Postgres dead rows check for maintenance review
Database Optimization Tool reviews PostgreSQL dead-row pressure from evidence-focused evidence, then frames AI-assisted notes as advisory input for human maintenance planning.
Dead rows as a review signal
The check compares estimated dead rows, estimated live rows, relation size, last vacuum, last autovacuum, and surrounding workload signals to identify tables that need closer review.
Evidence before maintenance advice
A high dead-row count is treated as evidence for investigation, not as an automatic command. The report weighs table size, write activity, scan behavior, and maintenance timestamps before prioritizing a finding.
Planner and bloat context
Dead rows can contribute to stale planner statistics and table-health risk, but Database Optimization Tool keeps exact bloat or reclaimable-space claims behind collected PostgreSQL evidence.
evidence-focused boundary
The product does not run VACUUM, VACUUM FULL, ANALYZE, or configuration changes. AI-assisted recommendations remain advisory only and require human review before production action.
Frequently asked questions
These answers describe the product focus: careful database evidence, clear findings, and team-approved next steps.
Does Database Optimization Tool remove dead rows?
No. It is evidence-focused. It reports PostgreSQL evidence and review candidates, but the database owner decides whether and when to run maintenance.
Are dead rows always a production incident?
No. Dead rows are normal in PostgreSQL. They become more important when the ratio, table size, workload, and maintenance history show pressure together.
Can AI decide the vacuum plan?
No. AI notes are advisory only. Evidence Gate controls official findings, and humans own lock-risk review, scheduling, and rollback planning.
Is this real-time dead-row monitoring?
No. This page describes a evidence-focused audit of collected PostgreSQL evidence, not a continuous monitoring agent or alerting service.