Changelog Stream

How changelog streams work

Changelog streams maintain an ordered sequence of change events, each containing:

• The type of operation (insert, update, delete)
• Timestamp of the change
• The affected data values (before and after states)
• Additional metadata (user, transaction ID, etc.)

Applications in time-series systems

In time-series databases, changelog streams are particularly valuable for:

Real-time data propagation

• Enabling real-time analytics by streaming changes to analytical systems
• Supporting stream processing applications with live data updates
• Facilitating real-time data ingestion across distributed systems

Data recovery and auditing

• Providing point-in-time recovery capabilities
• Supporting comprehensive audit trails
• Enabling data reconciliation across systems

Implementation considerations

Performance impact

Changelog streams must be designed to handle:

• High write throughput without impacting primary database performance
• Efficient storage and retrieval of change records
• Proper retention policies for historical changes

Data consistency

Key aspects include:

• Maintaining ordered delivery of change events
• Ensuring atomic transactions are captured correctly
• Supporting idempotent writes for reliable processing

The following example shows how QuestDB can track changes using a timestamp-based approach:

WITH changes AS (
    SELECT 
        timestamp,
        symbol,
        price as new_price,
        LAG(price) OVER (PARTITION BY symbol ORDER BY timestamp) as old_price
    FROM trades
    WHERE symbol = 'AAPL'
)
SELECT * FROM changes
WHERE old_price != new_price;

Integration patterns

Common integration patterns include:

• Direct database subscribers
• Message queue integration
• API-based change feeds

Best practices

1. Performance optimization

   • Implement efficient change capture mechanisms
   • Use appropriate compression techniques
   • Consider partitioning strategies for large streams

2. Data governance

   • Define clear retention policies
   • Implement access controls
   • Maintain data lineage information

3. Operational considerations

   • Monitor stream health and latency
   • Plan for disaster recovery
   • Implement error handling and retry mechanisms

Changelog streams are fundamental to modern data architectures, especially in time-series systems where tracking data evolution is crucial for analysis and compliance.