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Repeated the #compression #benchmark with the same file on a beefier machine (AMD Ryzen 9 5950X), results are quite identical, except faster overall.

This plot is also interesting:

- #gzip and #lz4 have fixed (!) and very low RAM usage across levels and compression/decompression
- #xz RAM usage scales with the level from a couple of MBs (0) to nearly a GB (9)
- #zstd RAM usage scales weirdly with level but not as extreme as #xz

#Python #matplotlib

Plot RAM at compression vs RAM at decompression for different compression algorithms. gzip very low RAM, lz4 a little more, xz increasing strongly with the level, zstd not as prominently
#python #compression #zstd #xz #matplotlib #gzip #lz4 #benchmark

First results of my #compression algorithm benchmark run on a 72MB CSV file. It seems #zstd really has something for everybody, though it can't reach #xz's insane (but slow) compression ratios at maximum settings.

This chart includes multithreaded runs for #zstd.

Very interesting! 🧐

gitlab.com/nobodyinperson/comp…

#Python #matplotlib #Jupyter #JupyterLab

Input file used for the benchmark. A CSV file with a very long header, then columns with a unix timestamp in the first column, then only one column is filled in every row, giving a cascading look to the file. It's atmospheric temperature, humidity and pressure data from several Sensirion SCD30 sensors.

Yann Büchau / ⏱️ Compression Algorithm Benchmark · GitLab

A Python script to benchmark file compression algorithms 🗜️
GitLab
#python #compression #zstd #xz #matplotlib #jupyter #jupyterlab

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