Vacuum freshness
Maintenance audit
Postgres vacuum analyze check for stale maintenance signals
Database Optimization Tool reviews PostgreSQL vacuum and analyze timestamps from evidence-focused statistics, then keeps findings evidence-gated and advisory for human planning.
Analyze freshness
Dead-row context
No automatic maintenance
Vacuum and analyze freshness
The check reviews last vacuum, last autovacuum, last analyze, last autoanalyze, estimated live rows, estimated dead rows, and table size to find stale maintenance candidates.
Why analyze drift matters
Stale planner statistics can make PostgreSQL choose poor plans. The audit treats old analyze timestamps as a review signal, especially when paired with query latency or scan pressure.
Why vacuum drift matters
Rising dead-row pressure and old vacuum timestamps can point to maintenance lag, but the report avoids unsafe conclusions unless the collected evidence supports the finding.
Advisory-only output
Database Optimization Tool does not run VACUUM, ANALYZE, VACUUM FULL, or autovacuum configuration changes. AI-assisted notes stay advisory and require human review.
FAQ
Frequently asked questions
These answers describe the product focus: careful database evidence, clear findings, and team-approved next steps.
Will Database Optimization Tool run VACUUM or ANALYZE?
No. The workflow is evidence-focused. It can surface stale maintenance evidence, but production commands remain manual owner decisions.
Does an old analyze timestamp always mean a problem?
No. It depends on table changes, query patterns, row estimates, and workload importance. The audit treats stale timestamps as signals to verify.
Can this tune autovacuum settings automatically?
No. It does not change PostgreSQL configuration. Any threshold, scale factor, or scheduling change needs human review and production planning.
Is the AI output authoritative?
No. AI assistance is advisory only. Evidence-gated findings and database-owner review determine what action, if any, is safe.