Support schema-qualified table names in db clean#70147
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Celery result backends are commonly provisioned into their own database
schema, separate from Airflow's metadata tables. airflow db clean has
no way to reach celery_taskmeta / celery_tasksetmeta in that case, since
it only ever looks in the connection's default schema, so those tables
are silently skipped every time.
_TableConfig, reflect_tables, and the archive/export/drop machinery in
db_cleanup.py now understand schema-qualified table names ("schema.table"
dot notation). A new [celery] result_backend_schema config option lets
a deployment point the built-in celery_taskmeta / celery_tasksetmeta
configs at the schema they actually live in, without needing a fork or
manual per-table config.
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Celery result backends are commonly provisioned into their own database schema, separate from Airflow's metadata tables.
airflow db cleanhas no way to reachcelery_taskmeta/celery_tasksetmetain that case, since it only ever looks in the connection's default schema, so those tables are silently skipped every time._TableConfig,reflect_tables, and the archive/export/drop machinery indb_cleanup.pynow understand schema-qualified table names ("schema.table"dot notation). A new[celery] result_backend_schemaconfig option lets a deployment point the built-incelery_taskmeta/celery_tasksetmetaconfigs at the schema they actually live in, without needing a fork or manual per-table config.Verified end-to-end against a real Postgres instance: clean → archive (lands in the same schema as the source table) → export → drop, all schema-aware. New unit + integration tests cover schema parsing, the config-driven wiring, and the full cleanup cycle; existing test suite passes unmodified on both sqlite and Postgres.
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