This problem is common to all compression algorithms, and reason is, compression algorithms learn from past data how to compress future data. The smaller the amount of data to compress, the more difficult it is to compress. Small data comes with different perspectives. Previous charts provide results applicable to typical file and stream scenarios (several MB). Compression Speed vs RatioĪ few other algorithms can produce higher compression ratios at slower speeds, falling outside of the graph.įor a larger picture including slow modes, click on this link. Using lzbench, an open-source in-memory benchmark by with gcc 7.3.0, On a server running Linux Debian ( Linux version 4.14.0-3-amd64) Speed vs Compression trade-off is configurable by small increments.ĭecompression speed is preserved and remains roughly the same at all settings,Ī property shared by most LZ compression algorithms, such as zlib or lzma. Zstd can also offer stronger compression ratios at the cost of compression speed. Offer faster compression and decompression speedĪt the cost of compression ratio (compared to level 1). The negative compression levels, specified with -fast=#, Using lzbench, an open-source in-memory benchmark by with gcc 9.3.0, On a desktop running Ubuntu 20.04 ( Linux 5.11.0-41-generic), Should your project require another programming language,Ī list of known ports and bindings is provided on Zstandard homepage.įor reference, several fast compression algorithms were tested and compared This repository represents the reference implementation, provided as an open-source dual BSD OR GPLv2 licensed C library,Īnd a command line utility producing and decoding. Multiple independent implementations are already available. Zstandard's format is stable and documented in RFC8878. It's backed by a very fast entropy stage, provided by Huff0 and FSE library. Targeting real-time compression scenarios at zlib-level and better compression ratios. Zstandard, or zstd as short version, is a fast lossless compression algorithm,
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