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gerrit-public.fairphone.software / platform / external / zstd / 2126ca8a5f3a4cb2a19c5b2923f4f9bb7f41f630
commit2126ca8a5f3a4cb2a19c5b2923f4f9bb7f41f630[log] [tgz]
authorYann Collet <cyan@fb.com>Wed Dec 27 17:49:18 2017 +0100
committerYann Collet <cyan@fb.com>Wed Dec 27 17:58:27 2017 +0100
treebcc52fbf26408d78d13c1db23d836175b1d251f4
parent88798028660dd233e8a9c179028e42daf2efe68b [diff]
%.o objects files in /tests

Recipe in /tests rebuild everything from source for each target.
zstd is still a "small" project, so it's not prohibitive,
yet, rebuilding same files over and over represents substantial redundant work.

This patch replaces *.c files from /lib by their corresponding *.o files.
They cannot be compiled and stored directly within /lib,
since /tests triggers additional debug capabilities unwelcome in release binary.
So the resulting *.o are stored directly within /tests.

It turns out, it's difficult to find several target using *exactly* the same rules.
Using only the default rules (debug enabled, multi-threading disabled, no legacy)
a surprisingly small amount of targets share their work.

It's because, in many cases there are additional modifications requested :
some targets are 32-bits, some enable multi-threading, some enable legacy support,
some disable asserts, some want different kind of sanitizer, etc.

I created 2 sets of object files : with and without multithreading.
Several targets share their work, saving compilation time when running `make all`.
Also, obviously, when modifying one source file, only this one needs rebuilding.

For targets requiring some different setting, build from source *.c remain the rule.

The new rules have been tested within `-j` parallel compilation, and work fine with it.
2 files changed
tree: bcc52fbf26408d78d13c1db23d836175b1d251f4
  1. build/
  2. contrib/
  3. doc/
  4. examples/
  5. lib/
  6. programs/
  7. tests/
  8. zlibWrapper/
  9. .buckconfig
  10. .buckversion
  11. .gitattributes
  12. .gitignore
  13. .travis.yml
  14. appveyor.yml
  15. circle.yml
  16. CONTRIBUTING.md
  17. COPYING
  18. LICENSE
  19. Makefile
  20. NEWS
  21. README.md
  22. 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 BSD-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

Benchmarks

For reference, several fast compression algorithms were tested and compared on a server running Linux Debian (Linux version 4.8.0-1-amd64), with a Core i7-6700K CPU @ 4.0GHz, using lzbench, an open-source in-memory benchmark by @inikep compiled with GCC 6.3.0, on the Silesia compression corpus.

Compressor nameRatioCompressionDecompress.
zstd 1.1.3 -12.877430 MB/s1110 MB/s
zlib 1.2.8 -12.743110 MB/s400 MB/s
brotli 0.5.2 -02.708400 MB/s430 MB/s
quicklz 1.5.0 -12.238550 MB/s710 MB/s
lzo1x 2.09 -12.108650 MB/s830 MB/s
lz4 1.7.52.101720 MB/s3600 MB/s
snappy 1.1.32.091500 MB/s1650 MB/s
lzf 3.6 -12.077400 MB/s860 MB/s

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 4.8.0-1-amd64) with a Core i7-6700K CPU @ 4.0GHz, using lzbench, an open-source in-memory benchmark by @inikep compiled with GCC 6.3.0, 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

  1. Compress with dictionary

zstd -D dictionaryName FILE

  1. 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.

Meson

A Meson project is provided within contrib/meson.

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 and VS2015.
  • 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.

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.