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.
Zstandard's format is stable and documented in RFC8878. Multiple independent implementations are already available.
This repository represents the reference implementation, provided as an open-source dual BSD OR GPLv2 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:
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.
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 |
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 |
---|---|
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![]() |
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.
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 Ratio | Compression Speed | Decompression Speed |
---|---|---|
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![]() |
![]() |
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.
Create the dictionary
zstd --train FullPathToTrainingSet/* -o dictionaryName
Compress with dictionary
zstd -D dictionaryName FILE
Decompress with dictionary
zstd -D dictionaryName --decompress FILE.zst
make
is the officially maintained build system of this project.
All other build systems are "compatible" and 3rd-party maintained,
they may feature small differences in advanced options.
When your system allows it, prefer using make
to build zstd
and libzstd
.
If your system is compatible with standard make
(or gmake
),
invoking make
in root directory will generate zstd
cli in root directory.
It will also create libzstd
into lib/
.
Other available options include:
make install
: create and install zstd cli, library and man pagesmake check
: create and run zstd
, test its behavior on local platformThe Makefile
follows the GNU Standard Makefile conventions,
allowing staged install, standard flags, directory variables and command variables.
For advanced use cases, specialized compilation flags which control binary generation
are documented in lib/README.md
for the libzstd
library
and in programs/README.md
for the zstd
CLI.
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
.
zstd
can be built and installed with support for both Apple Silicon (M1/M2) as well as Intel by using CMake's Universal2 support.
To perform a Fat/Universal2 build and install use the following commands:
cmake -B build-cmake-debug -S build/cmake -G Ninja -DCMAKE_OSX_ARCHITECTURES="x86_64;x86_64h;arm64"
cd build-cmake-debug
ninja
sudo ninja install
A Meson project is provided within build/meson
. Follow
build instructions in that directory.
You can also take a look at .travis.yml
file for an
example about how Meson is used to build this project.
Note that default build type is release.
You can build and install zstd vcpkg dependency manager:
git clone https://github.com/Microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.sh
./vcpkg integrate install
./vcpkg install zstd
The zstd port in vcpkg is kept up to date by Microsoft team members and community contributors. If the version is out of date, please create an issue or pull request on the vcpkg repository.
Going into build
directory, you will find additional possibilities:
build/VS_scripts
,
which will build zstd
cli and libzstd
library without any need to open Visual Studio solution.You can build the zstd binary via buck by executing: buck build programs:zstd
from the root of the repo.
The output binary will be in buck-out/gen/programs/
.
You easily can integrate zstd into your Bazel project by using the module hosted on the Bazel Central Repository.
You can run quick local smoke tests by running make check
.
If you can't use make
, execute the playTest.sh
script from the src/tests
directory.
Two env variables $ZSTD_BIN
and $DATAGEN_BIN
are needed for the test script to locate the zstd
and datagen
binary.
For information on CI testing, please refer to TESTING.md
.
Zstandard is currently deployed within Facebook and many other large cloud infrastructures. It is run continuously to compress large amounts of data in multiple formats and use cases. Zstandard is considered safe for production environments.
Zstandard is dual-licensed under BSD OR GPLv2.
The dev
branch is the one where all contributions are merged before reaching release
.
If you plan to propose a patch, please commit into the dev
branch, or its own feature branch.
Direct commit to release
are not permitted.
For more information, please read CONTRIBUTING.
link |
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Last commit: 4 days ago |
This is a quick fix release. The primary focus is to correct a rare corruption bug in high compression mode, detected by @danlark1 . The probability to generate such a scenario by random chance is extremely low. It evaded months of continuous fuzzer tests, due to the number and complexity of simultaneous conditions required to trigger it. Nevertheless, @danlark1 from Google shepherds such a humongous amount of data that he managed to detect a reproduction case (corruptions are detected thanks to the checksum), making it possible for @terrelln to investigate and fix the bug. Thanks !
While the probability might be very small, corruption issues are nonetheless very serious, so an update to this version is highly recommended, especially if you employ high compression modes (levels 16+).
When the issue was detected, there were a number of other improvements and minor fixes already in the making, hence they are also present in this release. Let’s detail the main ones.
--patch-from
modeV1.5.5
introduces memory-mapped dictionaries, by @daniellerozenblit, for both posix #3486 and windows #3557.
This feature allows zstd
to memory-map large dictionaries, rather than requiring to load them into memory. This can make a pretty big difference for memory-constrained environments operating patches for large data sets.
It's mostly visible under memory pressure, since mmap
will be able to release less-used memory and continue working.
But even when memory is plentiful, there are still measurable memory benefits, as shown in the graph below, especially when the reference turns out to be not completely relevant for the patch.
This feature is automatically enabled for --patch-from
compression/decompression when the dictionary is larger than the user-set memory limit. It can also be manually enabled/disabled using --mmap-dict
or --no-mmap-dict
respectively.
Additionally, @daniellerozenblit introduces significant speed improvements for --patch-from
.
An I/O
optimization in #3486 greatly improves --patch-from
decompression speed on Linux, typically by +50%
on large files (~1GB).
Compression speed is also taken care of, with a dictionary-indexing speed optimization introduced in #3545. It wildly accelerates --patch-from
compression, typically doubling speed on large files (~1GB), sometimes even more depending on exact scenario.
This speed improvement comes at a slight regression in compression ratio, and is therefore enabled only on non-ultra compression strategies.
The row-hash match finder introduced in version 1.5.0 for levels 5-12 has been improved in version 1.5.5, enhancing its speed in specific corner-case scenarios.
The first optimization (#3426) accelerates streaming compression using ZSTD_compressStream
on small inputs by removing an expensive table initialization step. This results in remarkable speed increases for very small inputs.
The following scenario measures compression speed of ZSTD_compressStream
at level 9 for different sample sizes on a linux platform running an i7-9700k cpu.
sample size | v1.5.4 (MB/s) |
v1.5.5 (MB/s) |
improvement |
---|---|---|---|
100 | 1.4 | 44.8 | x32 |
200 | 2.8 | 44.9 | x16 |
500 | 6.5 | 60.0 | x9.2 |
1K | 12.4 | 70.0 | x5.6 |
2K | 25.0 | 111.3 | x4.4 |
4K | 44.4 | 139.4 | x3.2 |
... | ... | ... | |
1M | 97.5 | 99.4 | +2% |
The second optimization (#3552) speeds up compression of incompressible data by a large multiplier. This is achieved by increasing the step size and reducing the frequency of matching when no matches are found, with negligible impact on the compression ratio. It makes mid-level compression essentially inexpensive when processing incompressible data, typically, already compressed data (note: this was already the case for fast compression levels).
The following scenario measures compression speed of ZSTD_compress
compiled with gcc-9
for a ~10MB incompressible sample on a linux platform running an i7-9700k cpu.
level | v1.5.4 (MB/s) |
v1.5.5 (MB/s) |
improvement |
---|---|---|---|
3 | 3500 | 3500 | not a row-hash level (control) |
5 | 400 | 2500 | x6.2 |
7 | 380 | 2200 | x5.8 |
9 | 176 | 1880 | x10 |
11 | 67 | 1130 | x16 |
13 | 89 | 89 | not a row-hash level (control) |
There are other welcome speed improvements in this package.
For example, @felixhandte managed to increase processing speed of small files by carefully reducing the nb of system calls (#3479). This can easily translate into +10% speed when processing a lot of small files in batch.
The Seekable format received a bit of care. It's now much faster when splitting data into very small blocks (#3544). In an extreme scenario reported by @P-E-Meunier, it improves processing speed by x90. Even for more "common" settings, such as using 4KB blocks on some "normally" compressible data like enwik
, it still provides a healthy x2 processing speed benefit. Moreover, @dloidolt merged an optimization that reduces the nb of I/O
seek()
events during reads (decompression), which is also beneficial for speed.
The release is not limited to speed improvements, several loose ends and corner cases were also fixed in this release. For a more detailed list of changes, please take a look at the changelog.
mmap
large dictionaries to save memory, by @daniellerozenblit--patch-from
mode (~+50%) (#3545) by @daniellerozenblitzstd
no longer crashes when requested to write into write-protected directory (#3541) by @felixhandte-o
(#3584, @Cyan4973) reported by @georgmucmake
does no longer require 3.18 as minimum version (#3510) by @koutests/fullbench
can benchmark multiple files (#3516) by @dloidoltf
-variants of chmod()
and chown()
by @felixhandte in https://github.com/facebook/zstd/pull/3479setvbuf()
on Null File Pointer by @felixhandte in https://github.com/facebook/zstd/pull/3541-std=c++11
When Default is Older by @felixhandte in https://github.com/facebook/zstd/pull/3574dest
is valid for decompression by @daniellerozenblit in https://github.com/facebook/zstd/pull/3555Full Changelog: https://github.com/facebook/zstd/compare/v1.5.4...v1.5.5
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