Efficient String Compression for Modern Database Systems
6 days ago
- #FSST
- #database-optimization
- #string-compression
- Strings are the most prominent data type, accounting for roughly 50% of stored data due to their flexibility and convenience.
- Compressing strings is crucial for reducing storage costs and improving query performance by minimizing data size and memory footprint.
- CedarDB initially supported three string compression schemes: Uncompressed, Single Value, and Dictionary compression.
- Dictionary compression replaces strings with fixed-size integer keys, enabling efficient random access and query evaluation on compressed data.
- FSST (Fast Static Symbol Table) compresses strings by replacing frequent substrings with 1-byte tokens, offering better compression for low-entropy strings.
- Integrating FSST with dictionary compression combines the benefits of both methods, improving compression ratios while maintaining efficient query processing.
- Benchmarks show FSST reduces storage size by 20-60% and improves cold query runtimes by up to 40%, though hot runs may slow down due to decompression overhead.
- Activating FSST in CedarDB is a net win, balancing storage savings and performance improvements despite some trade-offs in decompression speed.