PostgreSQL Performance Tips for Write-Heavy Systems: Lessons from the Trenches

Introduction

When working with PostgreSQL in write-heavy systems, the challenges go beyond simple speed tuning. Sustaining high performance, ensuring reliability, and avoiding late-night incidents due to locked tables are key priorities.

After years of optimizing backend systems—especially those with high write throughput—I've compiled the most effective tips and lessons learned from real-world scenarios. Whether you're building event-driven applications, ingesting large volumes of sensor data, or running transactional systems, these practical insights will help you get more out of PostgreSQL.

1. Use Bulk Inserts to Reduce Overhead

Why it matters:
Each INSERT is its own transaction, adding overhead. Switching to bulk inserts reduces network roundtrips, transaction cost, and context switching.

Real impact:
In one production system, changing from single-row inserts to batched inserts reduced write latency by over 60%.

2. Disable Unnecessary Indexes

Why it matters:
Indexes speed up reads, but they slow down writes. Every INSERT, UPDATE, or DELETE must also update all related indexes.

Optimization tip:
Audit your indexes using:

sqlCopyEditSELECT * FROM pg_stat_user_indexes WHERE idx_scan = 0;

Remove any indexes not being used by your queries.

3. Use UNLOGGED Tables for Temporary or Volatile Data

Why it matters:
For data that doesn't need to survive a crash—like temporary logs or event queues—UNLOGGED tables eliminate WAL overhead.

Caveat:
You’ll lose this data if PostgreSQL crashes, but gain significant write throughput in return.

4. Tune Write-Ahead Logging (WAL) Settings

Key parameters to adjust:

• wal_level: Consider minimal if you don't need replication.

• synchronous_commit: Set to off or local to reduce commit wait time.

• commit_delay: Fine-tune to batch WAL writes.

Goal:
Balance data safety with performance based on your use case.

5. Partition Large Tables for Better Performance

Why it matters:
Large tables with millions of rows slow down queries, inserts, and maintenance operations.

Strategy:
Use table partitioning based on time, tenant, or type. In one system, this reduced a materialized view refresh from 45 minutes to under 5.

6. Use Connection Pooling with PgBouncer

Why it matters:
High connection counts kill throughput in PostgreSQL. Use PgBouncer to:

• Limit open connections

• Reuse idle ones

• Boost concurrent writes efficiently

7. Monitor and Tune Autovacuum

Why it matters:
PostgreSQL uses MVCC (Multi-Version Concurrency Control), which means dead tuples build up over time.

Best practices:

• Monitor autovacuum activity

• Tune autovacuum_vacuum_cost_limit, autovacuum_vacuum_threshold, etc.

• Consider manual vacuuming during off-peak hours

8. Use COPY for Bulk Data Ingestion

Why it matters:
COPY is much faster than INSERT when importing large datasets.

Example:
Switching from INSERT loops to COPY slashed ingestion times from hours to minutes in one of our ETL pipelines.

9. Avoid Triggers on High-Volume Tables

Why it matters:
Triggers add overhead and latency to every row write. In write-heavy tables, this can significantly degrade performance.

Alternative:
Move complex logic to asynchronous background jobs using workers or queues.

10. Enable Logging for Slow Queries and Actually Use Them

Why it matters:
You can’t optimize what you don’t measure.

How to start:

• Enable log_min_duration_statement

• Analyze logs regularly to identify slow writes and missing indexes

Bonus Tip: Hardware Still Matters

Lesson learned:
Moving from HDDs to SSDs boosted throughput by 5x in one project.

Recommendation:
Make sure your storage and IOPS match your write performance requirements. Don’t let slow disks become your bottleneck.

Final Thoughts

Write-heavy PostgreSQL environments require a unique approach—one that’s equal parts tuning, monitoring, and architectural decision-making. By combining bulk operations, smart indexing, WAL tuning, and scalable design patterns, you can dramatically increase throughput and reduce system load.

When building for scale, PostgreSQL is more than capable—you just have to play to its strengths.