SQL

PostgreSQL diagnostics, autonomous.

dbexpertAI runs expert-curated PostgreSQL detection paths every 90 seconds — covering vacuum health, plan regressions, lock chains, replication, connection saturation, and WAL pressure — and delivers root cause with evidence and resolution steps, autonomously.

PostgreSQL fails in idiomatic ways: MVCC bloat quietly rots plans, autovacuum falls behind exactly when write load peaks, and a single idle-in-transaction session can freeze half an application. The investigation trails are well known to veteran Postgres DBAs — which is what makes them encodable. Our PostgreSQL path library is the deepest in the platform.

Failure modes we detect

How PostgreSQL actually fails in production

Autovacuum starvation and table bloat

Dead tuples accumulate faster than autovacuum clears them — because cost limits throttle it, long transactions hold back the xmin horizon, or per-table scale factors are wrong for hot tables. The visible symptom arrives much later, as sequential scans and latency, long after the cause started.

Plan regressions after statistics drift

A table crosses a row-count threshold, statistics go stale, or a correlated-column assumption breaks — and the planner flips a hot query from an index scan to a sequential scan or a bad join order. Nothing "errors"; p99 just multiplies. Finding the flipped plan by hand means catching it in the act.

Lock chains behind idle-in-transaction sessions

One connection opens a transaction, takes a lock, and goes idle — an ORM misconfiguration or a forgotten psql session. Everything queues behind it in a chain that monitoring shows as "high active connections" while the actual culprit sits idle and invisible at the head.

Replication lag and WAL pressure

Streaming replicas fall behind under bulk writes, replication slots pin WAL until disks fill, and hot-standby conflicts cancel long read queries on replicas. Each has a distinct evidence signature in pg_stat_replication and pg_replication_slots that names the fix.

Connection-pool saturation

Postgres backends are processes, not threads. Too many direct connections burn memory and context switches; a missing or misconfigured pooler (or transaction-mode pitfalls with prepared statements) turns load spikes into connection storms that look like 'database is slow' from the app side.

A real diagnosis, walked through

What a PostgreSQL detection path delivers

diagnosis walkthrough · postgresql

// symptom

Checkout API p99 jumped from 240ms to 8.4s. No deploy, no traffic spike.

// diagnostic evidence

pg_stat_user_tables: orders has n_dead_tup / n_live_tup = 0.34, last_autovacuum 26 hours ago
autovacuum cost budget saturated: workers spending their cycles on two other large tables
pg_stat_statements: the checkout query flipped from idx_orders_created to a sequential scan
no lock waits, replication healthy — competing hypotheses ruled out on evidence

// root cause

Autovacuum starved on the orders table → dead tuples accumulate to 0.34 of live rows → stale statistics and heap bloat raise the estimated cost of the index path until the planner abandons it for a sequential scan. Latency is the third-order symptom of a vacuum-scheduling problem.

// resolution steps

Immediate: VACUUM (ANALYZE) public.orders to clear dead tuples and refresh statistics
Raise autovacuum_vacuum_cost_limit so workers keep up with sustained write load
Set table-level autovacuum_vacuum_scale_factor = 0.02 on orders so hot tables vacuum proportionally to their churn, not their size

Composite walkthrough drawn from the PostgreSQL detection-path library — the shape of every diagnosis: symptom, evidence, root cause, resolution. Yours will name your tables.

Path library coverage

What the PostgreSQL detection paths watch

Vacuum health: dead-tuple ratios, xmin-horizon holdback, wraparound distance, per-table autovacuum tuning
Query performance: plan flips, statistics staleness, missing/unused indexes, pg_stat_statements outliers
Locking: lock chains, idle-in-transaction blockers, deadlock patterns
Replication: streaming lag, slot-pinned WAL, standby conflicts, failover readiness
Capacity: connection saturation, memory pressure, temp-file spills, WAL and disk growth
Configuration drift: settings that contradict workload shape (shared_buffers, work_mem, checkpoint spacing)

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