 'avg' }, alias: 'AVG_AGE' }],
         groupBy: ['department'],
@@ -217,7 +217,7 @@ describe('having', () => {
       department: string
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['department', { field: 'age', aggr: { func: 'sum' }, alias: 'TOTAL_AGE' }],
         groupBy: ['department'],
@@ -244,7 +244,7 @@ describe('having', () => {
     }
 
     expect(() =>
-      convertDSLToSQL<USER, 'orders'>(
+      convertDSLToDuckDB<USER, 'orders'>(
         {
           select: ['department', { field: 'age', aggr: { func: 'sum' }, alias: 'TOTAL_AGE' }],
           groupBy: ['department'],
diff --git a/packages/vquery/tests/unit/sql-builder/builders/limit.test.ts b/packages/vquery/tests/unit/sql-builder/builders/limit.test.ts
index 32711505b0..25eb73b554 100644
--- a/packages/vquery/tests/unit/sql-builder/builders/limit.test.ts
+++ b/packages/vquery/tests/unit/sql-builder/builders/limit.test.ts
@@ -1,4 +1,4 @@
-import { convertDSLToSQL } from '@visactor/vquery'
+import { convertDSLToDuckDB } from '@visactor/vquery'
 
 describe('limit', () => {
   it('simple', () => {
@@ -10,7 +10,7 @@ describe('limit', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id'],
         limit: 100,
diff --git a/packages/vquery/tests/unit/sql-builder/builders/orderBy.test.ts b/packages/vquery/tests/unit/sql-builder/builders/orderBy.test.ts
index 0ca1809554..f79707091e 100644
--- a/packages/vquery/tests/unit/sql-builder/builders/orderBy.test.ts
+++ b/packages/vquery/tests/unit/sql-builder/builders/orderBy.test.ts
@@ -1,4 +1,4 @@
-import { convertDSLToSQL } from '@visactor/vquery'
+import { convertDSLToDuckDB } from '@visactor/vquery'
 
 describe('orderBy', () => {
   it('desc', () => {
@@ -10,7 +10,7 @@ describe('orderBy', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id'],
         orderBy: [
@@ -23,7 +23,7 @@ describe('orderBy', () => {
       },
       'orders',
     )
-    expect(sql).toMatchInlineSnapshot(`"select "id" from "orders" order by "id" desc limit 100"`)
+    expect(sql).toMatchInlineSnapshot(`"select "id" from "orders" order by "id" desc nulls last limit 100"`)
   })
 
   it('asc', () => {
@@ -35,7 +35,7 @@ describe('orderBy', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id'],
         orderBy: [
@@ -48,14 +48,14 @@ describe('orderBy', () => {
       },
       'orders',
     )
-    expect(sql).toMatchInlineSnapshot(`"select "id" from "orders" order by "id" asc limit 100"`)
+    expect(sql).toMatchInlineSnapshot(`"select "id" from "orders" order by "id" asc nulls last limit 100"`)
   })
 
   it('order defaults to asc when omitted', () => {
     interface USER {
       id: number
     }
-    const sql = convertDSLToSQL<USER, 'orders'>({ select: ['id'], orderBy: [{ field: 'id' }] }, 'orders')
-    expect(sql).toMatchInlineSnapshot(`"select "id" from "orders" order by "id" asc"`)
+    const sql = convertDSLToDuckDB<USER, 'orders'>({ select: ['id'], orderBy: [{ field: 'id' }] }, 'orders')
+    expect(sql).toMatchInlineSnapshot(`"select "id" from "orders" order by "id" asc nulls last"`)
   })
 })
diff --git a/packages/vquery/tests/unit/sql-builder/builders/select.test.ts b/packages/vquery/tests/unit/sql-builder/builders/select.test.ts
index 286ea13973..3ede0dfded 100644
--- a/packages/vquery/tests/unit/sql-builder/builders/select.test.ts
+++ b/packages/vquery/tests/unit/sql-builder/builders/select.test.ts
@@ -1,4 +1,4 @@
-import { convertDSLToSQL } from '@visactor/vquery'
+import { convertDSLToDuckDB } from '@visactor/vquery'
 
 describe('select', () => {
   it('simple', () => {
@@ -10,7 +10,7 @@ describe('select', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id'],
       },
@@ -25,7 +25,7 @@ describe('select', () => {
       profit: number
     }
 
-    const sql = convertDSLToSQL<USER, 'demoDataset'>(
+    const sql = convertDSLToDuckDB<USER, 'demoDataset'>(
       {
         select: [
           {
@@ -57,7 +57,7 @@ describe('select', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: [
           'department',
@@ -79,7 +79,7 @@ describe('select', () => {
       'orders',
     )
     expect(sql).toMatchInlineSnapshot(
-      `"select "department", "name", avg("age") as "AGE" from "orders" group by "department", "name" order by "age" desc"`,
+      `"select "department", "name", avg("age") as "AGE" from "orders" group by "department", "name" order by "age" desc nulls last"`,
     )
   })
 
@@ -92,7 +92,7 @@ describe('select', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: [
           'department',
@@ -117,7 +117,7 @@ describe('select', () => {
       'orders',
     )
     expect(sql).toMatchInlineSnapshot(
-      `"select "department", "name" as "NAME", avg("age") as "AGE" from "orders" group by "department", "name" order by "age" desc"`,
+      `"select "department", "name" as "NAME", avg("age") as "AGE" from "orders" group by "department", "name" order by "age" desc nulls last"`,
     )
   })
 
@@ -130,7 +130,7 @@ describe('select', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: [
           {
@@ -157,7 +157,7 @@ describe('select', () => {
       'orders',
     )
     expect(sql).toMatchInlineSnapshot(
-      `"select "department" as "department", "name" as "NAME", avg("age") as "AGE" from "orders" group by "department", "name" order by "age" desc"`,
+      `"select "department" as "department", "name" as "NAME", avg("age") as "AGE" from "orders" group by "department", "name" order by "age" desc nulls last"`,
     )
   })
 
@@ -170,7 +170,7 @@ describe('select', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: [
           { field: 'age', aggr: { func: 'sum' }, alias: 'SUM' },
@@ -182,7 +182,7 @@ describe('select', () => {
       'orders',
     )
     expect(sql).toMatchInlineSnapshot(
-      `"select sum("age") as "SUM", min("age") as "MIN", max("age") as "MAX", CAST(count("id") AS INTEGER) as "CNT" from "orders""`,
+      `"select sum("age") as "SUM", min("age") as "MIN", max("age") as "MAX", count("id") as "CNT" from "orders""`,
     )
   })
 
@@ -195,7 +195,7 @@ describe('select', () => {
       active: number
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: [],
         groupBy: ['department', 'name'],
@@ -208,7 +208,9 @@ describe('select', () => {
       },
       'orders',
     )
-    expect(sql).toMatchInlineSnapshot(`"select * from "orders" group by "department", "name" order by "age" desc"`)
+    expect(sql).toMatchInlineSnapshot(
+      `"select * from "orders" group by "department", "name" order by "age" desc nulls last"`,
+    )
   })
 
   it('aggregate without alias falls back to field name', () => {
@@ -216,7 +218,7 @@ describe('select', () => {
       id: number
       age: number
     }
-    const sql = convertDSLToSQL<USER, 'orders'>({ select: [{ field: 'age', aggr: { func: 'avg' } }] }, 'orders')
+    const sql = convertDSLToDuckDB<USER, 'orders'>({ select: [{ field: 'age', aggr: { func: 'avg' } }] }, 'orders')
     expect(sql).toMatchInlineSnapshot(`"select avg("age") as "age" from "orders""`)
   })
 
@@ -225,7 +227,10 @@ describe('select', () => {
       value: number
     }
 
-    const sql = convertDSLToSQL<ORDER, 'orders'>({ select: [{ field: 'value', aggr: { func: 'quantile' } }] }, 'orders')
-    expect(sql).toMatchInlineSnapshot(`"select quantile("value", 0.5) as "value" from "orders""`)
+    const sql = convertDSLToDuckDB<ORDER, 'orders'>(
+      { select: [{ field: 'value', aggr: { func: 'quantile' } }] },
+      'orders',
+    )
+    expect(sql).toMatchInlineSnapshot(`"select quantile("value", CAST(0.5 AS DOUBLE)) as "value" from "orders""`)
   })
 })
diff --git a/packages/vquery/tests/unit/sql-builder/builders/where.test.ts b/packages/vquery/tests/unit/sql-builder/builders/where.test.ts
index c7ff74bc2c..53294c8aa0 100644
--- a/packages/vquery/tests/unit/sql-builder/builders/where.test.ts
+++ b/packages/vquery/tests/unit/sql-builder/builders/where.test.ts
@@ -1,4 +1,4 @@
-import { convertDSLToSQL } from '@visactor/vquery'
+import { convertDSLToDuckDB } from '@visactor/vquery'
 
 describe('where', () => {
   it('simple', () => {
@@ -11,7 +11,7 @@ describe('where', () => {
       gender: 'male' | 'female'
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id'],
         where: {
@@ -67,7 +67,7 @@ describe('where', () => {
       gender: 'male' | 'female'
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id'],
       },
@@ -86,7 +86,7 @@ describe('where', () => {
       gender: 'male' | 'female'
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id'],
         where: {
@@ -140,7 +140,7 @@ describe('where', () => {
       gender: 'male' | 'female'
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id'],
         where: {
@@ -196,7 +196,7 @@ describe('where', () => {
       gender: 'male' | 'female'
     }
 
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       {
         select: ['id'],
         where: {
@@ -247,7 +247,7 @@ describe('where', () => {
       id: number
       gender: 'male' | 'female'
     }
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       { select: ['id'], where: { op: 'and', conditions: [{ field: 'gender', op: 'in', value: 'male' }] } },
       'orders',
     )
@@ -259,7 +259,7 @@ describe('where', () => {
       id: number
       gender: 'male' | 'female'
     }
-    const sql = convertDSLToSQL<USER, 'orders'>(
+    const sql = convertDSLToDuckDB<USER, 'orders'>(
       { select: ['id'], where: { op: 'and', conditions: [{ field: 'gender', op: 'not in', value: 'female' }] } },
       'orders',
     )
@@ -272,7 +272,7 @@ describe('where', () => {
       order_date: string
     }
 
-    const sql = convertDSLToSQL<ORDER, 'orders'>(
+    const sql = convertDSLToDuckDB<ORDER, 'orders'>(
       {
         select: ['id'],
         where: {
@@ -310,7 +310,7 @@ describe('where', () => {
       order_date: string
     }
 
-    const sql = convertDSLToSQL<ORDER, 'orders'>(
+    const sql = convertDSLToDuckDB<ORDER, 'orders'>(
       {
         select: ['id'],
         where: {
@@ -341,7 +341,7 @@ describe('where', () => {
 
     const createdAt = new Date('2024-01-01T00:00:00.000Z')
     const updatedAt = new Date('2024-02-01T12:30:00.000Z')
-    const sql = convertDSLToSQL<EVENT, 'orders'>(
+    const sql = convertDSLToDuckDB<EVENT, 'orders'>(
       {
         select: ['id'],
         where: {
@@ -364,7 +364,7 @@ describe('where', () => {
     )
 
     expect(sql).toBe(
-      `select "id" from "orders" where ("created_at" >= '${createdAt.toString()}' and "updated_at" <= '${updatedAt.toString()}')`,
+      `select "id" from "orders" where ("created_at" >= '${createdAt.toISOString().slice(0, 23).replace('T', ' ')}' and "updated_at" <= '${updatedAt.toISOString().slice(0, 23).replace('T', ' ')}')`,
     )
   })
 
@@ -376,7 +376,7 @@ describe('where', () => {
 
     const start = new Date('2024-01-01T00:00:00.000Z')
     const end = new Date('2024-01-31T23:59:59.000Z')
-    const sql = convertDSLToSQL<EVENT, 'orders'>(
+    const sql = convertDSLToDuckDB<EVENT, 'orders'>(
       {
         select: ['id'],
         where: {
@@ -394,7 +394,7 @@ describe('where', () => {
     )
 
     expect(sql).toBe(
-      `select "id" from "orders" where ("created_at" between '${start.toString()}' and '${end.toString()}')`,
+      `select "id" from "orders" where ("created_at" between '${start.toISOString().slice(0, 23).replace('T', ' ')}' and '${end.toISOString().slice(0, 23).replace('T', ' ')}')`,
     )
   })
 })
diff --git a/packages/vquery/tests/unit/sql-builder/compile/dialect.introspector.test.ts b/packages/vquery/tests/unit/sql-builder/compile/dialect.introspector.test.ts
index aa5c606354..e5923bf5d8 100644
--- a/packages/vquery/tests/unit/sql-builder/compile/dialect.introspector.test.ts
+++ b/packages/vquery/tests/unit/sql-builder/compile/dialect.introspector.test.ts
@@ -1,19 +1,17 @@
-import { PostgresDialect } from '../../../../src/sql-builder/dialect/postgresDialect'
+import { mysqlStrategy, duckdbStrategy } from '../../../../src/sql-builder/dialect'
 import { Kysely } from 'kysely'
 
-describe('postgres dialect introspector', () => {
-  it('introspector methods return empty', async () => {
-    const dialect = new PostgresDialect()
+for (const [name, strategy] of Object.entries({ mysql: mysqlStrategy, duckdb: duckdbStrategy })) {
+  it(`${name} compiles without a database driver or live schema`, async () => {
+    const { dialect } = strategy
     const db = new Kysely<{ t: { id: number } }>({ dialect })
     const introspector = dialect.createIntrospector(db)
-    const schemas = await introspector.getSchemas()
-    const tables = await introspector.getTables()
-    const meta = await introspector.getMetadata()
-    expect(Array.isArray(schemas)).toBe(true)
-    expect(schemas.length).toBe(0)
-    expect(Array.isArray(tables)).toBe(true)
-    expect(tables.length).toBe(0)
-    expect(Array.isArray(meta.tables)).toBe(true)
-    expect(meta.tables.length).toBe(0)
+    expect(await introspector.getSchemas()).toEqual([])
+    expect(await introspector.getTables()).toEqual([])
+    expect(await introspector.getMetadata()).toEqual({ tables: [] })
+    expect(dialect.createDriver()).toBeDefined()
+    expect(dialect.createQueryCompiler()).toBeDefined()
+    expect(dialect.createAdapter()).toBeDefined()
+    await db.destroy()
   })
-})
+}
diff --git a/packages/vquery/tests/unit/sql-builder/compileQuery.test.ts b/packages/vquery/tests/unit/sql-builder/compileQuery.test.ts
new file mode 100644
index 0000000000..b378f99193
--- /dev/null
+++ b/packages/vquery/tests/unit/sql-builder/compileQuery.test.ts
@@ -0,0 +1,195 @@
+import { compileQuery, convertDSLToMysql, convertDSLToDuckDB, VQuery } from '@visactor/vquery'
+import type { VQueryDSL } from '@visactor/vquery'
+
+const invalid: unknown[] = [
+  null,
+  [],
+  {},
+  { select: 'id' },
+  { select: [''] },
+  { select: [null] },
+  { select: [{ field: '' }] },
+  { select: [{ field: 'id', alias: 1 }] },
+  { select: ['id'], sql: 'DROP TABLE orders' },
+  ...[
+    null,
+    { func: 'sleep' },
+    { func: 'sum', evil: true },
+    { func: 'sum', quantile: 0.5 },
+    { func: 'quantile', quantile: '0.5' },
+    { func: 'quantile', quantile: Infinity },
+    { func: 'quantile', quantile: -2 },
+    { func: 'quantile', quantile: 2 },
+  ].map((aggr) => ({ select: [{ field: 'id', aggr }] })),
+  ...[
+    null,
+    { op: 'xor', conditions: [] },
+    { op: 'and', conditions: {} },
+    { field: '' },
+    { field: 'id', op: 1 },
+    { field: 'id', op: 'sleep' },
+    { field: 'id', op: '=', value: {} },
+    { field: 'id', op: '=', value: Infinity },
+    { field: 'id', op: 'in', value: [] },
+    { field: 'id', op: 'in', value: [1, {}] },
+    { field: 'id', op: 'in', value: {} },
+    { field: 'id', op: 'between', value: 1 },
+    { field: 'id', op: 'between', value: [1] },
+    { field: 'id', op: 'between', value: [1, {}] },
+  ].map((where) => ({ select: ['id'], where })),
+  ...[null, { field: 'id', op: '=', value: 1 }, { field: 'id', op: '=', value: 1, aggr: { func: 'to_year' } }].map(
+    (having) => ({ select: ['id'], having }),
+  ),
+  ...['id', [null], ['']].map((groupBy) => ({ select: ['id'], groupBy })),
+  ...['id', [null], [{ field: '' }], [{ field: 'id', order: 'evil' }]].map((orderBy) => ({
+    select: ['id'],
+    orderBy,
+  })),
+  ...[-1, 1.5, Infinity, '1;DROP'].map((limit) => ({ select: ['id'], limit })),
+]
+
+const compile = (dsl: unknown, dialect: 'mysql' | 'duckdb' = 'mysql') =>
+  compileQuery(dsl as VQueryDSL, { table: 'orders', dialect })
+
+it('defaults to parameterized MySQL, quotes qualified tables, and preserves parameter order', () => {
+  const dsl: VQueryDSL = {
+    select: [{ field: 'sales', alias: 'total', aggr: { func: 'sum' } }],
+    where: { op: 'and', conditions: [{ field: 'province', op: '=', value: "x\\' OR 1=1; -- ? $1" }] },
+    groupBy: ['province'],
+    having: { op: 'and', conditions: [{ field: 'sales', aggr: { func: 'sum' }, op: '>', value: 10 }] },
+    orderBy: [{ field: 'total', order: 'desc' }],
+    limit: 8,
+  }
+  const result = compileQuery(dsl, { table: { database: 'local_data', name: 'supermarket' } })
+  expect(result.sql).toBe(
+    'select sum(`sales`) as `total` from `local_data`.`supermarket` where (`province` = convert(? using utf8mb4) collate utf8mb4_0900_bin) group by `province` having (sum(`sales`) > ?) order by `total` is null asc, `total` desc limit 8',
+  )
+  expect(result.parameters).toEqual(["x\\' OR 1=1; -- ? $1", 10])
+  expect(result.sql).not.toContain('OR 1=1')
+  expect(compile({ select: ['id'], limit: 0 })).toEqual({ sql: 'select `id` from `orders` limit 0', parameters: [] })
+  expect(() => compileQuery(dsl, { table: 'orders', dialect: 'postgres' as never })).toThrow(
+    'Unsupported query dialect',
+  )
+})
+
+it('uses distinct date/aggregate expressions for each backend in SELECT and HAVING', () => {
+  for (const func of ['to_year', 'to_quarter', 'to_month', 'to_week', 'to_day', 'to_hour', 'to_minute', 'to_second']) {
+    const dsl = { select: [{ field: 'date', alias: 'bucket', aggr: { func } }], groupBy: ['bucket'] }
+    expect(compile(dsl).sql).toContain(func === 'to_quarter' ? 'quarter(' : 'date_format(')
+    expect(compile(dsl, 'duckdb').sql).toContain(func === 'to_quarter' ? 'date_part(' : 'strftime(')
+  }
+  expect(compile({ select: [{ field: 'date', aggr: { func: 'to_minute' } }] }).parameters).toEqual(['%Y-%m-%d %H:%i'])
+  for (const func of ['count', 'count_distinct', 'variance', 'variance_pop', 'stddev', 'sum', 'avg', 'min', 'max']) {
+    const dsl = {
+      select: [{ field: 'sales', aggr: { func } }],
+      having: { field: 'sales', aggr: { func }, op: '>', value: 1 },
+    }
+    const mysql = compile(dsl)
+    expect(mysql.sql).not.toContain('AS INTEGER')
+    expect(mysql.parameters).toEqual([1])
+    expect(compile(dsl, 'duckdb').parameters).toEqual([1])
+  }
+  expect(compile({ select: [{ field: 'sales', aggr: { func: 'stddev' } }] }).sql).toContain('stddev_samp(')
+  for (const func of ['median', 'quantile']) {
+    expect(compile({ select: [{ field: 'sales', aggr: { func } }] }).sql).toContain('row_number() over')
+    expect(compile({ select: [{ field: 'sales', aggr: { func } }] }, 'duckdb').sql).toContain(`${func}(`)
+    expect(compile({ select: ['sales'], having: { field: 'sales', aggr: { func }, op: '>', value: 1 } }).sql).toContain(
+      'row_number() over',
+    )
+  }
+  expect(
+    compile({ select: [{ field: 'sales', aggr: { func: 'quantile', quantile: 0.9 } }] }, 'duckdb').parameters,
+  ).toEqual([0.9])
+})
+
+it('adapts insensitive matches and empty boolean groups to both dialects', () => {
+  for (const dialect of ['mysql', 'duckdb'] as const) {
+    for (const op of ['ilike', 'not ilike']) {
+      const result = compile({ select: ['name'], where: { field: 'name', op, value: 'Ab%' } }, dialect)
+      expect(result.sql).toContain(dialect === 'mysql' ? 'lower(convert(' : op)
+      expect(result.parameters).toEqual(['Ab%'])
+    }
+    for (const clause of ['where', 'having']) {
+      for (const op of ['and', 'or']) {
+        expect(compile({ select: ['id'], [clause]: { op, conditions: [] } }, dialect).sql).toContain(
+          op === 'and' ? '1=1' : '0=1',
+        )
+      }
+    }
+  }
+})
+
+it('renders explicit dialect SQL without replacing placeholders inside identifiers or bound values', () => {
+  const dsl: VQueryDSL = {
+    select: [{ field: 'name', alias: '? $1' }],
+    where: {
+      op: 'and',
+      conditions: [
+        { field: 'name', op: '=', value: "O'Reilly ? $1\\path" },
+        { field: 'active', op: '=', value: true },
+      ],
+    },
+    limit: 2,
+  }
+  expect(convertDSLToMysql(dsl, 'orders')).toBe(
+    "select `name` as `? $1` from `orders` where (`name` = convert('O''Reilly ? $1\\\\path' using utf8mb4) collate utf8mb4_0900_bin and `active` = TRUE) limit 2",
+  )
+  expect(convertDSLToDuckDB(dsl, 'orders')).toBe(
+    'select "name" as "? $1" from "orders" where ("name" = \'O\'\'Reilly ? $1\\path\' and "active" = TRUE) limit 2',
+  )
+})
+
+it('rejects malformed JSON before producing SQL', () => {
+  for (const dsl of invalid) expect(() => compile(dsl)).toThrow()
+  expect(compile({ select: ['id'], where: { field: 'id', op: 'in', value: null } }).parameters).toEqual([null])
+  expect(compile({ select: ['id'], where: { field: 'id', op: 'in', value: true } }).parameters).toEqual([true])
+})
+
+it('retains DuckDB date, median and quantile behavior for in-memory datasets', async () => {
+  const vquery = new VQuery()
+  await vquery.createDataset(
+    'orders',
+    [
+      { name: 'date', type: 'string' },
+      { name: 'sales', type: 'number' },
+    ],
+    {
+      type: 'json',
+      rawDataset: [
+        { date: '2026-10-13 12:34:56', sales: 3 },
+        { date: '2026-10-13 15:00:00', sales: 9 },
+      ],
+    },
+  )
+  const dataset = await vquery.connectDataset('orders')
+  const result = await dataset.query({
+    select: [
+      { field: 'date', alias: 'day', aggr: { func: 'to_day' } },
+      { field: 'sales', alias: 'median', aggr: { func: 'median' } },
+      { field: 'sales', alias: 'quantile', aggr: { func: 'quantile' } },
+    ],
+    groupBy: ['day'],
+  })
+  expect(result.dataset).toEqual([{ day: '2026-10-13', median: 6, quantile: 3 }])
+  await vquery.close()
+})
+
+it('preserves VBI comparison aliases and HAVING lists', () => {
+  for (const op of ['gt', 'gte', 'lt', 'lte', 'eq', 'neq']) {
+    expect(
+      compile({
+        select: ['sales'],
+        where: { field: 'sales', op, value: 1 },
+        having: { field: 'sales', aggr: { func: 'sum' }, op, value: 2 },
+      }).parameters,
+    ).toEqual([1, 2])
+  }
+  for (const op of ['in', 'not in']) {
+    expect(
+      compile({ select: ['sales'], having: { field: 'sales', aggr: { func: 'sum' }, op, value: [1, 2] } }).parameters,
+    ).toEqual([1, 2])
+  }
+  expect(() =>
+    compile({ select: ['id'], having: { field: 'id', aggr: { func: 'count' }, op: 'ilike', value: '%' } }),
+  ).toThrow('Unsupported filter operator')
+})
diff --git a/packages/vquery/tests/unit/sql-builder/dialectParity.test.ts b/packages/vquery/tests/unit/sql-builder/dialectParity.test.ts
new file mode 100644
index 0000000000..70edfaf003
--- /dev/null
+++ b/packages/vquery/tests/unit/sql-builder/dialectParity.test.ts
@@ -0,0 +1,176 @@
+import { compileQuery, convertDSLToMysql, convertDSLToDuckDB } from '@visactor/vquery'
+import type { VQueryDSL, DatasetColumn } from '@visactor/vquery'
+
+const columns: DatasetColumn[] = [
+  { name: 'category', type: 'string' },
+  { name: 'value', type: 'number' },
+  { name: 'date', type: 'datetime' },
+]
+const compile = (dsl: unknown, dialect: 'mysql' | 'duckdb' = 'mysql', schema = columns) =>
+  compileQuery(dsl as VQueryDSL, { table: 'orders', dialect, columns: schema })
+
+it('normalizes source text collations without coercing numeric or temporal columns', () => {
+  const dsl = {
+    select: ['category', { field: 'value', alias: 'total', aggr: { func: 'sum' } }],
+    groupBy: ['category'],
+    orderBy: [{ field: 'total', order: 'desc' }],
+  }
+  expect(compile(dsl).sql).toContain('convert(`category` using utf8mb4) collate utf8mb4_0900_bin as `category`')
+  expect(compile(dsl).sql).toContain('`value` as `value`')
+  expect(compile(dsl, 'duckdb').sql).toContain('"category" collate "binary" as "category"')
+  expect(convertDSLToMysql(dsl as VQueryDSL, 'orders', { columns })).toContain('utf8mb4_0900_bin')
+  expect(convertDSLToDuckDB(dsl as VQueryDSL, 'orders', { columns })).toContain('collate "binary"')
+})
+
+it('ranks each statistical field within its logical groups and preserves WHERE/HAVING/limit', () => {
+  const dsl = {
+    select: [
+      'category',
+      { field: 'value', alias: 'median', aggr: { func: 'median' } },
+      { field: 'value', alias: 'q', aggr: { func: 'quantile', quantile: -0.5 } },
+      { field: 'value', alias: 'sum', aggr: { func: 'sum' } },
+    ],
+    where: { op: 'and', conditions: [{ field: 'value', op: '>=', value: 1 }] },
+    groupBy: ['category'],
+    having: { op: 'and', conditions: [{ field: 'value', aggr: { func: 'quantile' }, op: '>', value: 2 }] },
+    orderBy: [{ field: 'median', order: 'desc' }],
+    limit: 2,
+  }
+  const result = compile(dsl)
+  expect(result.sql).toContain('row_number() over (partition by `category` order by `value` is null, `value`)')
+  expect(result.sql.match(/row_number\(\)/g)).toHaveLength(1)
+  expect(result.sql).toContain('avg(case when')
+  expect(result.sql).toContain('max(case when')
+  expect(result.sql).toContain('having (max(case when')
+  expect(result.sql).toContain('limit 2')
+  expect(result.parameters).toEqual([0.5, 1, 0.5, 2])
+})
+
+it('handles unprojected grouping/sort keys and nested HAVING-only statistics', () => {
+  const result = compile({
+    select: [{ field: 'value', alias: 'result', aggr: { func: 'median' } }],
+    groupBy: ['category'],
+    orderBy: [{ field: 'category' }],
+    having: {
+      op: 'or',
+      conditions: [{ op: 'and', conditions: [{ field: 'value', aggr: { func: 'median' }, op: '>', value: 1 }] }],
+    },
+  })
+  expect(result.sql).toContain('partition by `category`')
+  expect(result.sql).toContain('having ((avg(case when')
+  const date = compile({
+    select: [
+      { field: 'date', alias: 'day', aggr: { func: 'to_day' } },
+      { field: 'value', aggr: { func: 'median' } },
+    ],
+    groupBy: ['day'],
+  })
+  expect(date.sql).toContain('partition by date_format(`date`, ?)')
+  const collision = compile({
+    select: [
+      { field: 'value', alias: 'category', aggr: { func: 'sum' } },
+      { field: 'value', aggr: { func: 'median' } },
+    ],
+    groupBy: ['category'],
+  })
+  expect(collision.sql).toContain('partition by `category`')
+  expect(() =>
+    compile({ select: [{ field: 'value', alias: 'median', aggr: { func: 'median' } }], groupBy: ['median'] }),
+  ).toThrow('Statistical aggregates cannot be used as grouping keys')
+})
+
+it('avoids internal name collisions and supports every quantile direction/boundary', () => {
+  for (const quantile of [-1, -0.5, 0, 0.5, 1]) {
+    const result = compile({
+      select: [{ field: 'value', alias: '__vquery_stat_0', aggr: { func: 'quantile', quantile } }],
+    })
+    expect(result.sql).toContain('as `__vquery_stat_1`')
+    expect(result.parameters).toEqual([Math.abs(quantile)])
+  }
+  expect(compile({ select: [{ field: 'value', aggr: { func: 'quantile' } }] }).parameters).toEqual([0.5])
+})
+
+it('validates portable numeric aggregate types identically for both engines', () => {
+  for (const dialect of ['mysql', 'duckdb'] as const) {
+    for (const func of ['sum', 'avg', 'variance', 'variance_pop', 'stddev', 'median', 'quantile']) {
+      expect(() => compile({ select: [{ field: 'category', aggr: { func } }] }, dialect)).toThrow(
+        'requires a number column',
+      )
+      expect(() =>
+        compile({ select: ['category'], having: { field: 'category', aggr: { func }, op: '>', value: 1 } }, dialect),
+      ).toThrow('requires a number column')
+    }
+    expect(() => compile({ select: ['value'], orderBy: [{ field: 'value', nulls: 'random' }] }, dialect)).toThrow(
+      'Invalid orderBy',
+    )
+    for (const order of ['asc', 'desc'])
+      for (const nulls of ['first', 'last']) {
+        const result = compile({ select: ['value'], orderBy: [{ field: 'value', order, nulls }] }, dialect)
+        expect(result.sql).toContain(
+          dialect === 'mysql' ? `is null ${nulls === 'last' ? 'asc' : 'desc'}` : `nulls ${nulls}`,
+        )
+      }
+  }
+})
+
+it('quotes exact string values and fixes LIKE escape semantics in both dialects', () => {
+  for (const dialect of ['mysql', 'duckdb'] as const) {
+    for (const op of ['like', 'not like', 'ilike', 'not ilike']) {
+      const result = compile({ select: ['category'], where: { field: 'category', op, value: 'a\\_b' } }, dialect)
+      expect(result.sql).toContain("escape ''")
+      expect(result.parameters).toEqual(['a\\_b'])
+      for (const value of [1, false])
+        expect(() => compile({ select: ['category'], where: { field: 'category', op, value } }, dialect)).toThrow(
+          'LIKE requires a string pattern',
+        )
+    }
+    expect(
+      compile({ select: ['category'], where: { field: 'category', op: 'like', value: null } }, dialect).parameters,
+    ).toEqual([null])
+    expect(() => compile({ select: ['value'], where: { field: 'value', op: 'like', value: '%' } }, dialect)).toThrow(
+      'LIKE requires a string column',
+    )
+    const date = new Date('2026-10-13T01:02:03.004Z')
+    expect(compile({ select: ['date'], where: { field: 'date', op: '=', value: date } }, dialect).parameters).toEqual([
+      '2026-10-13 01:02:03.004',
+    ])
+  }
+  expect(compile({ select: [{ field: 'value', aggr: { func: 'count' } }] }, 'duckdb').sql).not.toContain('AS INTEGER')
+})
+
+it('rejects mixed comparison types before native implicit conversions diverge', () => {
+  for (const dialect of ['mysql', 'duckdb'] as const) {
+    for (const [field, value] of [
+      ['value', '2'],
+      ['category', 2],
+      ['date', 2],
+    ]) {
+      expect(() => compile({ select: [field], where: { field, op: '=', value } }, dialect)).toThrow(
+        'Filter value does not match',
+      )
+    }
+    expect(
+      compile({ select: ['value'], where: { field: 'value', op: 'in', value: [1, null] } }, dialect).parameters,
+    ).toEqual([1, null])
+    expect(
+      compile(
+        { select: ['category'], having: { field: 'category', aggr: { func: 'count' }, op: '>', value: 1 } },
+        dialect,
+      ).parameters,
+    ).toEqual([1])
+    expect(
+      compile(
+        { select: ['category'], having: { field: 'category', aggr: { func: 'min' }, op: '=', value: 'A' } },
+        dialect,
+      ).parameters,
+    ).toEqual(['A'])
+    expect(
+      compile({ select: ['category'], having: { field: 'category', aggr: { func: 'max' }, op: 'is null' } }, dialect)
+        .parameters,
+    ).toEqual([])
+    expect(
+      compile({ select: ['date'], where: { field: 'date', op: '>=', value: '2026-01-01' } }, dialect).parameters,
+    ).toEqual(['2026-01-01'])
+    expect(compile({ select: ['date'], where: { field: 'date', op: 'is null' } }, dialect).parameters).toEqual([])
+  }
+})
diff --git a/packages/vquery/tests/unit/sql-builder/index-export.test.ts b/packages/vquery/tests/unit/sql-builder/index-export.test.ts
index b6280a013f..0e3522556d 100644
--- a/packages/vquery/tests/unit/sql-builder/index-export.test.ts
+++ b/packages/vquery/tests/unit/sql-builder/index-export.test.ts
@@ -1,7 +1,7 @@
 import * as convert from '../../../src/sql-builder'
 
 describe('convert index export', () => {
-  it('has convertDSLToSQL function', () => {
-    expect(typeof convert.convertDSLToSQL).toBe('function')
+  it('has convertDSLToDuckDB function', () => {
+    expect(typeof convert.convertDSLToDuckDB).toBe('function')
   })
 })
diff --git a/packages/vquery/tests/unit/sql-builder/utils/dialect.test.ts b/packages/vquery/tests/unit/sql-builder/utils/dialect.test.ts
deleted file mode 100644
index 0bfb2708a1..0000000000
--- a/packages/vquery/tests/unit/sql-builder/utils/dialect.test.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-import { PostgresDialect } from '../../../../src/sql-builder/dialect/postgresDialect'
-import { Kysely } from 'kysely'
-
-describe('kysely dialect coverage', () => {
-  it('createIntrospector executes', async () => {
-    const dialect = new PostgresDialect()
-    const db = new Kysely<{ t: { id: number } }>({ dialect })
-    const introspector = dialect.createIntrospector(db)
-    expect(introspector).toBeDefined()
-  })
-  it('driver/compiler/adapter are creatable', () => {
-    const dialect = new PostgresDialect()
-    const driver = dialect.createDriver()
-    const compiler = dialect.createQueryCompiler()
-    const adapter = dialect.createAdapter()
-    expect(driver).toBeDefined()
-    expect(compiler).toBeDefined()
-    expect(adapter).toBeDefined()
-  })
-})
diff --git a/packages/vquery/tests/unit/sql-builder/utils/escape.test.ts b/packages/vquery/tests/unit/sql-builder/utils/escape.test.ts
index a3c588befa..ffebfb7603 100644
--- a/packages/vquery/tests/unit/sql-builder/utils/escape.test.ts
+++ b/packages/vquery/tests/unit/sql-builder/utils/escape.test.ts
@@ -1,29 +1,16 @@
-import { convertDSLToSQL } from '@visactor/vquery'
+import { convertDSLToDuckDB } from '@visactor/vquery'
 
-describe('escapeValue paths', () => {
-  it('boolean and object values', () => {
-    interface T {
-      id: number
-      flag: boolean
-      meta: Record<string, unknown>
-    }
-
-    const sql = convertDSLToSQL<T, 'orders'>(
-      {
-        select: ['id'],
-        where: {
-          op: 'and',
-          conditions: [
-            { field: 'flag', op: '=', value: true },
-            { field: 'meta', op: '=', value: { a: 1 } },
-          ],
-        },
-      },
+it('renders booleans and rejects object filter values', () => {
+  expect(
+    convertDSLToDuckDB(
+      { select: ['id'], where: { op: 'and', conditions: [{ field: 'flag', op: '=', value: true }] } },
       'orders',
-    )
-
-    expect(sql).toMatchInlineSnapshot(
-      `"select "id" from "orders" where ("flag" = TRUE and "meta" = '[object Object]')"`,
-    )
-  })
+    ),
+  ).toBe('select "id" from "orders" where ("flag" = TRUE)')
+  expect(() =>
+    convertDSLToDuckDB(
+      { select: ['id'], where: { op: 'and', conditions: [{ field: 'meta', op: '=', value: { a: 1 } }] } },
+      'orders',
+    ),
+  ).toThrow('Invalid filter value')
 })
diff --git a/packages/vquery/tests/unit/sql-engine/predicateSemantics.test.ts b/packages/vquery/tests/unit/sql-engine/predicateSemantics.test.ts
new file mode 100644
index 0000000000..3393c3cb18
--- /dev/null
+++ b/packages/vquery/tests/unit/sql-engine/predicateSemantics.test.ts
@@ -0,0 +1,61 @@
+import { executeSQL, parseSQL } from '../../../src/query-engine/sql'
+
+it('preserves null predicates and three-valued boolean logic', () => {
+  const rows = [{ value: null }, { value: 3 }]
+  expect(executeSQL(parseSQL('select * from orders where value is null'), rows)).toEqual([{ value: null }])
+  expect(executeSQL(parseSQL('select * from orders where value is not null'), rows)).toEqual([{ value: 3 }])
+  expect(executeSQL(parseSQL('select * from orders where null = 1 and 1 = 1'), rows)).toEqual([])
+  expect(executeSQL(parseSQL('select * from orders where null = 1 or 1 = 0'), rows)).toEqual([])
+})
+
+it('computes extrema regardless of input ordering', () => {
+  expect(
+    executeSQL(parseSQL('select min(value) as minimum, max(value) as maximum from orders'), [
+      { value: 3 },
+      { value: 9 },
+      { value: 1 },
+    ]),
+  ).toEqual([{ minimum: 1, maximum: 9 }])
+})
+
+it('keeps explicit and default NULL ordering independent of sort direction', () => {
+  const rows = [{ value: null }, { value: 2 }, { value: 1 }]
+  for (const direction of ['asc', 'desc']) {
+    const sorted = direction === 'asc' ? [{ value: 1 }, { value: 2 }] : [{ value: 2 }, { value: 1 }]
+    for (const suffix of ['', ' nulls last'])
+      expect(executeSQL(parseSQL(`select * from orders order by value ${direction}${suffix}`), rows)).toEqual([
+        ...sorted,
+        { value: null },
+      ])
+    expect(executeSQL(parseSQL(`select * from orders order by value ${direction} nulls first`), rows)).toEqual([
+      { value: null },
+      ...sorted,
+    ])
+  }
+  expect(() => parseSQL('select * from orders order by value nulls random')).toThrow('Expected last')
+})
+
+it('uses literal backslashes for DSL LIKE patterns', () => {
+  const rows = [{ label: 'a\\_b' }, { label: 'a_b' }]
+  expect(executeSQL(parseSQL("select * from orders where label like 'a\\_b' escape ''"), rows)).toEqual([
+    { label: 'a\\_b' },
+  ])
+  expect(() => parseSQL("select * from orders where label like 'a%' escape '!'")).toThrow('Only empty LIKE escape')
+  expect(() => parseSQL("select * from orders where label like 'a%' escape label")).toThrow('Only empty LIKE escape')
+})
+
+it('matches DuckDB DOUBLE quantile rounding near integer rank boundaries', () => {
+  const rows = Array.from({ length: 50 }, (_, index) => ({ value: index + 1 }))
+  expect(executeSQL(parseSQL('select quantile(value, 0.14) as q from orders'), rows)).toEqual([{ q: 7 }])
+  expect(executeSQL(parseSQL('select quantile(value, -0.14) as q from orders'), rows)).toEqual([{ q: 44 }])
+  expect(executeSQL(parseSQL('select quantile(value, 0.140000000000001) as q from orders'), rows)).toEqual([{ q: 8 }])
+  expect(
+    executeSQL(parseSQL('select * from orders order by value, id'), [
+      { id: 2, value: undefined },
+      { id: 1, value: null },
+    ]),
+  ).toEqual([
+    { id: 1, value: null },
+    { id: 2, value: undefined },
+  ])
+})
diff --git a/packages/vquery/tests/unit/sql-engine/sqlLogicTest.test.ts b/packages/vquery/tests/unit/sql-engine/sqlLogicTest.test.ts
index 58ae3bf751..15ed477882 100644
--- a/packages/vquery/tests/unit/sql-engine/sqlLogicTest.test.ts
+++ b/packages/vquery/tests/unit/sql-engine/sqlLogicTest.test.ts
@@ -171,7 +171,7 @@ class SQLLogicTestDatabase {
     if (quantileTable) {
       const [, name, rangeColumn, randomAlias, countText, values] = quantileTable
       const randomColumn = randomAlias ?? 'random()'
-      const rows = Array.from({ length: Number(countText) }, (_, value) => ({
+      const rows: DataRow[] = Array.from({ length: Number(countText) }, (_, value) => ({
         [rangeColumn]: value,
         [randomColumn]: Math.random(),
       }))
diff --git a/packages/vquery/tests/unit/vquery/storage.test.ts b/packages/vquery/tests/unit/vquery/storage.test.ts
new file mode 100644
index 0000000000..d32ebbc405
--- /dev/null
+++ b/packages/vquery/tests/unit/vquery/storage.test.ts
@@ -0,0 +1,101 @@
+import { VQuery as BrowserVQuery, type VQueryOptions } from '../../../src/browser'
+import { VQuery as NodeVQuery } from '../../../src/node'
+
+const originalIndexedDB = Object.getOwnPropertyDescriptor(globalThis, 'indexedDB')
+
+beforeEach(() => {
+  Object.defineProperty(globalThis, 'indexedDB', {
+    configurable: true,
+    get: () => {
+      throw new Error('IndexedDB unavailable')
+    },
+  })
+})
+
+afterEach(() => {
+  rstest.restoreAllMocks()
+  if (originalIndexedDB) Object.defineProperty(globalThis, 'indexedDB', originalIndexedDB)
+  else Reflect.deleteProperty(globalThis, 'indexedDB')
+})
+
+for (const [entrypoint, VQuery] of [
+  ['browser', BrowserVQuery],
+  ['node', NodeVQuery],
+] as const) {
+  describe(`${entrypoint} dataset storage`, () => {
+    const defaults: Array<[string, VQueryOptions | undefined]> = [
+      ['no options', undefined],
+      ['empty options', {}],
+      ['explicitly disabled', { enableIndexedDB: false }],
+    ]
+
+    for (const [name, options] of defaults) {
+      it(`queries isolated memory datasets without IndexedDB: ${name}`, async () => {
+        const vquery = new VQuery(options)
+        const other = new VQuery(options)
+        const columns = [{ name: 'sales', type: 'number' as const }]
+        try {
+          await vquery.createDataset('sales', columns, {
+            type: 'json',
+            rawDataset: [{ sales: 10 }, { sales: 20 }],
+          })
+          const dataset = await vquery.connectDataset('sales')
+          expect(
+            (
+              await dataset.query({
+                select: [{ field: 'sales', alias: 'total', aggr: { func: 'sum' } }],
+              })
+            ).dataset,
+          ).toEqual([{ total: 30 }])
+          expect(await other.listDatasets()).toEqual([])
+
+          await vquery.updateDatasetSource('sales', columns, { type: 'json', rawDataset: [{ sales: 50 }] })
+          const updated = await vquery.connectDataset('sales')
+          expect((await updated.query({ select: ['sales'] })).dataset).toEqual([{ sales: 50 }])
+          await vquery.dropDataset('sales')
+          expect(await vquery.hasDataset('sales')).toBe(false)
+        } finally {
+          await vquery.close()
+          await other.close()
+        }
+      })
+    }
+
+    it('opens IndexedDB lazily when explicitly enabled and closes it', async () => {
+      const close = rstest.fn()
+      const getAll = rstest.fn(() => {
+        const request = { result: [], onsuccess: undefined as (() => void) | undefined }
+        queueMicrotask(() => request.onsuccess?.())
+        return request
+      })
+      const transaction = rstest.fn(() => ({ objectStore: () => ({ getAll }) }))
+      const open = rstest.fn(() => {
+        const request = {
+          result: { close, transaction },
+          onsuccess: undefined as ((event: unknown) => void) | undefined,
+        }
+        queueMicrotask(() => request.onsuccess?.({ target: request }))
+        return request
+      })
+      Object.defineProperty(globalThis, 'indexedDB', { configurable: true, value: { open } })
+      const vquery = new VQuery({ enableIndexedDB: true })
+      expect(open).not.toHaveBeenCalled()
+      try {
+        expect(await vquery.listDatasets()).toEqual([])
+        expect(await vquery.listDatasets()).toEqual([])
+        expect(open).toHaveBeenCalledTimes(1)
+        expect(open).toHaveBeenCalledWith('vquery', 2)
+        expect(transaction).toHaveBeenCalledWith(['vqueryDatasets'], 'readonly')
+        expect(getAll).toHaveBeenCalledTimes(2)
+      } finally {
+        await vquery.close()
+      }
+      expect(close).toHaveBeenCalledTimes(1)
+    })
+
+    it('reports unavailable IndexedDB when explicitly enabled', async () => {
+      const vquery = new VQuery({ enableIndexedDB: true })
+      await expect(vquery.listDatasets()).rejects.toThrow('IndexedDB unavailable')
+    })
+  })
+}