PostgreSQL monitoring guide

CPU activity

High CPU usage by itself is not a problem, but sustained saturation often indicates deeper issues such as missing indexes or inefficient query plans. Monitoring CPU utilization alongside active sessions and query types helps map performance variations to workload changes.

Memory usage

PostgreSQL uses shared buffers, work memory, and maintenance memory to manage caching, sorting, and maintenance operations. When memory limits are reached, queries spill to disk, which slows down response times. Tracking memory consumption along with temporary file creation provides early visibility into memory pressure.

Disk performance

Storage latency affects nearly every aspect of PostgreSQL performance. High read latency signals poor cache efficiency or slow storage devices, while high write latency affects WAL commits and checkpoint operations. Monitoring IOPS, queue depth, and disk throughput helps identify storage imbalances before they escalate.

Query execution trends

Tracking slow queries, frequently executed queries, and high resource consuming queries makes it easier to identify tuning opportunities. Variations in query timing are especially useful because they highlight factors such as caching differences, plan changes, or write contention.

Wait events

PostgreSQL includes detailed wait events that describe what each backend is waiting for. Some common categories include:

• Lock waits, which highlight concurrency challenges
• I/O waits, which suggest disk dependency

Monitoring wait events by query pattern helps pinpoint the exact cause of latency.

Connection patterns

Many performance issues originate from inefficient connection handling. Excessive connection churn, missing connection pooling, or long lived idle sessions can impact throughput. Monitoring connection creation, active backends, and average session duration provides insight into application behavior under load.

Table and index growth

Monitoring the growth rate of tables and indexes helps identify whether increases are due to genuine data expansion or internal churn. Indexes should be monitored separately since their growth does not always match table growth. Sudden increases often signal missing autovacuum cycles or inefficient update patterns.

Bloat and autovacuum activity

Dead tuples and index bloat slow down both reads and writes. Autovacuum helps manage this, but it must run frequently enough to keep up with workload changes. Monitoring bloat, vacuum frequency, and vacuum durations helps maintain healthy data layouts.

Related: MySQL performance bottlenecks and how to prevent them.

Checkpoints and WAL activity

Checkpoints flush modified pages to disk, but overly frequent checkpoints cause I/O bursts. Monitoring checkpoint intervals, WAL generation rates, and background writer behavior helps prevent unexpected slowdowns during busy periods.

Replication lag

Replication lag occurs when the standby cannot keep up with WAL application. It can be caused by slow disks, high WAL volume, or network congestion. Monitoring both time based lag and byte based lag provides a fully accurate picture.

Standby performance

A healthy standby should apply WAL segments at a steady rate. Tracking apply latency, standby I/O, and conflict events ensures that the standby remains ready for immediate promotion.

Backup status and integrity

Monitoring backup completion, WAL archiving, and periodic restore validation ensures that disaster recovery processes are reliable. Even occasional restore rehearsals help prevent unexpected failures.

Transaction characteristics

Long running transactions block vacuum processes and delay cleanup. Monitoring transaction duration helps teams detect inefficient application logic.

Workload segmentation

Different application modules generate different query patterns. Monitoring activity by client host, user, or application label helps isolate problematic components.

Traffic surges

Predictable or unexpected surges in traffic can create pressure on CPU, memory, and connection pools. Monitoring these spikes helps improve capacity planning and autoscaling decisions.

Correlation across layers

CPU spikes are more meaningful when mapped to slow queries or increased WAL activity. Lock waits matter more when connected to specific transactions. Correlation shortens troubleshooting time and improves accuracy during incidents.

Trend based analysis

Gradual increases in bloat, WAL generation, or lock contention often forecast future problems. Monitoring trends over weeks or months helps teams take proactive action.

Baselines and anomaly detection

Each PostgreSQL environment has unique performance characteristics. Establishing baselines allows monitoring tools to surface anomalies quickly, even if the numbers look acceptable on their own.

Post change validation

Any modification to schemas, indexes, or application logic should be followed by monitoring. Comparing pre change and post change metrics ensures that optimizations do not introduce new regressions.