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MT compression generates a single frame. Multi-threading operates by breaking the frames into independent sections. But from a decoder perspective, there is no difference : it's just a suite of blocks. Problem is, decoder preserves repCodes from previous block to start decoding next block. This is also valid between sections, since they are no different than changing block. Previous version would incorrectly initialize repcodes to their default value at the beginning of each section. When using them, there was a mismatch between encoder (default values) and decoder (values from previous block). This change ensures that repcodes won't be used at the beginning of a new section. It works by setting them to 0. This only works with regular (single segment) variants : extDict variants will fail ! Fortunately, sections beyond the 1st one belong to this category. To be checked : btopt strategy. This change was only validated from fast to btlazy2 strategies. |
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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 is provided as an open-source BSD-licensed C library,
and a command line utility producing and decoding .zst compressed files.
For other programming languages,
you can consult a list of known ports on Zstandard homepage.
| Branch | Status |
|---|---|
| master |  |
| dev |  |
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 deflate -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 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 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 |
|---|---|
 |
 |
Several algorithms can produce higher compression ratios, but at slower speeds, 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 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 achieve any significant compression.
This problem is common to many compression algorithms. 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:
These compression gains are achieved while simultaneously providing faster compression and decompression speeds.
Dictionary works 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 greatest 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 :
- Create the dictionary
zstd --train FullPathToTrainingSet/* -o dictionaryName
- Compress with dictionary
zstd FILE -D dictionaryName
- Decompress with dictionary
zstd --decompress FILE.zst -D dictionaryName
Build
Once you have the repository cloned, there are multiple ways provided to build Zstandard.
Makefile
If your system is compatible with a standard make (or gmake) binary generator,
you can simply run it at the root directory.
It will generate zstd within root directory.
Other available options include :
make install: create and install zstd binary, library and man pagemake test: create and runzstdand test tools 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.
Visual (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 buildzstdcli andlibzstdlibrary without any need to open Visual Studio solution.
Status
Zstandard is currently deployed within Facebook. It is used daily to compress and decompress very large amounts 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.