Top PostgreSQL Monitoring Tools Every DBA Should Know

(pg_stat_statements, pgBadger, pgAdmin, Prometheus, Grafana)

Introduction

As databases grow in size and complexity, monitoring PostgreSQL is no longer optional — it’s essential.

• Without monitoring, DBAs are blind to slow queries, blocking locks, or system bottlenecks.

• Proper monitoring helps in troubleshooting performance issues, optimizing queries, and avoiding downtime.

In this blog, we’ll cover the most popular PostgreSQL monitoring tools:

• pg_stat_statements

• pgBadger

• pgAdmin

• Prometheus

• Grafana

Each tool serves a unique purpose and can be combined for comprehensive database observability.

1. pg_stat_statements (Built-in Query Tracking)

pg_stat_statements is a PostgreSQL extension that tracks SQL execution statistics.

✅ Key Features

• Tracks frequency, execution time, and I/O per query.

• Normalizes queries (ignores literal values).

• Helps identify slow and expensive queries.

Setup

⚡ Use Case

Best for query performance monitoring and tuning workloads.

⚠️ Limitation

• Only query-level statistics.

• Does not provide system-wide metrics (CPU, memory, I/O).

2. pgBadger (Log Analyzer)

pgBadger is a fast log analyzer for PostgreSQL.

✅ Key Features

• Parses PostgreSQL logs into HTML reports.

• Highlights slow queries, errors, deadlocks, autovacuum activity.

• Supports incremental reports (daily/weekly).

Usage

⚡ Use Case

Best for post-mortem analysis of logs, auditing, and understanding workload trends.

⚠️ Limitation

• Requires detailed logging configuration.

• Not real-time (works on logs after they’re written).

3. pgAdmin (Official GUI Tool)

pgAdmin is PostgreSQL’s official management tool.

✅ Key Features

• Visual dashboard for queries, locks, sessions, and activity.

• Query execution with EXPLAIN/ANALYZE visualization.

• Backup/restore management.

• Built-in monitoring graphs for sessions, locks, transactions.

⚡ Use Case

Best for DBAs who want a GUI and occasional performance checks.

⚠️ Limitation

• Not a full monitoring solution.

• Suitable for small-scale use, not large production workloads.

4. Prometheus (Metrics Collection)

Prometheus is a time-series monitoring system widely used in modern DevOps setups.

✅ Key Features

• Scrapes metrics via exporters (e.g., postgres_exporter).

• Stores historical data for trend analysis.

• Queryable with PromQL.

• Integrates seamlessly with Grafana for visualization.

Setup

1. Install postgres_exporter:

   docker run -d -p 9187:9187 \
-e DATA_SOURCE_NAME="postgresql://user:pass@host:5432/dbname?sslmode=disable" \
quay.io/prometheuscommunity/postgres-exporter


2. Configure Prometheus to scrape exporter.

⚡ Use Case

Best for collecting metrics like CPU, memory, I/O, cache hit ratio, deadlocks, connections.

⚠️ Limitation

• Requires setup and infrastructure.

• Raw metrics need Grafana for visualization.

5. Grafana (Visualization & Dashboards)

Grafana is the most popular visualization layer for monitoring systems like Prometheus.

✅ Key Features

• Beautiful dashboards for PostgreSQL metrics.

• Pre-built community dashboards (e.g., PG overview, slow queries, locks).

• Supports alerts (Slack, PagerDuty, Email).

Example Dashboard Metrics

• Active connections

• Cache hit ratio

• Autovacuum runs

• Deadlocks

• Slow queries (from pg_stat_statements)

⚡ Use Case

Best for real-time dashboards and alerts for PostgreSQL clusters.

⚠️ Limitation

• Requires Prometheus or another data source.

• Dashboard setup requires initial effort.

Summary: Which Tool to Use?

DBA Best Practices

• Use pg_stat_statements daily for query tuning.

• Run pgBadger on logs weekly for trend insights.

• Use pgAdmin for one-off debugging or quick stats.

• For production systems, deploy Prometheus + Grafana for comprehensive monitoring.

• Set up alerts (deadlocks, replication lag, high connections).

Final Thoughts

There’s no one-size-fits-all monitoring tool.

• Small projects may be fine with pgAdmin + pg_stat_statements.

• Large production workloads should use Prometheus + Grafana for scalability.

• Combine with pgBadger for historical log audits.

The best approach is a hybrid strategy: query-level monitoring (pg_stat_statements), log analysis (pgBadger), and cluster-wide observability (Prometheus + Grafana).

With these tools in place, you’ll always have visibility into PostgreSQL’s health, performance, and reliability