Query monitoring
Query monitoring is where PostgreSQL observability becomes actionable. Instance CPU tells you that something hurts; query monitoring tells you which SQL, which plan, and which application path is responsible.
A few query fingerprints dominate total execution time
Call count increases after a feature release
Rows-per-call changes while application behavior looks the same
Index scans drop and sequential scans rise
Wait events reveal lock or I/O pressure during query execution
Normalized query fingerprints let teams watch behavior over time instead of chasing every literal SQL value.
SELECT queryid, calls, mean_exec_time, rows, shared_blks_hit, shared_blks_read
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 20;The same slow query can be CPU-bound, I/O-bound, blocked on locks, or waiting behind connection pressure. Wait events keep the diagnosis honest.
SELECT pid, wait_event_type, wait_event, state, query
FROM pg_stat_activity
WHERE state <> 'idle'
ORDER BY wait_event_type NULLS LAST;MonPG turns cumulative pg_stat_statements counters into time-windowed query trends.
Teams can compare query behavior by deploy, database, user, and application name.
Query monitoring connects slow SQL with index health, waits, vacuum, and connection pressure.
Hands-on setup, integration, and troubleshooting guides from the MonPG product docs.
The one-page fleet summary. Active alerts, connection pressure, slowest queries, top size consumers.
Drill into any metric over any time range with full chart controls.
The Queries page is how you find performance outliers. Columns explained + filtering tips.
Diff this hour vs. previous hour, today vs. yesterday — find what regressed.
How Query Advisor parses captured plans and what the bottleneck labels mean.
Proposes indexes for queries that need them, flags indexes that aren't being used.
Find bloated tables, tune autovacuum per-table, and catch XID wraparound before it bites.
Per-table size breakdown, growth trends, and TOAST / index ratios.
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.
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.
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.
MonPG gives teams query history, alerts, index guidance, vacuum visibility, replication signals, and cloud PostgreSQL monitoring in one place.
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