5 Critical PostgreSQL Mistakes

In this guide, I will share insights distilled from industry-standard resources like PostgreSQL 14 Administration Cookbook and PostgreSQL…

5 Critical PostgreSQL Mistakes

In this guide, I will share insights distilled from industry-standard resources like PostgreSQL 14 Administration Cookbook and PostgreSQL Mistakes and How to Avoid Them, blended with modern architectural tools like Patroni and PgBouncer. My goal is not just to highlight the errors, but to demonstrate how we solve them in a modern infrastructure.

What You Will Find in This Article

The “More Connections” Fallacy: Why max_connections is not a performance slider.
The Vacuum Suicide: The catastrophic results of disabling Autovacuum.
The Patroni Era (Modern HA): Why manual failover scripts are obsolete.
The Default Config Trap: Why shared_buffers shouldn't be your first concern (but matters at #4).
JSON Anti-Patterns: The cost of treating PostgreSQL entirely like NoSQL.

1. The “More Connections Is Better” Fallacy & PgBouncer

Developers often assume that the more connections a database can accept, the better the performance will be. Consequently, they often crank up max_connections to astronomical numbers like 2000 or 5000.

The Technical Reality: Unlike some other databases, PostgreSQL is process-based, not thread-based. Every new connection spawns a new process on the operating system, consuming its own memory footprint.
The Consequence: An excessive number of connections forces the CPU to spend more time on Context Switching than on executing actual queries. This leads to a sudden performance cliff.

The Modern Solution: PgBouncer

Instead of allowing thousands of direct connections, you should implement a connection pooler like PgBouncer. It acts as a gatekeeper, accepting thousands of client connections but mapping them to a small, efficient number of persistent connections (e.g., 50–100) on the database side.

Pro Tip: Using “Transaction Mode” in PgBouncer reduces connection overhead to almost zero.

2. Disabling Autovacuum: A Silent Suicide

Some system administrators, in an attempt to reduce I/O usage during peak hours, make the fatal mistake of disabling the autovacuum process.

The Problem: Due to PostgreSQL’s MVCC (Multi-Version Concurrency Control) architecture, updated or deleted rows are not immediately removed from the disk. They are marked as “dead tuples.”
The Risk: If Autovacuum is off, these dead tuples accumulate. Your tables suffer from “bloat,” disk space evaporates, and queries slow down as they have to scan through garbage data.
The Nightmare: Eventually, you may hit the Transaction ID Wraparound issue, which forces the database into a read-only state to prevent data corruption.

The Recommendation: Never disable it. Instead, tune it. For high-write tables, adjust parameters like autovacuum_vacuum_scale_factor to trigger more frequently (e.g., at 5% change instead of the default 20%) to keep cleanup operations small and manageable.

3. High Availability (HA) Illusions: The Patroni

In the past, we relied on manual failover scripts or simple “ping” triggers. However, in modern cloud environments, we need smarter systems to prevent “Split-Brain” scenarios.

The Risk: If the network link between the Primary and Standby servers goes down, both servers might declare themselves as “Primary.” This leads to divergent data timelines and potential data loss.

The Modern Solution: Patroni

Patroni has become the gold standard for PostgreSQL HA.

How it works: It uses a Distributed Consensus Store (like ETCD or Consul) to manage the cluster state.
The Benefit: It democratically decides which node is the leader. In the event of a failure, it performs an automatic failover without data loss. When integrated with HAProxy, your application automatically routes traffic to the correct leader without manual intervention.

4. The Default Configuration Trap

I placed this item at number four intentionally. While important, many people obsess over config files before fixing their architecture (Pooling/HA). However, running dnf install postgresql-server and leaving the default postgresql.conf is still a major error.

The Error: Default settings are extremely conservative, designed to ensure PostgreSQL runs on anything, including a Raspberry Pi with 512MB RAM.
Example: shared_buffers is often defaulted to a mere 128MB. On a modern server with 64GB RAM, this is wasted potential.
The Tuning Strategy:
shared_buffers: Should be set to 15% - 25% of your total system RAM.
work_mem: Critical for performing sorts and hashes in RAM rather than spilling to disk. Warning: This value is per-connection. Setting it too high can cause Out of Memory (OOM) crashes if connection counts spike.

5. JSON Data Types: Not Everything is NoSQL

PostgreSQL’s JSONB support is fantastic, but some developers use it as an excuse to abandon relational design entirely.

The Anti-Pattern (Relational JSON): Dumping all data into a single JSON column (Entity-Attribute-Value pattern) instead of using proper columns.
The Cost: You lose the power of foreign keys (data integrity), and query performance suffers significantly compared to standard columns.

The Approach: Use a Hybrid Model.

Keep structured data (User IDs, Dates, Status) in standard columns. Use JSONB strictly for unstructured data or variable attributes. PostgreSQL is the only database that handles both paradigms simultaneously with excellence.

Conclusion

PostgreSQL offers incredible performance when configured correctly. However, relying on default settings or neglecting maintenance processes (Vacuum) creates technical debt that will eventually crash your system.

By adopting modern architectures like Patroni for HA and PgBouncer for pooling, you move from “managing a database” to “engineering a data platform.”

References & Further Reading

The following authoritative sources were consulted during the preparation of this guide.

Angelakos, J. (2020). PostgreSQL Mistakes and How to Avoid Them. Manning Publications.
Riggs, S., & Ciolli, G. (2022). PostgreSQL 14 Administration Cookbook. Packt Publishing.
Thomas, S. (2020). PostgreSQL High Availability Cookbook (2nd ed.). Packt Publishing.
Valeja, S., & Gonzalez, D. (2023). PostgreSQL for Jobseekers: Architecture, performance optimization, and high availability. Packt Publishing.