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<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).

Zstandard's format is stable and documented in [RFC8878](https://datatracker.ietf.org/doc/html/rfc8878). Multiple independent implementations are already available.
This repository represents the reference implementation, 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](https://facebook.github.io/zstd/#other-languages).

**Development branch status:**

[![Build Status][travisDevBadge]][travisLink]
[![Build status][CircleDevBadge]][CircleLink]
[![Build status][CirrusDevBadge]][CirrusLink]
[![Fuzzing Status][OSSFuzzBadge]][OSSFuzzLink]

[travisDevBadge]: https://api.travis-ci.com/facebook/zstd.svg?branch=dev "Continuous Integration test suite"
[travisLink]: https://travis-ci.com/facebook/zstd
[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 desktop running Ubuntu 20.04 (`Linux 5.11.0-41-generic`),
with a Core i7-9700K CPU @ 4.9GHz,
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]: https://sun.aei.polsl.pl//~sdeor/index.php?page=silesia
[gcc]: https://gcc.gnu.org/

| Compressor name         | Ratio | Compression| Decompress.|
| ---------------         | ------| -----------| ---------- |
| **zstd 1.5.1 -1**       | 2.887 |   530 MB/s |  1700 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 |   450 MB/s |
| **zstd 1.5.1 --fast=1** | 2.437 |   600 MB/s |  2150 MB/s |
| **zstd 1.5.1 --fast=3** | 2.239 |   670 MB/s |  2250 MB/s |
| quicklz 1.5.0 -1        | 2.238 |   540 MB/s |   760 MB/s |
| **zstd 1.5.1 --fast=4** | 2.148 |   710 MB/s |  2300 MB/s |
| lzo1x 2.10 -1           | 2.106 |   660 MB/s |   845 MB/s |
| [lz4] 1.9.3             | 2.101 |   740 MB/s |  4500 MB/s |
| lzf 3.6 -1              | 2.077 |   410 MB/s |   830 MB/s |
| snappy 1.1.9            | 2.073 |   550 MB/s |  1750 MB/s |

[zlib]: https://www.zlib.net/
[lz4]: https://lz4.github.io/lz4/

The negative compression levels, specified with `--fast=#`,
offer faster compression and decompression speed
at the cost of compression ratio (compared to level 1).

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](doc/images/CSpeed2.png "Compression Speed vs Ratio") | ![Decompression Speed](doc/images/DSpeed3.png "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 ...

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.