refactor(jobs): run maintenance jobs on serverless compute#30
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The five maintenance jobs are pure psycopg + SDK (no SparkSession), so run them on serverless instead of requiring a provisioned cluster. - bundle: drop existing_cluster_id and the cluster_id variable; declare dependencies (psycopg[binary], databricks-sdk) per job in an `environments` block (reused via a YAML anchor) and reference it from each task via environment_key; task `libraries` no longer apply on serverless - partition_manager: fix stale docstring (job parameter / LAKETS_INSTANCE, not spark.conf.get) - docs/CHANGELOG: note jobs run on serverless compute
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Summary
The five LakeTS maintenance jobs are pure psycopg + Databricks SDK (no
SparkSession—cold_rollup_refreshuses a SQL warehouse via the SDK, not Spark). This moves them off a provisioned cluster onto serverless compute.Changes
Bundle (
databricks/bundles/databricks.yml)existing_cluster_idfrom every task and the now-unusedcluster_idvariable (and its dev/prod overrides).psycopg[binary],databricks-sdk) moved from tasklibraries(which don't apply on serverless) into a per-jobenvironmentsblock, reused across all five jobs via a YAML anchor and referenced from each task viaenvironment_key: lakets_env.run_asservice principal (prod) andsync.pathsare unchanged.Source
partition_manager.py: fixed a stale docstring that referencedspark.conf.get(...); the instance name comes from the job parameter (sys.argv[1]) orLAKETS_INSTANCE.Docs / CHANGELOG
environments.Test plan
tests/test_python_patterns.pypasses (11/11)environmentsspec, every task hasenvironment_key: lakets_env, noexisting_cluster_id, nocluster_idvar, no tasklibrariesdatabricks bundle deploy -t prod --var="service_principal_name=<sp>"provisions the jobs on serverless and a manual run of each completes against a Lakebase instanceclientversion ("3") is valid in the target workspace; bump if needed