The mathematics of compression in database systems
3 months ago
- #database
- #performance
- #compression
- Compression in databases trades CPU cycles for reduced I/O bandwidth, optimizing between I/O, CPU, and memory resources.
- Breakeven analysis shows compression's worth depends on transfer bandwidth, with higher compression levels not always beneficial for latency.
- Logical bandwidth increases with compression, allowing higher throughput if CPU can handle the compression workload.
- Cost analysis reveals that optimal compression levels balance CPU costs against reduced data transfer fees in cloud environments.
- Semantic encoding (e.g., varint, delta encoding) and entropy compression (e.g., zstd) are combined for efficient data reduction.
- Techniques like dictionary encoding and bit-packing further optimize storage but require careful implementation to avoid inefficiencies.
- Lossy compression, while not covered in detail, is noted as valuable in specific domains like vector databases.