PostgreSQL CDC Best Practices: Managing WAL Growth and Replication Slots

Introduction

Following up on the comment about CDC and the Outbox Pattern, let’s dive deeper into PostgreSQL’s Write-Ahead Logging (WAL) mechanism and how it interacts with Change Data Capture through replication slots. Understanding this relationship is crucial for implementing robust CDC solutions and avoiding common pitfalls in production environments.

The WAL Lifecycle: Active vs. Inactive

PostgreSQL’s WAL serves as the foundation for both crash recovery and replication. Some of my recent investigation reveals a critical distinction between active and inactive WAL segments, and how replication slots influence their retention.

The Danger of Inactive Replication Slots

My findings highlight a critical operational challenge: inactive replication slots act as anchors, preventing WAL cleanup even when other healthy replicas are consuming changes normally.

Why This Matters for CDC

When implementing CDC with logical replication:

1. Disk Space Explosion: An inactive slot can cause WAL accumulation at the rate of the database’s write activity (in this case, generating TBs of data)
2. Cascading Failures: If WAL fills the disk, it can:
   • Block new writes to the database
   • Prevent other replication slots from advancing
   • Cause application downtime
3. Hidden Time Bombs: A CDC consumer that goes offline (network issues, application crashes, configuration problems) creates an inactive slot that silently accumulates WAL

Best Practices for WAL and Slot Management

1. Implement Slot Monitoring

-- Monitor slot lag and activity
SELECT 
    slot_name,
    active,
    pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)) AS lag_size,
    pg_size_pretty(safe_wal_size) AS safe_wal_size
FROM pg_replication_slots
WHERE NOT active;

2. Set WAL Size Limits

Configure max_slot_wal_keep_size (PostgreSQL 13+) to automatically invalidate slots that fall too far behind:

ALTER SYSTEM SET max_slot_wal_keep_size = '20GB';

3. Implement Automated Cleanup

Create monitoring scripts that:

• Alert when slots become inactive for extended periods
• Automatically drop slots that exceed retention thresholds
• Track WAL generation rates vs. consumption rates

4. Design for Failure

For CDC implementations:

• Use temporary replication slots where possible
• Implement circuit breakers in CDC consumers
• Design for consumer restart/recovery scenarios
• Consider using physical standbys with cascading replication for high-volume CDC

The CDC Connection

The research perfectly illustrates why the Outbox Pattern might be preferable to WAL-based CDC in certain scenarios:

Outbox Pattern Advantages:

• No WAL retention concerns
• Application-controlled cleanup
• Simpler disaster recovery

WAL-based CDC Advantages:

• Guaranteed ordering across all tables
• No application code changes
• Lower latency for changes

Key Takeaways

1. Active Monitoring is Non-negotiable: The data shows how quickly WAL can accumulate
2. Understand Write Patterns: With growing WAL logs size, databases can have significant write activity that amplifies any slot management issues
3. Plan for Consumer Failures: CDC consumers may fail – design WAL retention strategies accordingly
4. Consider Hybrid Approaches: Use WAL-based CDC for critical real-time data, Outbox Pattern for less time-sensitive changes

Conclusion

Whether choosing WAL-based CDC or the Outbox Pattern, understanding these underlying mechanics helps make informed architectural decisions and build more resilient data pipelines.
Apache Nifi can be an option to consume CDC or Outbox Pattern data.