Append-only Log

How append-only logs work

Append-only logs store data sequentially, with each new record receiving a unique, monotonically increasing identifier. This sequential nature creates a natural timeline of events, making them ideal for:

• Time-series data storage
• Event sourcing
• Transaction logging
• Change data capture (CDC)

Benefits of append-only design

Data integrity

Since existing records cannot be modified, append-only logs provide natural audit trails and make it easier to maintain data consistency across distributed systems.

Performance

Sequential writes are typically faster than random access patterns, and the immutable nature eliminates write conflicts.

Simplicity

The append-only model simplifies system design by eliminating update and delete operations.

Real-world applications

Time-series databases

Time-series databases often use append-only logs as their primary storage mechanism, optimizing for the sequential nature of temporal data.

Event streaming

Platforms like Apache Kafka use append-only logs to implement:

• Message queues
• Event sourcing
• Change Data Capture (CDC)

Financial systems

In financial markets, append-only logs are crucial for:

• Trade audit trails
• Regulatory compliance
• Transaction history

Implementation considerations

Storage management

While append-only logs grow continuously, practical implementations often include:

• Retention policies
• Log compaction
• Archival strategies

Partitioning

For scalability, logs are often partitioned by:

• Time ranges
• Topic
• Customer ID
• Geographic region

Recovery and replication

Append-only logs facilitate:

• Point-in-time recovery
• Replication across nodes
• Event replay for system recovery

Performance characteristics

Write optimization

# Pseudocode for append operation
def append_event(log, event):
    position = log.head_position
    log.write(position, event)
    log.head_position += 1
    return position

Read patterns

• Sequential reads are efficient
• Random access may require index structures
• Replay from any point is straightforward

Best practices

1. Use appropriate partitioning strategies
2. Implement clear retention policies
3. Consider compression for older segments
4. Monitor growth rate and storage usage
5. Plan for disaster recovery scenarios

Relationship to other patterns

Append-only logs often work in conjunction with:

• Write-ahead Log
• Event Sourcing
• Streaming Feature Extraction

Conclusion

Append-only logs are a fundamental pattern in modern data systems, providing a robust foundation for time-series data storage, event streaming, and distributed systems. Their simplicity, performance characteristics, and natural support for auditing make them invaluable in financial systems, databases, and event-driven architectures.