PostgreSQL in Production: Performance Tuning, Backups, and High Availability

Understanding the Problem

Essential PostgreSQL production practices: connection pooling, query optimization, automated backups, replication, monitoring, and security hardening.

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At TechSaaS, we build and operate production systems that serve real users. Every technical decision we make is battle-tested.

In this article, we'll dive deep into the practical aspects of postgresql in production: performance tuning, backups, and high availability, sharing real code, real numbers, and real lessons from production.

Our Approach

When we first tackled this challenge, we evaluated several approaches. The key factors were:

We chose a pragmatic approach that balances these concerns. Here's what that looks like in practice.

Technical Implementation

The implementation required careful attention to several technical details. Let's walk through the key components.

# Infrastructure health check script
#!/bin/bash
echo "=== System Health ==="
echo "Containers: $(docker ps -q | wc -l) running"
echo "CPU: $(top -bn1 | grep 'Cpu(s)' | awk '{print $2}')%"
echo "Memory: $(free -h | awk '/Mem:/{print $3"/"$2}')"
echo "Disk: $(df -h / | awk 'NR==2{print $5}')"

# Check critical services
for svc in traefik directus gitea prometheus; do
  status=$(docker inspect -f '{{.State.Status}}' $svc 2>/dev/null || echo "missing")
  echo "$svc: $status"
done

This configuration reflects lessons learned from running similar setups in production. A few things to note:

1. Resource limits are essential — without them, a single misbehaving service can take down your entire stack. We learned this the hard way when a memory leak in one container consumed 14GB of RAM.

2. Volume mounts for persistence — never rely on container storage for data you care about. We mount everything to dedicated LVM volumes on SSD.

3. Health checks with real verification — a container being "up" doesn't mean it's "healthy." Always verify the actual service endpoint.

Common Pitfalls

We've seen teams make these mistakes repeatedly:

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Real-World Application

In production, this approach has delivered measurable results:

|--------|--------|-------|-------------|

These numbers come from our actual production infrastructure running 90+ containers on a single server — proving that you don't need expensive cloud services to run reliable, scalable systems.

What We'd Do Differently

If we were starting today, we'd:

Key Takeaways

Building postgresql in production: performance tuning, backups, and high availability taught us several important lessons:

1. Start with the problem, not the technology — the best architecture is the one that solves your specific constraints 2. Measure everything — you can't improve what you don't measure 3. Automate the boring stuff — manual processes are error-prone and don't scale 4. Plan for failure — every system fails eventually; the question is how gracefully

If you're tackling a similar challenge, we've been there. We've shipped 36+ products across 8 industries, and we're happy to share our experience.

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*Tags: postgresql, database, performance, production, backups*