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Zstandard - Fast real-time compression algorithm

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Zstandard

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.

Zstandard's format is stable and documented in RFC8878. Multiple independent implementations are already available. This repository represents the reference implementation, provided as an open-source dual BSD OR 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 Fuzzing Status

Benchmarks

For reference, several fast compression algorithms were tested and compared on a desktop featuring a Core i7-9700K CPU @ 4.9GHz and running Ubuntu 20.04 (Linux ubu20 5.15.0-101-generic), using lzbench, an open-source in-memory benchmark by @inikep compiled with gcc 9.4.0, on the Silesia compression corpus.

Compressor name Ratio Compression Decompress.
zstd 1.5.6 -1 2.887 510 MB/s 1580 MB/s
zlib 1.2.11 -1 2.743 95 MB/s 400 MB/s
brotli 1.0.9 -0 2.702 395 MB/s 430 MB/s
zstd 1.5.6 --fast=1 2.437 545 MB/s 1890 MB/s
zstd 1.5.6 --fast=3 2.239 650 MB/s 2000 MB/s
quicklz 1.5.0 -1 2.238 525 MB/s 750 MB/s
lzo1x 2.10 -1 2.106 650 MB/s 825 MB/s
lz4 1.9.4 2.101 700 MB/s 4000 MB/s
lzf 3.6 -1 2.077 420 MB/s 830 MB/s
snappy 1.1.9 2.073 530 MB/s 1660 MB/s

The negative compression levels, specified with --fast=#, offer faster compression and decompression speed at the cost of compression ratio.

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

make is the officially maintained build system of this project. All other build systems are "compatible" and 3rd-party maintained, they may feature small differences in advanced options. When your system allows it, prefer using make to build zstd and libzstd.

Makefile

If your system is compatible with standard make (or gmake), invoking make in root directory will generate zstd cli in root directory. It will also create libzstd into lib/.

Other available options include:

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

The Makefile follows the GNU Standard Makefile conventions, allowing staged install, standard flags, directory variables and command variables.

For advanced use cases, specialized compilation flags which control binary generation are documented in lib/README.md for the libzstd library and in programs/README.md for the zstd CLI.

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.

Support for Fat (Universal2) Output

zstd can be built and installed with support for both Apple Silicon (M1/M2) as well as Intel by using CMake's Universal2 support. To perform a Fat/Universal2 build and install use the following commands:

cmake -B build-cmake-debug -S build/cmake -G Ninja -DCMAKE_OSX_ARCHITECTURES="x86_64;x86_64h;arm64"
cd build-cmake-debug
ninja
sudo ninja install

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/.

Bazel

You easily can integrate zstd into your Bazel project by using the module hosted on the Bazel Central Repository.

Testing

You can run quick local smoke tests by running make check. If you can't use make, execute 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 and many other large cloud infrastructures. It is run 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 OR GPLv2.

Contributing

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