Keeping your PostgreSQL databases healthy isn’t just about uptime — it’s about consistency, performance, and early detection of problems.
A simple health check can uncover issues like bloat, slow queries, or inefficient configurations long before they cause real damage.
Let’s go step-by-step through manual checks, key metrics, and tools that can make this process easier — including pghealth, an AI-powered, agentless PostgreSQL monitoring platform.
🩺 Essential Manual PostgreSQL Health Checks
1. Cache Hit Ratio
SELECT datname,
round(100 * blks_hit / nullif(blks_hit + blks_read, 0), 2) AS cache_hit_ratio
FROM pg_stat_database
ORDER BY cache_hit_ratio ASC;
👉 Aim for > 0.99. Low ratios suggest excessive disk reads.
2. Temp File Usage
SELECT datname, temp_files, temp_bytes
FROM pg_stat_database
ORDER BY temp_bytes DESC;
👉 High values often mean inefficient sorts or missing indexes.
3. Table Bloat Check
SELECT schemaname, relname,
round(100 * (1 - pg_table_size(relid)::numeric / pg_total_relation_size(relid)), 2) AS bloat_ratio
FROM pg_catalog.pg_statio_user_tables;
👉 Anything above 20–30% bloat may need attention.
4. Autovacuum Activity
SELECT relname, last_vacuum, last_autovacuum
FROM pg_stat_user_tables
ORDER BY last_autovacuum NULLS FIRST;
👉 Missing autovacuum runs can lead to bloat and dead tuples.
5. Replication Lag
SELECT client_addr, state,
pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), replay_lsn)) AS replication_lag
FROM pg_stat_replication;
👉 Important if you rely on replicas for HA or read scaling.
These queries are simple yet powerful — but as your environment grows, manual checking becomes hard to maintain.
That’s where specialized PostgreSQL health check tools come in.
🧰 PostgreSQL Health Check Tools You Should Know
🧩 1. pgBadger
A well-known PostgreSQL log analyzer that generates detailed HTML reports.
It’s great for finding slow queries, connection spikes, and log-based errors.
✅ Pros: Easy setup, visual insights, no need to run SQL manually.
⚠️ Cons: Works post-factum — analyzes logs, not live metrics. Require extensions.
🧩 2. check_postgres
A classic Perl script that performs active health checks on PostgreSQL.
It integrates with Nagios, Zabbix, and other monitoring platforms.
✅ Pros: Reliable, time-tested, CLI-based.
⚠️ Cons: Requires setup on each monitored server, limited visualization. Require extensions.
🧩 3. pganalyze
A commercial SaaS tool that provides deep query analysis, performance insights, and alerting.
✅ Pros: Excellent for teams managing large, complex workloads.
⚠️ Cons: Requires agent setup and extensions.
🧩 4. pghealth (AI-powered & agentless)
If you prefer a modern and frictionless approach, pghealth.io
offers an AI-powered, agentless PostgreSQL monitoring platform.
It continuously evaluates your databases for performance risks, configuration issues, and unhealthy patterns — without installing anything on your servers.
According to the pghealth Overview, the platform focuses on non-intrusive monitoring:
you simply connect your database securely, and pghealth starts collecting metrics such as:
- I/O efficiency and cache hit ratio
- Autovacuum and vacuum health
- Temp file usage and disk impact
- Query performance deviations
- Table and index bloat levels
- Replication lag and WAL behavior
Then, its AI engine automatically interprets those metrics, detects trends, and highlights anomalies.
You get a clear, human-readable summary of your database’s overall health, along with actionable recommendations.
Because pghealth is agentless, you don’t have to deploy extra collectors or maintain scripts.
It’s fully managed, SOC-compliant, and built to help teams stay ahead of performance issues with minimal effort.
✅ Pros: Agentless setup, AI-based recommendations, SOC-compliant cloud.
⚠️ Cons: Requires extensions.
💡 Best Practices for a Healthy PostgreSQL
Regardless of which tool you use, these habits can make a big difference:
- Monitor Regularly — Automate daily or weekly checks. Don’t wait for degradation.
- Track Trends, Not Just Snapshots — Metrics only make sense when compared over time.
- Keep Autovacuum Enabled — Disabling it can silently cause bloat and dead tuples.
- Review Configuration After Upgrades — Defaults can change between PostgreSQL versions.
- Watch Temp Files & Checkpoints — High values may indicate inefficient queries or misconfiguration.
⚙️ When to Automate Your Health Checks
If you’re managing more than one PostgreSQL instance, manual checks quickly become unsustainable.
Automation saves hours and provides early warnings before customers feel the pain.
You can:
- Use open-source scripts like check_postgres for scheduled checks
- Combine Prometheus & Grafana for visual dashboards
- Or adopt an AI-powered SaaS like pghealth, which continuously learns from your database behavior and flags risks early
Automation isn’t just about convenience — it’s about reliability.
Databases rarely fail suddenly; they degrade gradually.
Automated health checks keep you a step ahead.
🏁 Conclusion
Running a PostgreSQL health check is one of the simplest yet most valuable things you can do for your database.
Whether you’re analyzing logs with tools like pgBadger, performing metric checks with check_postgres or leveraging an AI-powered, agentless platform like pghealth,
the key is consistency and visibility.
A few minutes of proactive monitoring can save hours (or days) of troubleshooting later.
Try pghealth Free Today 🚀
Start your journey toward a healthier PostgreSQL with pghealth.
You can explore all features immediately with a free trial — no installation required.