Time-Series Compression Algorithms

Understanding time-series compression

Time-series compression algorithms are designed specifically for handling sequential data points indexed by time. Unlike general-purpose compression methods, these algorithms exploit the unique characteristics of time-series data, such as temporal locality and value correlation between adjacent points. In time-series databases, compression serves dual purposes: reducing storage costs and improving query performance.

The most common compression techniques for time-series data include delta encoding, run-length encoding, and dictionary compression. Delta encoding stores differences between consecutive values rather than absolute values, which is particularly effective for slowly changing metrics like temperature readings or asset prices during stable market periods.

Advanced compression strategies

Modern time-series compression algorithms often combine multiple techniques adaptively. For instance, when handling tick data from financial markets, the system might use different compression methods for timestamps versus price values. Timestamps often benefit from delta encoding with variable-length encoding, while price values might use domain-specific compression based on tick sizes.

Compression in practice

Financial and industrial applications require careful balance between compression ratio and access speed. For example, when storing OHLC candlestick data, compression algorithms must preserve exact values while enabling fast range queries:

-- Query demonstrating precision-sensitive financial data
SELECT timestamp, price, amount
FROM trades
WHERE timestamp >= dateadd('h', -1, now())
ORDER BY timestamp;

Performance considerations

The effectiveness of compression algorithms varies based on data characteristics and access patterns. For real-time data ingestion, compression must be fast enough to keep up with incoming data while maintaining query performance. This is particularly important in financial applications where microsecond-level access times are crucial.

Summary

Time-series compression algorithms are fundamental to managing large-scale temporal data efficiently. By understanding the tradeoffs between compression ratio, processing overhead, and query performance, organizations can choose appropriate compression strategies for their specific use cases. Whether handling financial market data, industrial sensor readings, or IoT telemetry, effective compression is key to building scalable time-series data systems.