PostgreSQL Slow Query Monitoring Without Guesswork

Slow query monitoring

PostgreSQL Slow Query Monitoring Without Guesswork

Slow query monitoring is most useful when it tells you what changed. A static list of slow SQL is a start, but production teams need baselines, deploy comparisons, plans, and enough context to fix the right thing.

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Signals Worth Watching

  • A query is called often enough that small regressions matter
  • Mean execution time looks fine but p95 is getting worse
  • Rows returned stay flat while blocks read increase
  • A release changes a query’s filter or join behavior
  • The same normalized query behaves differently for different tenants

Practical PostgreSQL Checks

Rank by total impact, not only one slow execution

A query that is moderately slow and called constantly may matter more than a rare outlier.

SELECT query, calls, total_exec_time, mean_exec_time, rows
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 20;

Use logs for outliers and statements for trends

Slow query logs are useful for individual outliers. pg_stat_statements is better for normalized trends and regressions.

ALTER SYSTEM SET log_min_duration_statement = '500ms';
SELECT pg_reload_conf();

How MonPG Helps

  • MonPG keeps query history so slow query regressions can be compared to earlier baselines.
  • Query pages connect timing, calls, rows, blocks, WAL, and related index signals.
  • Alerts can focus on regression and impact instead of noisy one-off slow statements.

Related PostgreSQL Guides

PostgreSQL Tools

  • pgvector HNSW Index Tuner — Benchmark 48 HNSW configurations against your real pgvector data in 10 minutes. Get the optimal m, ef_construction, and ef_search plus zero-downtime migration SQL.
  • PostgreSQL Plan Autopsy — Paste EXPLAIN ANALYZE output and get an incident-style read of the plan: planner estimate drift, loop explosions, disk spills, buffer pressure, and the evidence SQL to prove the fix.
  • PostgreSQL Index Rollout Simulator — Model a proposed PostgreSQL index as a production rollout: DDL shape, lock level, WAL pressure, write amplification, replica lag risk, validation SQL, and rollback criteria.

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