Compaction

How compaction works

Compaction operates by reading multiple data files or segments and combining them into a new, optimized file structure. This process typically involves:

1. Selecting candidate files for compaction
2. Merging overlapping data
3. Removing deleted or outdated records
4. Rewriting data in an optimized format

Types of compaction strategies

Size-tiered compaction

This strategy triggers compaction when a certain number of similarly-sized files accumulate. It's effective for write-heavy workloads but can temporarily require more storage during the compaction process.

Leveled compaction

Data is organized into levels, with each level containing non-overlapping files. This approach provides more predictable query performance but may require more frequent compactions.

Time-window compaction

Particularly relevant for time-series databases, this strategy compacts data based on temporal proximity, optimizing for time-based queries.

Impact on database performance

Compaction significantly affects several aspects of database operation:

Query performance

• Reduces the number of files that must be read
• Improves query latency by consolidating data
• Enables more efficient use of cache eviction strategies

Storage efficiency

• Reduces storage footprint through better data organization
• Eliminates redundant or obsolete data versions
• Optimizes storage tiering operations

Write amplification

Compaction can increase write amplification as data is rewritten during the process. Database systems must balance this overhead against the benefits of improved read performance.

Real-world applications

Time-series data management

In financial market data systems, compaction helps manage vast amounts of tick data while maintaining fast access to recent information:

Industrial telemetry

For industrial systems collecting sensor data, compaction helps maintain efficient storage while preserving data accessibility:

• Raw sensor readings are initially stored in detail
• Older data is compacted with appropriate aggregation
• Historical trends remain queryable with optimized storage

Best practices for compaction

1. Schedule compaction during off-peak hours
2. Monitor compaction performance metrics
3. Balance compaction frequency with system resources
4. Configure appropriate compaction triggers
5. Maintain adequate headroom for compaction operations

The effectiveness of compaction strategies directly impacts both operational efficiency and query performance in time-series database systems.