the mathematics of compression in database systems
Hacker News
February 10, 2026
AI-Generated Deep Dive Summary
Compression in database systems is a critical strategy for optimizing resource usage, particularly when balancing CPU cycles with I/O bandwidth. While compression reduces data size, it introduces additional processing time due to the need for both compressing and decompressing data. This trade-off makes sense when the benefits of reduced I/O outweigh the computational costs, especially in scenarios where bandwidth is constrained.
The article explores this balance through mathematical models, introducing variables such as uncompressed size (S), compressed size (Sc), compression ratio (R), and time factors for compression/decompression (Tc, Td). By calculating a breakeven point based on I/O bandwidth, the analysis determines whether compression will improve overall performance. For instance, using zstd compression at level 4 is beneficial in scenarios with moderate network speeds, while level 10 becomes less efficient due to its high computational demands.
In practical database operations, constant data handling and caching mean that sustained throughput often matters more than individual transfer latencies. Databases typically saturate I/O bandwidth before CPU usage, making compression a valuable tool for managing resource allocation
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Originally published on Hacker News on 2/10/2026