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gerrit-public.fairphone.software / platform / external / zstd / 29c5de8780252ab30ae74356fa1bcb9f782b0ff1
commit29c5de8780252ab30ae74356fa1bcb9f782b0ff1[log] [tgz]
authorNick Terrell <terrelln@fb.com>Mon Aug 10 23:11:20 2020 -0700
committerNick Terrell <terrelln@fb.com>Wed Sep 09 14:35:57 2020 -0700
treeb12b64cb17e2c1b762a25d09722b99e6e1e92072
parent1c3cb2c05cfa96c3386f114311f045098d8f3c56 [diff]
Add linux-kernel freestanding
64 files changed
tree: b12b64cb17e2c1b762a25d09722b99e6e1e92072
  1. .circleci/
  2. .github/
  3. build/
  4. contrib/
  5. doc/
  6. examples/
  7. lib/
  8. programs/
  9. tests/
  10. zlibWrapper/
  11. .buckconfig
  12. .buckversion
  13. .cirrus.yml
  14. .gitattributes
  15. .gitignore
  16. .travis.yml
  17. appveyor.yml
  18. CHANGELOG
  19. CODE_OF_CONDUCT.md
  20. CONTRIBUTING.md
  21. COPYING
  22. LICENSE
  23. Makefile
  24. README.md
  25. TESTING.md
README.md

Zstandard, or zstd as short version, is a fast lossless compression algorithm, targeting real-time compression scenarios at zlib-level and better compression ratios. It's backed by a very fast entropy stage, provided by Huff0 and FSE library.

The project is provided as an open-source dual BSD and GPLv2 licensed C library, and a command line utility producing and decoding .zst, .gz, .xz and .lz4 files. Should your project require another programming language, a list of known ports and bindings is provided on Zstandard homepage.

Development branch status:

Build Status Build status Build status Build status Fuzzing Status

Benchmarks

For reference, several fast compression algorithms were tested and compared on a server running Arch Linux (Linux version 5.5.11-arch1-1), with a Core i9-9900K CPU @ 5.0GHz, using lzbench, an open-source in-memory benchmark by @inikep compiled with gcc 9.3.0, on the Silesia compression corpus.

Compressor nameRatioCompressionDecompress.
zstd 1.4.5 -12.884500 MB/s1660 MB/s
zlib 1.2.11 -12.74390 MB/s400 MB/s
brotli 1.0.7 -02.703400 MB/s450 MB/s
zstd 1.4.5 --fast=12.434570 MB/s2200 MB/s
zstd 1.4.5 --fast=32.312640 MB/s2300 MB/s
quicklz 1.5.0 -12.238560 MB/s710 MB/s
zstd 1.4.5 --fast=52.178700 MB/s2420 MB/s
lzo1x 2.10 -12.106690 MB/s820 MB/s
lz4 1.9.22.101740 MB/s4530 MB/s
zstd 1.4.5 --fast=72.096750 MB/s2480 MB/s
lzf 3.6 -12.077410 MB/s860 MB/s
snappy 1.1.82.073560 MB/s1790 MB/s

The negative compression levels, specified with --fast=#, offer faster compression and decompression speed in exchange for some loss in compression ratio compared to level 1, as seen in the table above.

Zstd can also offer stronger compression ratios at the cost of compression speed. Speed vs Compression trade-off is configurable by small increments. Decompression speed is preserved and remains 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 server running Linux Debian (Linux version 4.14.0-3-amd64) with a Core i7-6700K CPU @ 4.0GHz, using lzbench, an open-source in-memory benchmark by @inikep compiled with gcc 7.3.0, on the Silesia compression corpus.

Compression Speed vs RatioDecompression Speed
Compression Speed vs RatioDecompression Speed

A few other algorithms can produce higher compression ratios at slower speeds, falling outside of the graph. For a larger picture including slow modes, click on this link.

The case for Small Data compression

Previous charts provide results applicable to typical file and stream scenarios (several MB). Small data comes with different perspectives.

The smaller the amount of data to compress, the more difficult it is to compress. This problem is common to all compression algorithms, and reason is, compression algorithms learn from past data how to compress future data. But at the beginning of a new data set, 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. Training Zstandard is achieved by providing it with a few samples (one file per sample). The result of this training is stored in a file called "dictionary", which must be loaded before compression and decompression. Using this dictionary, the compression ratio achievable on small data improves dramatically.

The following example uses the github-users sample set, created from github public API. It consists of roughly 10K records weighing about 1KB each.

Compression RatioCompression SpeedDecompression Speed
Compression RatioCompression SpeedDecompression Speed

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

Training works if there is some correlation in a family of small data samples. The more data-specific a dictionary is, the more efficient it is (there is no universal dictionary). Hence, deploying one dictionary per type of data will provide the greatest benefits. Dictionary gains are mostly effective in the first few KB. Then, the compression algorithm will gradually use previously decoded content to better compress the rest of the file.

Dictionary compression How To:

  1. Create the dictionary

    zstd --train FullPathToTrainingSet/* -o dictionaryName

  2. Compress with dictionary

    zstd -D dictionaryName FILE

  3. Decompress with dictionary

    zstd -D dictionaryName --decompress FILE.zst

Build instructions

Makefile

If your system is compatible with standard make (or gmake), invoking make in root directory will generate zstd cli in root directory.

Other available options include:

  • make install : create and install zstd cli, library and man pages
  • make check : create and run zstd, tests its behavior on local platform

cmake

A cmake project generator is provided within build/cmake. It can generate Makefiles or other build scripts to create zstd binary, and libzstd dynamic and static libraries.

By default, CMAKE_BUILD_TYPE is set to Release.

Meson

A Meson project is provided within build/meson. Follow build instructions in that directory.

You can also take a look at .travis.yml file for an example about how Meson is used to build this project.

Note that default build type is release.

VCPKG

You can build and install zstd vcpkg dependency manager:

git clone https://github.com/Microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.sh
./vcpkg integrate install
./vcpkg install zstd

The zstd port in vcpkg is kept up to date by Microsoft team members and community contributors. If the version is out of date, please create an issue or pull request on the vcpkg repository.

Visual Studio (Windows)

Going into build directory, you will find additional possibilities:

  • Projects for Visual Studio 2005, 2008 and 2010.
    • VS2010 project is compatible with VS2012, VS2013, VS2015 and VS2017.
  • Automated build scripts for Visual compiler by @KrzysFR, in build/VS_scripts, which will build zstd cli and libzstd library without any need to open Visual Studio solution.

Buck

You can build the zstd binary via buck by executing: buck build programs:zstd from the root of the repo. The output binary will be in buck-out/gen/programs/.

Testing

You can run quick local smoke tests by executing the playTest.sh script from the src/tests directory. Two env variables $ZSTD_BIN and $DATAGEN_BIN are needed for the test script to locate the zstd and datagen binary. For information on CI testing, please refer to TESTING.md

Status

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

License

Zstandard is dual-licensed under BSD and GPLv2.

Contributing

The "dev" branch is the one where all contributions are 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.