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Zstd, short for Zstandard, is a fast lossless compression algorithm, targeting real-time compression scenarios at zlib-level and better compression ratios.

It is provided as an open-source BSD-licensed C library. For other programming languages, you can consult a list of known ports on Zstandard homepage.

Branch Status
master Build Status
dev Build Status

As a reference, several fast compression algorithms were tested and compared on a Core i7-3930K CPU @ 4.5GHz, using lzbench, an open-source in-memory benchmark by @inikep compiled with gcc 5.4.0, with the Silesia compression corpus.

Name Ratio C.speed D.speed
MB/s MB/s
zstd 0.8.2 -1 2.877 330 940
zlib 1.2.8 -1 2.730 95 360
brotli 0.4 -0 2.708 320 375
QuickLZ 1.5 2.237 510 605
LZO 2.09 2.106 610 870
LZ4 r131 2.101 620 3100
Snappy 1.1.3 2.091 480 1600
LZF 3.6 2.077 375 790

Zstd can also offer stronger compression ratios at the cost of compression speed. Speed vs Compression trade-off is configurable by small increment. Decompression speed is preserved and remain roughly the same at all settings, a property shared by most LZ compression algorithms, such as zlib or lzma.

The following tests were run on a Core i7-3930K CPU @ 4.5GHz, using lzbench, an open-source in-memory benchmark by @inikep compiled with gcc 5.2.1, on the Silesia compression corpus.

Compression Speed vs Ratio Decompression Speed
Compression Speed vs Ratio Decompression Speed

Several algorithms can produce higher compression ratio but at slower speed, falling outside of the graph. For a larger picture including very slow modes, click on this link .

The case for Small Data compression

Previous charts provide results applicable to typical files and streams scenarios (several MB). Small data come with different perspectives. The smaller the amount of data to compress, the more difficult it is to achieve any significant compression.

This problem is common to any compression algorithm. The reason is, compression algorithms learn from past data how to compress future data. But at the beginning of a new file, there is no “past” to build upon.

To solve this situation, Zstd offers a training mode, which can be used to tune the algorithm for a selected type of data, by providing it with a few samples. The result of the training is stored in a file called “dictionary”, which can be loaded before compression and decompression. Using this dictionary, the compression ratio achievable on small data improves dramatically :

Compressing Small Data
Compressing Small Data

These compression gains are achieved while simultaneously providing faster compression and decompression speeds.

Dictionary work if there is some correlation in a family of small data (there is no universal dictionary). Hence, deploying one dictionary per type of data will provide the greater benefits. Dictionary gains are mostly effective in the first few KB. Then, the compression algorithm will rely more and more on previously decoded content to compress the rest of the file.

Dictionary compression How To :

  1. Create the dictionary

zstd --train FullPathToTrainingSet/* -o dictionaryName

  1. Compress with dictionary

zstd FILE -D dictionaryName

  1. Decompress with dictionary

zstd --decompress FILE.zst -D dictionaryName

Status

Zstandard is currently deployed within Facebook. It is used daily to compress and decompress very large amount of data in multiple formats and use cases. Zstandard is considered safe for production environments.

License

Zstandard is BSD-licensed. We also provide an additional patent grant.

Contributing

The “dev” branch is the one where all contributions will be merged before reaching “master”. If you plan to propose a patch, please commit into the “dev” branch or its own feature branch. Direct commit to “master” are not permitted. For more information, please read CONTRIBUTING.

Miscellaneous

Zstd entropy stage is provided by Huff0 and FSE, from Finite State Entropy library.