cmake-3.21.1.tar.gz

<p align="center"><img src="https://raw.githubusercontent.com/facebook/zstd/dev/doc/images/zstd_logo86.png" alt="Zstandard"></p> __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](https://github.com/Cyan4973/FiniteStateEntropy). The project is provided as an open-source dual [BSD](LICENSE) and [GPLv2](COPYING) 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](http://www.zstd.net/#other-languages). **Development branch status:** [![Build Status][travisDevBadge]][travisLink] [![Build status][AppveyorDevBadge]][AppveyorLink] [![Build status][CircleDevBadge]][CircleLink] [![Build status][CirrusDevBadge]][CirrusLink] [![Fuzzing Status][OSSFuzzBadge]][OSSFuzzLink] [travisDevBadge]: https://travis-ci.org/facebook/zstd.svg?branch=dev "Continuous Integration test suite" [travisLink]: https://travis-ci.org/facebook/zstd [AppveyorDevBadge]: https://ci.appveyor.com/api/projects/status/xt38wbdxjk5mrbem/branch/dev?svg=true "Windows test suite" [AppveyorLink]: https://ci.appveyor.com/project/YannCollet/zstd-p0yf0 [CircleDevBadge]: https://circleci.com/gh/facebook/zstd/tree/dev.svg?style=shield "Short test suite" [CircleLink]: https://circleci.com/gh/facebook/zstd [CirrusDevBadge]: https://api.cirrus-ci.com/github/facebook/zstd.svg?branch=dev [CirrusLink]: https://cirrus-ci.com/github/facebook/zstd [OSSFuzzBadge]: https://oss-fuzz-build-logs.storage.googleapis.com/badges/zstd.svg [OSSFuzzLink]: https://bugs.chromium.org/p/oss-fuzz/issues/list?sort=-opened&can=1&q=proj:zstd ## 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]. [lzbench]: https://github.com/inikep/lzbench [Silesia compression corpus]: http://sun.aei.polsl.pl/~sdeor/index.php?page=silesia [gcc]: https://gcc.gnu.org/ | Compressor name | Ratio | Compression| Decompress.| | --------------- | ------| -----------| ---------- | | **zstd 1.4.5 -1** | 2.884 | 500 MB/s | 1660 MB/s | | zlib 1.2.11 -1 | 2.743 | 90 MB/s | 400 MB/s | | brotli 1.0.7 -0 | 2.703 | 400 MB/s | 450 MB/s | | **zstd 1.4.5 --fast=1** | 2.434 | 570 MB/s | 2200 MB/s | | **zstd 1.4.5 --fast=3** | 2.312 | 640 MB/s | 2300 MB/s | | quicklz 1.5.0 -1 | 2.238 | 560 MB/s | 710 MB/s | | **zstd 1.4.5 --fast=5** | 2.178 | 700 MB/s | 2420 MB/s | | lzo1x 2.10 -1 | 2.106 | 690 MB/s | 820 MB/s | | lz4 1.9.2 | 2.101 | 740 MB/s | 4530 MB/s | | **zstd 1.4.5 --fast=7** | 2.096 | 750 MB/s | 2480 MB/s | | lzf 3.6 -1 | 2.077 | 410 MB/s | 860 MB/s | | snappy 1.1.8 | 2.073 | 560 MB/s | 1790 MB/s | [zlib]: http://www.zlib.net/ [LZ4]: http://www.lz4.org/ 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 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](doc/images/DCspeed5.png). ## 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](https://github.com/facebook/zstd/releases/tag/v1.1.3), created from [github public API](https://developer.github.com/v3/users/#get-all-users). It consists of roughly 10K records weighing about 1KB each. 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 ### 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`](build/meson). Follow build instructions in that directory. You can also take a look at [`.travis.yml`](.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](https://github.com/Microsoft/vcpkg/) dependency manager: git clone https://github.com/Microsoft/vcpkg.git cd vcpkg ./bootstrap-vcpkg.sh ./vcpkg integrate install ./vcpkg install zstd The