ZstandardWatch Introductory Video
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 24.04 (Linux 6.8.0-53-generic),
using lzbench, an open-source in-memory benchmark by @inikep
compiled with gcc 14.2.0,
on the Silesia compression corpus.
| Compressor name | Ratio | Compression | Decompress. |
|---|---|---|---|
| zstd 1.5.7 -1 | 2.896 | 510 MB/s | 1550 MB/s |
| zlib 1.3.1 -1 | 2.743 | 105 MB/s | 390 MB/s |
| brotli 1.1.0 -0 | 2.702 | 400 MB/s | 425 MB/s |
| zstd 1.5.7 --fast=1 | 2.439 | 545 MB/s | 1850 MB/s |
| zstd 1.5.7 --fast=3 | 2.241 | 635 MB/s | 1980 MB/s |
| quicklz 1.5.0 -1 | 2.238 | 520 MB/s | 750 MB/s |
| lzo1x 2.10 -1 | 2.106 | 650 MB/s | 780 MB/s |
| lz4 1.10.0 | 2.101 | 675 MB/s | 3850 MB/s |
| snappy 1.2.1 | 2.089 | 520 MB/s | 1500 MB/s |
| lzf 3.6 -1 | 2.077 | 410 MB/s | 820 MB/s |
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 trade compression speed for stronger compression ratios. It is configurable by small increment. Decompression speed is preserved and remain 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 |
|---|---|
 |
 |
Several algorithms can produce higher compression ratio but at slower speed, 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 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 provide 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 weighting 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.
A rich API set :
Zstandard API is designed with learning curve in mind. At the top, you'll find simple methods, using trivial arguments and behavior. Then, at each new paragraph, the API introduces new concepts and parameters, giving gradually more control for advanced usages.
You can learn more about Zstandard API by reading its documentation.
Multiple programming languages
Should you need Zstandard in different language than reference C, here is a list of known bindings and ports provided by great authors. All versions support the Zstandard format specification and are therefore interoperable. Many versions listed below are bindings to the reference C library. There are also full re-implementation of the algorithm in the target language which are explicitly labelled as Ports.
Graphical User Interfaces
Tino Reichardt has created a version of 7-zip archive manager with Zstandard.
Denis Anisimov, of TC4shell fame, also developed a 7-zip plugin with Zstandard support.
Peazip is a free multi-platforms archiver by Giorgio Tani with support for Zstandard
WinRAR supports decoding of Zstandard files (as well as zip files using Zstandard encoder)
