GitHub user x-at-01 added a comment to the discussion: Sharing benchmark observations on decimal floating-point compression: comparing integer mapping with Gorilla/Chimp on time-series datasets
Performance update: fastalp v0.1.37 release I have updated the benchmark observations with the latest fastalp v0.1.37 release (published on [crates.io](https://crates.io/crates/fastalp) with source code at [fastalp](https://github.com/webc-site/wedb_embed/tree/main/fastalp)). Key improvements and updated metrics: 1. Decompression throughput: reached 32.53 GB/s (a 14.7% increase from 28.36 GB/s), compared to reference C++ ALP at ~20.0 GB/s and Gorilla / Chimp decoders at 1.5 ~ 2.5 GB/s. The speedup comes from zero-cost monomorphized decoder traits and direct raw pointer writes without temporary slice allocations. 2. Compression ratio: across the 37 standard time-series datasets from the SIGMOD 2024 ALP benchmark, the total compressed volume dropped from 104,465 bytes to 93,909 bytes (a 10.1% footprint reduction and +11.2% compression ratio improvement, reaching 3.23x overall and 6.99x geometric mean). Smooth datasets with continuous trends reach up to 431x via relaxed delta threshold evaluation. 3. Compression throughput: 4.87 GB/s with full dynamic parameter sampling, and 6.02 GB/s for pure kernel encoding with cached parameters (vs 5.45 GB/s for reference C++ ALP). 4. Production-grade memory soundness: full zero-allocation slice APIs (compress_into and decompress_into_raw) with bounded expansion on incompressible streams. GitHub link: https://github.com/apache/datafusion/discussions/24935#discussioncomment-18280362 ---- This is an automatically sent email for [email protected]. To unsubscribe, please send an email to: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
