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Make the FTS candidate query single-table with a search projection - #292
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Hybrid search takes seconds for any term matching a large share of the corpus: 6.5 s for a term matching all 5M reports on the benchmark rig. The cost is not ts_rank itself but the joins around it -- filters are built as report__ traversals, so the candidate query joins three tables and applies SELECT DISTINCT, which drags the tsvector through a spilling hash join and blocks the top-N heapsort. The spec completes ReportSearchIndex as a search projection so the scan is single-table, maintained by statement-level triggers rather than application signals because group membership is access-control data. Measured 13,440 ms -> ~420 ms at 5M reports. Records what was ruled out with measurements, including BM25 via pg_textsearch (33x slower as written) and a stripped ranking tsvector (collapses ts_rank to one distinct value). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
The spec suggested sites "may want VACUUM FULL afterwards" without noting that it rebuilds every index on the table, including the HNSW vector index. On a deployment with embeddings populated that is an index rebuild over millions of vectors under an ACCESS EXCLUSIVE lock. Also adds the VACUUM ANALYZE that the migration must run -- ten new columns and three new indexes carry no statistics, and the design depends on the planner choosing a parallel sequential scan -- and records why indexes are created last: unindexed columns let the backfill use HOT updates, which is the main defence against bloat. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Section 4.5 said the settings were "driven by env vars" without naming them, which is not implementable. Adds the four variable names, their defaults, the compose snippet they feed, and where they get documented. Also corrects a claim that max_parallel_workers and max_worker_processes must be raised alongside per_gather: at per_gather=4 the stock cap of 8 is not a throttle, it only binds under concurrency. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
The four tuning variables are compose-level overrides with defaults, not example.env entries. This matches how the project already separates the two kinds: deployer choices (ports, EXAMPLE_REPORTS_LANGUAGE) live in example.env, while tuning knobs with sensible defaults are compose-only (EMBEDDINGS_WORKER_CONCURRENCY, WAIT_POSTGRES_TIMEOUT, RADIS_IMAGE). Operator guidance goes in docs/user-docs/admin-guide.md. Also drops the raised shared_buffers default back to PostgreSQL's 128MB. No measured gain was attributable to it, and 512MB of shared memory on a small container fails to start or thrashes -- the same reasoning that kept work_mem unchanged. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Django's __date lookup compiles to (col AT TIME ZONE 'Europe/Berlin')::date, which no plain btree can serve and which is evaluated per row -- five million timezone conversions per query on the sequential scan that dominates our plans. Converts the two date filters to half-open ranges with the boundaries computed in Python. Reworks the index set based on which callers actually populate which filters. Drops the modality_codes GIN (a quarter of the corpus, never selective enough to beat a scan) and adds a btree on report_updated_at, which subscriptions filter on with no tsquery to drive the query. Records that patient_id, created_after and created_before have no producer at all. Also corrects a factual error: array operators do not require a GIN index to be usable, only to be served by an index scan. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Fixes found by re-reading it end to end and verifying its claims: - The statement-level trigger justification cited bulk_upsert_report_search_indexes, which writes to a different table and never fires these triggers. The real bulk writer is the bulk-upsert API endpoint (reports/api/viewsets.py:215,229). The matching test in section 5 described a groups.set() "spanning many reports", which is not a thing a related manager can do. - Section 4.4 still claimed three new indexes after 4.1 was cut to two. - Verified the BEFORE-trigger trap rather than asserting it: a BEFORE trigger reads NULL from a stored generated column whose value was 42. - Verified the production pool size on the new shape: LIMIT 10000 runs in 401 ms against 421 ms for LIMIT 25, so HYBRID_FTS_MAX_RESULTS staying at 10,000 is now measured on both query shapes rather than only the old one. - Only the modalities join can duplicate rows; the groups join cannot, since the filter is a single value against a unique_together table. - Records two edges the triggers do not cover: Report deletion cascades and Language.code renames. - Normalises headline figures to the configured 4-worker default rather than the 8-worker best case, and softens an overstatement about the CTE alternative being the only way to avoid duplication. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Split the 5M benchmark corpus 94.9/5/0.1 across three groups and compared the planner's choice against the same query with index scans forced off. The index gives ~170x at 0.1% selectivity and ~2.9x at 5%, and makes no difference at 94.9% -- where the planner abandons it unprompted, so it cannot make the degenerate single-group install worse. It costs 6 MB and 1.2 s to build. Also labels the report_updated_at btree as reasoned rather than measured, with a note to confirm it during implementation using the same technique. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Grew the rig to 8,001,000 reports, added the real projection columns, ran the actual chunked backfill and measured before and after. Three things the spec had wrong: - The headline overstated the gain. "13,440 ms -> ~420 ms" compared the old shape at two workers against the new shape at four to eight, so part of that ratio was parallelism and part was a smaller corpus. The controlled comparison at 8M -- same table, same four workers -- is 8,388 ms -> 882 ms, about 9.5x. - The backfill estimate of "2-5 minutes at 5M" was low. Measured 9 min 20 s at 8M, extrapolating to ~18 min at 15M. Ten minutes of announced downtime has been accepted, which is what keeps the blocking approach. - Chunking and index-last do not prevent the heap doubling. Measured 10 GB -> 21 GB, and a plain VACUUM clears all dead tuples while leaving the file at 21 GB for 11 GB of live data. The 882 ms already includes that penalty. Also states 8 million as the explicit design target and records the benchmark host relative to production. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Two timings in sections 4.3 and 6 were 5M-rig numbers sitting next to 8M-rig ones with nothing to tell them apart, now that the spec has an explicit 8M design target. Also tidies a doubled parenthetical. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Ten TDD tasks from the approved spec: projection columns, statement-level triggers, creation-path population, chunked backfill, indexes, drift detection, the single-table filter query, language predicates, a plan-shape regression test, and Postgres tuning. Splits the spec's single migration into four (0003-0006), one per ordered step, so no task has to edit a migration a previous task already applied. Order is load-bearing and unchanged: columns, triggers, backfill, indexes. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Implement the first stage of query-shape optimization by adding projection columns (group_ids, modality_codes, language_code, patient_sex, patient_age, patient_id, study_datetime, study_description, report_created_at, report_updated_at) that mirror Report fields used by search filters. These columns allow the FTS candidate query to remain single-table rather than joining three tables with DISTINCT. Columns default to empty/null, keeping all AddField operations metadata-only on the 8M-row production table; later tasks add triggers (0004) to maintain them and backfill tasks to populate them. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Add AFTER, statement-level triggers that mirror group_ids, modality_codes, and the scalar Report fields into ReportSearchIndex on every write - management commands, the admin, and raw SQL included, not just ORM saves. Triggers must be AFTER (not BEFORE) so patient_age, a stored generated column, is visible to them. Fix a latent bug in the pre-existing post_save signal that the new triggers exposed: OneToOneField reciprocally caches the search index on the Report instance at creation, so a later report.save() reused that stale ReportSearchIndex object and its unqualified .save() call wrote all of its (stale) columns back, clobbering the projection fields the trigger had just set in the same statement. Scoping that save to update_fields=["search_vector"] - the only thing this signal is meant to refresh - fixes it.
test_new_index_row_defaults_to_empty_arrays' modalities=[] edit removed the only assertion that ever touched modality_codes, leaving the INSERT and DELETE triggers on reports_report_modalities with no test proving they run. Add test_adding_a_modality_updates_the_projection and test_removing_a_modality_updates_the_projection, mirroring the existing group_ids trigger tests.
Task 2 discovered that OneToOneField reverse-caching makes a bare instance.search_index.save() write a stale in-memory copy back over the values the AFTER UPDATE trigger just set, and narrowed it to update_fields=["search_vector"]. Task 3's snippet still carried the bare save and would have silently reverted that fix. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
ReportFactory.create() masked the gap this test was meant to pin: its post_generation hooks make factory_boy issue an implicit extra save(), which fires the AFTER UPDATE trigger from migration 0004 and populates the scalars regardless of whether the signal's sync_projection() call exists. Building the report and saving it explicitly is a single INSERT with no follow-up UPDATE, so only sync_projection() can fill the scalars.
Adds the two measured indexes to ReportSearchIndex: a GIN index on group_ids (up to ~170x faster access-control filtering on small groups) and a btree on report_updated_at (serves the subscription filter() path, which has no tsquery to drive it). Created after the backfill so the backfill could rely on HOT updates. Also flips fastupdate off on the search_vector GIN index and drains its pending list, since RADIS writes in async batches and reads interactively. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
The search projection duplicates access-control data (group_ids) and other Report/Language fields the FTS scan filters on. Triggers keep it in sync, but a Language.code rename and any writer that bypasses the triggers can still drift it from its sources. This command compares ReportSearchIndex against Report/Language/group/modality membership and exits non-zero with a per-column drift count when they disagree, so operators can verify integrity after a restore, bulk import, or migration. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
_build_filter_query traversed report__ joins for every predicate, forcing the candidate query through Report/Language/Modality even for simple filters -- the join that made common-term searches take seconds. It now reads the search projection columns on ReportSearchIndex directly: group membership becomes an array containment check (empty array when filters.group is None, staying fail-closed), date filters become sargable half-open ranges instead of a per-row timezone-converting __date lookup, and the modalities filter becomes an array overlap. With no joins left, the modalities predicate can no longer duplicate a row, so .distinct() is dropped from both the FTS and vector querysets in _fuse_hybrid. test_filter_query_equivalence.py keeps the old joined implementation as an oracle and asserts the new query selects the same report ids across a filter matrix, plus dedicated cases for the fail-closed group=None behavior and the removed .distinct().
Two gaps the review found in the oracle test from the previous commit: - The date parametrisation used only "N days ago" filters, which never land on or near a report's exact date. An off-by-one in the new half-open range, a wrong inclusive/exclusive boundary, or a UTC-vs-local mistake inside _local_day_start were all invisible to it. Add reports pinned to explicit local midnight, 23:30 local, and midnight of the following day on a fixed day, plus a second run of the same check under override_settings(TIME_ZONE="America/New_York") so local midnight isn't incidentally equal to UTC midnight. - The legacy oracle was missing created_after, created_before and labels -- three predicates the pre-change implementation had. labels is the one whose lookup shape actually changed (report__in -> report_id__in) and had zero equivalence coverage. Restore all three to the oracle and add parametrised cases for each, including a corpus report with a surfacing LabelResult. Verified the new boundary tests have teeth: temporarily reverting the upper-bound fix to the old __lte-on-day-start form made both boundary tests fail (dropping the 23:30 report), then reverting back turned them green again.
Five report__language__code__in traversals in providers.py each re-added the reports_report and reports_language joins to the candidate query, undoing the single-table projection from the filter-query rewrite. Read ReportSearchIndex.language_code directly instead.
Task 9's review demonstrated empirically that asserting on the absence of a Unique node does not detect a reintroduced .distinct(). Postgres implements SELECT DISTINCT as HashAggregate rather than Sort+Unique once the matching row count is non-trivial -- measured at 3, 1,000 and 100,000 rows with the same GIN array-containment pattern this design uses. The assertion therefore passes with the regression present at exactly the 8M scale it exists to protect. Django emits the DISTINCT keyword unconditionally whenever .distinct() is called, regardless of the plan the planner later chooses, so checking the compiled SQL text is structural and scale-independent. The join half of the assertion was verified sound and is unchanged. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
EXPLAIN can report a distinct step as either a Sort+Unique pair or a HashAggregate depending on the planner's cost estimate, and at realistic corpus sizes it picks HashAggregate -- so asserting "Unique" not in the plan only caught a reintroduced .distinct() on this test's tiny fixture, not at the 8-million-row scale the guard exists for. Check the compiled SQL text for the DISTINCT keyword instead: Django emits it unconditionally whenever .distinct() is called, independent of corpus size or planner choice.
Raise max_parallel_workers_per_gather to 4 (from PostgreSQL's default of 2) so the report-index scan behind full-text search uses more workers. The other three related GUCs are exposed as compose-only overrides at their existing PostgreSQL defaults, so an operator raising per_gather on a bigger host can raise them coherently.
Every service that waits for `init` (prod) or `web` (dev) waited with a fixed
`wait-for-it -t 300`. The search projection backfill takes 9 min 20 s at 8M
reports, so the wait expired mid-migration, the `&&` chain aborted and the
containers exited -- fatally in dev, which has no restart policy, and after
three attempts in prod at the 15M extrapolation.
The timeout is now `${WAIT_INIT_TIMEOUT:-3600}` at every such site. It is a
tuning knob with a sensible default, so it stays a compose-only override and
does not go into example.env.
The admin guide's upgrade procedure and the design spec now say that this
particular upgrade runs a long migration and that the stack is unavailable for
roughly ten minutes at 8M reports.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
Spec 4.4 step 1 called `'{}'` "the fail-closed value". It is fail-closed for
`group=<id>`, which compiles to `group_ids @> ARRAY[id]` and matches nothing,
but fail-*open* for `group=None`, which compiles to the exact match
`group_ids = '{}'` and therefore matches the whole archive between migrations
0003 and 0005. That path is reachable: extractions/views.py passes `group=None`
for a logged-in user with no active group. It is contained only accidentally
today, because 0003 also leaves `language_code` NULL corpus-wide and the FTS
half then returns nothing -- the vector half carries no language predicate, so
on a deployment with embeddings the leak is real.
The spec now states both directions, and the admin guide says the migration
must be run with the web tier stopped.
Also corrects the parallelism measurements in the admin guide: 606/421/343 ms
came from the 5M rig (spec 4.5), not from 8M.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
The backfill test used one report against CHUNK_SIZE = 50,000, so the loop ran exactly once and an off-by-one in its `low`/`high` arithmetic -- the kind that silently skips whole id ranges -- would not have shown. It now monkeypatches CHUNK_SIZE to 1 with three reports (given explicit ids 1..3, because the loop always starts at 0) and asserts one chunk statement per report as well as the resulting values. The group triggers only had multi-row coverage for INSERT. reports/api/ viewsets.py:217 clears a whole batch's memberships with a single DELETE, and removal is the leak direction, so the OLD transition table now gets the same many-rows-at-once test. Each report keeps a second group, so the assertion pins a recomputed aggregate rather than a blanket reset. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HqE6h8sRWQT57B9Lomfvbk
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In `@docker-compose.base.yml`:
- Line 64: Update the default for POSTGRES_SHM_SIZE_BYTES in the shared-memory
size setting to 4 GiB so it meets or exceeds the default HNSW maintenance
budget. Preserve the environment-variable override.
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AGENTS.mddocker-compose.base.ymldocs/superpowers/specs/2026-08-29-fts-query-shape-performance-design.mddocs/user-docs/admin-guide.mdradis/pgsearch/admin.pyradis/pgsearch/migrations/0005_search_projection_backfill.pyradis/pgsearch/migrations/0006_search_projection_indexes.pyradis/pgsearch/migrations/0007_code_rename_triggers.pyradis/pgsearch/tests/test_admin.pyradis/pgsearch/tests/test_check_search_projection.pyradis/pgsearch/tests/test_search_projection.pyradis/settings/base.py
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| size: ${POSTGRES_SHM_SIZE_BYTES:-1073741824} |
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🩺 Stability & Availability | 🟠 Major | ⚡ Quick win
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🏁 Script executed:
sed -n '1,80p' radis/pgsearch/migrations/0006_search_projection_indexes.py
sed -n '30,75p' docker-compose.base.yml
grep -n 'PGSEARCH_HNSW\|SHM' AGENTS.md docs/user-docs/admin-guide.md radis/settings/base.py
grep -rn 'pgvector\|image:' docker-compose*.yml | head -20Repository: openradx/radis
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set -eu
printf '%s\n' '--- repository guidance ---'
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printf '%s\n' '--- changed compose and settings ---'
sed -n '35,72p' docker-compose.base.yml
sed -n '535,550p' radis/settings/base.py
printf '%s\n' '--- relevant local references ---'
rg -n -C 3 'esthnswarea|maintenance_work_mem|fallocate|posix.*dsm|dynamic shared memory|/dev/shm' . --glob '!*.lock' --glob '!*.json' || true
printf '%s\n' '--- image manifest metadata ---'
curl -fsSL -H 'Accept: application/vnd.docker.distribution.manifest.v2+json' 'https://registry.hub.docker.com/v2/pgvector/pgvector/manifests/pg17' | python3 -c 'import json,sys; d=json.load(sys.stdin); print(json.dumps({k:d.get(k) for k in ("schemaVersion","mediaType","config","manifests")}, indent=2))'
printf '%s\n' '--- pgvector v0.8.6 source ---'
curl -fsSL https://raw.githubusercontent.com/pgvector/pgvector/v0.8.6/src/hnswbuild.c | nl -ba | grep -C 12 -E 'esthnswarea|maintenance_work_mem|dsm_create|dsm_attach'
printf '%s\n' '--- PostgreSQL POSIX DSM source ---'
curl -fsSL https://raw.githubusercontent.com/postgres/postgres/REL17_STABLE/src/backend/storage/dsm/dsm_impl.c | nl -ba | grep -C 15 -E 'fallocate|posix.*create|dsm_impl_posix|shm_open'Repository: openradx/radis
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printf '%s\n' '--- pgvector v0.8.6 source ---'
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| nl -ba | grep -C 14 -E 'esthnswarea|maintenance_work_mem|dsm_create|dsm_attach' || true
printf '%s\n' '--- PostgreSQL REL17_STABLE POSIX DSM source ---'
curl -fsSL https://raw.githubusercontent.com/postgres/postgres/REL17_STABLE/src/backend/storage/dsm/dsm_impl.c \
| nl -ba | grep -C 18 -E 'fallocate|posix.*create|dsm_impl_posix|shm_open' || true
printf '%s\n' '--- pinned image digest lookup ---'
token=$(curl -fsSL 'https://auth.docker.io/token?service=registry.docker.io&scope=repository:pgvector/pgvector:pull' | python3 -c 'import json,sys; print(json.load(sys.stdin)["token"])')
curl -fsSL -H "Authorization: Bearer $token" \
-H 'Accept: application/vnd.docker.distribution.manifest.list.v2+json, application/vnd.oci.image.index.v1+json, application/vnd.docker.distribution.manifest.v2+json' \
https://registry-1.docker.io/v2/pgvector/pgvector/manifests/pg17 \
| python3 - <<'PY'
import json, sys
d=json.load(sys.stdin)
print(json.dumps(d, indent=2))
PYRepository: openradx/radis
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printf '%s\n' '--- PostgreSQL REL17_STABLE POSIX DSM source ---'
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| nl -ba | grep -C 20 -E 'fallocate|posix.*create|dsm_impl_posix|shm_open|dsm_impl_op' || true
printf '%s\n' '--- PostgreSQL REL17_STABLE DSM source path references ---'
curl -fsSL https://api.github.com/repos/postgres/postgres/contents/src/backend/storage/ipc/dsm_impl.c \
| python3 -c 'import json,sys; d=json.load(sys.stdin); print(d.get("download_url",""), d.get("sha",""))' || true
printf '%s\n' '--- pinned image manifest list ---'
token=$(curl -fsSL 'https://auth.docker.io/token?service=registry.docker.io&scope=repository:pgvector/pgvector:pull' | python3 -c 'import json,sys; print(json.load(sys.stdin)["token"])')
curl -fsSL -H "Authorization: Bearer $token" \
-H 'Accept: application/vnd.docker.distribution.manifest.list.v2+json, application/vnd.oci.image.index.v1+json, application/vnd.docker.distribution.manifest.v2+json' \
https://registry-1.docker.io/v2/pgvector/pgvector/manifests/pg17 \
| python3 -c 'import json,sys; d=json.load(sys.stdin); print(json.dumps(d, indent=2))' || trueRepository: openradx/radis
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printf '%s\n' '--- PostgreSQL REL_17_STABLE POSIX DSM source ---'
curl -fsSL https://raw.githubusercontent.com/postgres/postgres/REL_17_STABLE/src/backend/storage/ipc/dsm_impl.c \
| nl -ba | grep -C 20 -E 'fallocate|posix.*create|dsm_impl_posix|shm_open|dsm_impl_op' || true
printf '%s\n' '--- pinned image manifest and config ---'
token=$(curl -fsSL 'https://auth.docker.io/token?service=registry.docker.io&scope=repository:pgvector/pgvector:pull' | python3 -c 'import json,sys; print(json.load(sys.stdin)["token"])')
manifest=$(curl -fsSL -H "Authorization: Bearer $token" \
-H 'Accept: application/vnd.oci.image.manifest.v1+json, application/vnd.docker.distribution.manifest.v2+json' \
https://registry-1.docker.io/v2/pgvector/pgvector/manifests/sha256:cf134a767f474095eeba57e0117be8e568e011a63f33fbf252f14c9b760f8e6f)
printf '%s\n' "$manifest" | python3 -c 'import json,sys; d=json.load(sys.stdin); print(json.dumps(d,indent=2))'
config=$(printf '%s\n' "$manifest" | python3 -c 'import json,sys; print(json.load(sys.stdin)["config"]["digest"])')
printf '%s\n' '--- pinned image config labels ---'
curl -fsSL -H "Authorization: Bearer $token" \
"https://registry-1.docker.io/v2/pgvector/pgvector/blobs/$config" \
| python3 -c 'import json,sys; d=json.load(sys.stdin); print(json.dumps({"config":d.get("config",{}),"container_config":d.get("container_config",{})},indent=2))' || trueRepository: openradx/radis
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set -u
token=$(curl -fsSL 'https://auth.docker.io/token?service=registry.docker.io&scope=repository:pgvector/pgvector:pull' | python3 -c 'import json,sys; print(json.load(sys.stdin)["token"])')
child=sha256:dca0d688bbb31d3f851502ffcb9c7791387b4fcc544ae434dab41761e5ece317
printf '%s\n' '--- pinned amd64 child manifest ---'
child_manifest=$(curl -fsSL -H "Authorization: Bearer $token" \
-H 'Accept: application/vnd.oci.image.manifest.v1+json, application/vnd.docker.distribution.manifest.v2+json' \
"https://registry-1.docker.io/v2/pgvector/pgvector/manifests/$child")
printf '%s\n' "$child_manifest" | python3 -c 'import json,sys; print(json.dumps(json.load(sys.stdin),indent=2))'
config=$(printf '%s\n' "$child_manifest" | python3 -c 'import json,sys; print(json.load(sys.stdin)["config"]["digest"])')
printf '%s\n' '--- pinned amd64 image config ---'
curl -fsSL -H "Authorization: Bearer $token" \
"https://registry-1.docker.io/v2/pgvector/pgvector/blobs/$config" \
| python3 -c 'import json,sys; d=json.load(sys.stdin); print(json.dumps({"config":d.get("config",{}),"container_config":d.get("container_config",{})},indent=2))'Repository: openradx/radis
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#!/bin/bash
set -u
token=$(curl -fsSL 'https://auth.docker.io/token?service=registry.docker.io&scope=repository:pgvector/pgvector:pull' | python3 -c 'import json,sys; print(json.load(sys.stdin)["token"])')
config=sha256:17a06c0a60bf6fb548a8493b8f6057dc830b791bbbe1f1ab706ca4b9d97c8180
printf '%s\n' '--- pinned image config history ---'
curl -fsSL -H "Authorization: Bearer $token" \
"https://registry-1.docker.io/v2/pgvector/pgvector/blobs/$config" \
| python3 -c 'import json,sys; d=json.load(sys.stdin); print(json.dumps({"history":d.get("history"),"rootfs":d.get("rootfs")},indent=2))'Repository: openradx/radis
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set -u
printf '%s\n' '--- embedding/index definitions and feature gates ---'
rg -n -C 4 'embedding|HnswIndex|pgsearch_embedding_hnsw|0005_search_projection_backfill|PGSEARCH_HNSW' \
radis --glob '*.py' --glob '*.sql' || true
printf '%s\n' '--- migration 0005 and model context ---'
sed -n '1,220p' radis/pgsearch/migrations/0005_search_projection_backfill.py 2>/dev/null || true
rg -n -C 8 'class ReportSearchIndex|embedding' radis/pgsearch radis/reports --glob '*.py' || trueRepository: openradx/radis
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set -u
printf '%s\n' '--- pgsearch source files ---'
find radis/pgsearch -maxdepth 2 -type f \( -name 'models.py' -o -name '0005_search_projection_backfill.py' -o -name '0006_search_projection_indexes.py' \) -print
printf '%s\n' '--- model ---'
sed -n '1,220p' radis/pgsearch/models.py
printf '%s\n' '--- migration 0005 ---'
sed -n '1,260p' radis/pgsearch/migrations/0005_search_projection_backfill.py
printf '%s\n' '--- migration 0006 ---'
sed -n '1,100p' radis/pgsearch/migrations/0006_search_projection_indexes.pyRepository: openradx/radis
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curl -fsSL https://raw.githubusercontent.com/pgvector/pgvector/v0.8.6/src/hnswbuild.c \
| nl -ba | grep -C 25 -E 'HnswBeginParallel|parallelWorkers|request > 0|plan_create_index_workers' || trueRepository: openradx/radis
Length of output: 5292
Make the /dev/shm default at least as large as the HNSW maintenance budget.
POSTGRES_SHM_SIZE_BYTES defaults to 1 GiB, while PGSEARCH_HNSW_REBUILD_MAINTENANCE_WORK_MEM defaults to 2 GiB. pgvector v0.8.6 allocates the parallel HNSW graph area from maintenance_work_mem before scanning the table. PostgreSQL 17 preallocates that DSM segment with posix_fallocate.
The failure can occur whenever PostgreSQL plans a parallel HNSW build. It does not require the graph to exceed 1 GiB. A sufficiently large FTS-only ReportSearchIndex table can also reach this path because migration 0006 recreates the HNSW index even when embedding is nullable.
Proposed default change
- size: ${POSTGRES_SHM_SIZE_BYTES:-1073741824}
+ size: ${POSTGRES_SHM_SIZE_BYTES:-4294967296}📝 Committable suggestion
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| size: ${POSTGRES_SHM_SIZE_BYTES:-1073741824} | |
| size: ${POSTGRES_SHM_SIZE_BYTES:-4294967296} |
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minimal, and validate.
In `@docker-compose.base.yml` at line 64, Update the default for
POSTGRES_SHM_SIZE_BYTES in the shared-memory size setting to 4 GiB so it meets
or exceeds the default HNSW maintenance budget. Preserve the
environment-variable override.
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Two Django settings for a migration that runs once were the wrong shape. 0006's rebuild is now a plain CREATE INDEX IF NOT EXISTS that honors the server's maintenance_work_mem and max_parallel_maintenance_workers, exposed as compose GUC knobs (POSTGRES_MAINTENANCE_WORK_MEM, POSTGRES_MAX_PARALLEL_MAINTENANCE_WORKERS) beside their existing siblings, defaulting to PostgreSQL's own defaults. The /dev/shm tmpfs stays: an undersized shared memory segment is a standing footgun for any PostgreSQL parallelism, not just this build, and a tmpfs size is a cap, not a reservation. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VDei6anDxfR5eoHhFfXBGs
Neither the GUC pass-throughs nor a /dev/shm tmpfs belong in the repo: server tuning is the deployer's call. maintenance_work_mem and the parallel-worker count are reachable through ALTER SYSTEM (persisted in the data volume, reloadable without restart), and the container's /dev/shm -- which parallel maintenance pre-allocates and which cannot be changed from inside PostgreSQL -- is documented as a deploy-side compose override or service mount instead. The admin guide carries the full recipe for the large-archive migration window. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VDei6anDxfR5eoHhFfXBGs
The compose file already carries maintainer-established postgres tuning variables (max_parallel_workers_per_gather and friends), so the maintenance knobs and the /dev/shm tmpfs follow the house pattern after all. This restores POSTGRES_MAINTENANCE_WORK_MEM, POSTGRES_MAX_PARALLEL_MAINTENANCE_WORKERS and POSTGRES_SHM_SIZE_BYTES with their documentation. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VDei6anDxfR5eoHhFfXBGs
Five migrations become three, split exactly where the semantics force it: 0003 (columns and all seven triggers, atomic and instant), 0004 (the non-atomic chunked backfill with the HNSW drop), 0005 (indexes, HNSW rebuild and ANALYZE, atomic). The former column/trigger split and the separate code-rename-trigger migration carried no transaction boundary of their own, and creating the rename triggers before the backfill also covers renames during the backfill window. Databases that applied the old migration names need their django_migrations rows renamed once; fresh installs are unaffected (nothing upstream has applied any of these). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VDei6anDxfR5eoHhFfXBGs
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VDei6anDxfR5eoHhFfXBGs
Makes the full-text search candidate query single-table, which is what was making
search take multiple seconds on a large archive.
Spec:
docs/superpowers/specs/2026-08-29-fts-query-shape-performance-design.mdThe problem
_build_filter_querybuilt every predicate as areport__traversal, so the FTScandidate query joined
reports_report,reports_report_groupsandreports_language, then appliedSELECT DISTINCT. Two consequences:search_vector, becausets_rankabove the join still needs it. On a 1M rig with 730k matches that hashtable was 1.2 GB, spilling to ~1.2 GB of temp files at default
work_mem.SELECT DISTINCTblocked the top-N heapsort, forcing a full sort of everymatching row before
LIMITcould apply.ORDER BY ts_rankhas no top-k shortcut in PostgreSQL -- a GIN posting list isdocid-ordered and carries no impact data -- so every match must be scored. That
part is inherent. The constant factor was not.
The change
Completes
ReportSearchIndexas a search projection: ten columns mirroring theReportfields search filters on, so the candidate query touches one table.0003,0006): nullable scalars and constant-defaultarrays, so the
AddFieldis metadata-only; GIN ongroup_ids, btree onreport_updated_at,fastupdate=offon the search-vector GIN,ANALYZE.0004): three trigger functions and five AFTER statement-leveltriggers with transition tables, so writers that bypass the ORM stay correct.
0005): chunked byreport_id(50k), one transaction per chunk,each chunk locking its rows first to close an EvalPlanQual window under READ
COMMITTED where a stale aggregate could restore a just-removed group.
_build_filter_query, both.distinct()callsgone, date filters as half-open ranges rather than
__date(which compiles to anon-sargable function over the column),
group=Nonefail-closed.manage.py check_search_projection..env.Results
Measured at the 8M design target, same table and same four workers on both sides:
findingsPlan shape is now parallel scan -> top-N heapsort -> gather merge, with no joins
and no
DISTINCT.test_filter_query_plan_is_single_tableguards against areport__traversal creeping back in.Deployment
The backfill rewrites every row, so it blocks the deploy -- measured at 9 min 20 s
for 8M reports.
WAIT_INIT_TIMEOUTis now3600in both compose files, becausethe previous fixed
-t 300timed out mid-backfill and left containers exitedafter a technically successful migration. The web tier must stay down for the
migration: between adding the columns and backfilling them every row carries an
empty group list, and a search with no active group would match the whole archive.
The rewrite also roughly doubles the table on disk (10 GB -> 21 GB at 8M) and that
does not decay in useful time, since only net new inserts consume the freed space.
The admin guide documents compacting it in a separate maintenance window, what it
costs (the HNSW rebuild, not the table copy), and that embeddings are copied
rather than recomputed.
Testing
991 passed, output pristine. Migrations applied and timings taken against a real
8M-report corpus, not only the test suite.
Notes for review
ts_rankdiversity islower than real reports. Section 8 of the spec lists the caveats that bound
every figure.
functions, drift check) because migrations must not import app code. Each site
says so and names the others.
Extraction jobs silently process at most ~10,100 reports regardless of how many match #290 (extraction silently caps at ~10,100 reports) and subscription_retrieval_provider is registered but never used #291.
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