Data Compression Techniques for Time-series Data
Data compression techniques for time-series data are specialized methods that reduce storage requirements while preserving data integrity and accessibility. These techniques are optimized for the sequential nature of time-series data and its unique characteristics, such as temporal locality and value patterns.
Understanding time-series data compression
Time-series data compression addresses the challenge of efficiently storing and processing large volumes of sequential data in financial markets and industrial systems. Unlike general-purpose compression, time-series compression exploits the inherent properties of temporal data:
- Temporal locality - adjacent values often show high correlation
- Regular sampling intervals
- Value patterns and seasonality
- Mixed precision requirements
Core compression strategies
Delta encoding
Delta encoding stores differences between consecutive values rather than absolute values. This is particularly effective for:
- Market price movements
- Sensor readings with gradual changes
- Sequential timestamps
Run-length encoding
Run-length encoding (RLE) compresses sequences of identical or similar values by storing the value and its repeat count. This works well for:
- Market data during low-activity periods
- Binary state changes in industrial systems
- Repeated values in sensor data
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Advanced compression techniques
Floating-point compression
Financial markets generate massive volumes of floating-point price data. Specialized techniques include:
- Significant digit preservation
- Custom exponent handling
- Precision-based compression
Dictionary-based compression
Dictionary compression identifies and catalogs repeated patterns in the data stream:
- Build dictionary of common patterns
- Replace patterns with shorter codes
- Optimize for time-series specific patterns
Performance considerations
The choice of compression technique impacts several performance metrics:
- Compression ratio vs. CPU usage
- Query performance on compressed data
- Random access capabilities
- Memory requirements during decompression
Integration with time-series databases
Modern time-series databases implement compression as a core feature:
- Automatic compression selection
- Multi-tier compression strategies
- Compression-aware query optimization
- Real-time compression for streaming data
Applications in financial markets
Financial markets require specialized compression approaches for:
- Tick data storage and analysis
- Market data feed handling
- Real-time market data processing
- Historical data archives
Industrial applications
Industrial systems benefit from compression in:
- Sensor fusion analytics
- Industrial IoT data collection
- Process control systems
- Long-term trend analysis
Best practices
When implementing time-series compression:
- Analyze data patterns and requirements
- Consider query patterns and access frequency
- Balance compression ratio with processing overhead
- Test with representative datasets
- Monitor compression performance metrics
Future trends
Emerging developments in time-series compression include:
- Machine learning-based compression
- Hardware-accelerated compression
- Adaptive compression algorithms
- Cloud-optimized compression formats
Time-series data compression continues to evolve with new techniques and optimizations, driven by the growing volume of temporal data in financial markets and industrial systems.