Compression Ratio

Understanding compression ratio

Compression ratio is typically expressed as a ratio or percentage of compressed size to original size. For example, a 10:1 ratio means the compressed data is one-tenth the size of the original data. The higher the ratio, the more effective the compression.

compression_ratio = original_size / compressed_size
storage_savings_percentage = (1 - compressed_size/original_size) * 100

Time-series data compression characteristics

Time-series data often exhibits patterns that make it highly compressible:

1. Temporal locality - consecutive values tend to be similar
2. Regular sampling intervals
3. Common patterns like seasonality
4. Limited value ranges within columns

Impact on database performance

Compression ratio directly affects several aspects of database performance:

Storage efficiency

• Reduced disk space requirements
• Lower storage costs
• Improved cache utilization
• More efficient backup operations

Query performance

• Faster cold start queries due to reduced I/O
• Potential CPU overhead for decompression
• Balance between compression level and query latency

Write performance

• Higher write amplification with more aggressive compression
• Impact on ingestion latency

Compression strategies for time-series data

Different compression techniques offer varying compression ratios:

Lossless compression

• Delta encoding for timestamps
• Dictionary encoding for repeated values
• Run-length encoding for constant periods
• Maintains exact data reconstruction

Lossy compression

• Downsampling high-frequency data
• Floating-point precision reduction
• Acceptable for certain analytical queries
• Higher compression ratios

Hybrid approaches

• Different compression methods per column
• Age-based compression policies
• Integration with storage tiering

Monitoring and optimization

Key considerations for maintaining optimal compression ratios:

1. Regular monitoring of compression effectiveness
2. Column-specific compression strategies
3. Data pattern analysis
4. Storage cost vs. query performance tradeoffs
5. Integration with retention policies

Best practices for optimizing compression ratio

1. Choose appropriate compression algorithms based on data characteristics
2. Monitor compression ratio trends over time
3. Balance compression ratio with query performance requirements
4. Consider column-specific compression strategies
5. Implement testing procedures for compression changes

Time-series databases must carefully balance compression ratio with other performance metrics to provide optimal data storage and retrieval capabilities.