File Compaction

How file compaction works

File compaction addresses the "small files problem" common in data lakes and table formats. When data is initially ingested, it often creates numerous small files, which can degrade query performance and increase metadata management overhead.

The compaction process typically involves:

1. Identifying small files that are candidates for compaction
2. Reading the data from these files
3. Combining them into larger files
4. Updating metadata to reflect the new file structure

Benefits of file compaction

Query performance

Larger files enable more efficient read operations by:

• Reducing the number of file opens
• Enabling better use of columnar storage formats like Apache Parquet
• Improving scan performance through fewer metadata operations

Storage optimization

Compaction provides several storage benefits:

• Reduced metadata overhead
• More efficient compression ratios
• Better utilization of storage block sizes

Compaction strategies

Time-based compaction

Files are compacted based on their timestamp ranges, making it particularly effective for time-series data:

Size-based compaction

Files are combined when they fall below a certain size threshold, optimizing for consistent read performance.

Hybrid approaches

Modern data lake systems often use a combination of strategies, considering factors like:

• Query patterns
• Data freshness requirements
• Storage costs
• Processing windows

Implementation considerations

Timing and scheduling

• Schedule compaction during low-usage periods
• Balance frequency against processing overhead
• Consider data freshness requirements

Resource management

• Monitor CPU and memory usage during compaction
• Implement backoff strategies during high system load
• Consider cluster capacity and availability

Metadata management

• Maintain snapshot isolation during compaction
• Update table metadata efficiently
• Handle schema evolution gracefully

Integration with modern data architectures

File compaction is a crucial component in:

• Data lakehouse architectures
• Delta Lake and similar systems
• Apache Iceberg table formats

These systems typically provide automated compaction features that can be configured based on workload requirements and performance goals.

Best practices

1. Monitor file sizes and distributions

   • Track file size metrics over time
   • Identify patterns in small file creation
   • Adjust compaction strategies accordingly

2. Optimize compaction scheduling

   • Align with data ingestion patterns
   • Consider query workload patterns
   • Balance resource usage

3. Configure appropriate thresholds

   • Set minimum file sizes
   • Define maximum file sizes
   • Adjust based on storage and query patterns

4. Maintain data freshness

   • Balance compaction frequency with data accessibility
   • Consider incremental processing needs
   • Implement appropriate retention policies