ddidderr
caf12dda22
Move the dictionary builder's suffix-array construction from
lib/dictBuilder/divsufsort.c to rust/src/divsufsort.rs, the first
dictBuilder module to migrate. It rides on the dict-builder cargo
feature dimension introduced by the previous commit.
divsufsort() is a self-contained algorithm (two-stage sort of type-B*
substrings via sssort, rank refinement via trsort, then induced sorting
of the full array), so its context-free signature allows a direct symbol
takeover: the Rust #[no_mangle] export provides the existing `divsufsort`
symbol and the C file becomes a declaration-only shim that just keeps the
header's prototypes in the build. Only divsufsort() moved; divbwt() has
no callers anywhere in zstd, so it is now declaration-only, keeping the
Rust export surface minimal. The unused openMP parameter is retained for
signature compatibility (zstd never defines LIBBSC_OPENMP).
The port is a mechanical translation of the exact configuration zstd
compiles: ALPHABET_SIZE=256, SS_INSERTIONSORT_THRESHOLD=8,
SS_BLOCKSIZE=1024, SS_MISORT_STACKSIZE=16, SS_SMERGE_STACKSIZE=32,
TR_STACKSIZE=64. Every C `int*` cursor into the SA buffer becomes an
`isize` index into a single `&mut [i32]` slice, preserving the pointer
arithmetic (including transient one-before-the-range cursors and the
bitwise-complement rank marking) while staying bounds-checked; all value
arithmetic keeps C int semantics. The C -1/-2 error results are
preserved, with Vec::try_reserve_exact standing in for the bucket-array
malloc failure path. Behavior is bit-identical by construction and by
measurement (see test plan); runtime on an 11 MB training buffer is
within ~5% of the C build end-to-end.
Users see no behavioral change: dictionaries trained through
ZDICT_trainFromBuffer_legacy() are byte-identical to the C build. The
only external difference is that the never-called `divbwt` symbol is no
longer defined in the library.
Test plan:
- cd rust && cargo fmt --check && cargo clippy --all-targets
-- -D warnings && cargo test --all-targets && cargo build --release
(125 tests pass; new unit tests cover empty/one/two-byte inputs,
all-equal bytes, an exact hand-computed "abracadabra" SA, and
fixed-seed LCG buffers at 256-, 4-, and 2-symbol alphabets verified
against a naive reference sort plus permutation/sorted invariants)
- Feature matrix: cargo build --release --no-default-features
--features compression,decompression (and decompression-only,
compression-only, compression,dict-builder); `divsufsort` is exported
only when dict-builder is enabled
- make -C tests fuzzer && ./tests/fuzzer -i1 --no-big-tests (includes
ZDICT training tests): pass
- make -C tests test-rust-lib-smoke: pass
- make -C tests test-invalidDictionaries: pass
- make -C programs zstd zstd-dictBuilder zstd-small zstd-compress
zstd-decompress: build; compress/decompress round-trip verified
- Byte-identity vs pristine C build (commit 959e4852): a harness calling
ZDICT_trainFromBuffer_legacy() (the only zstd path reaching
divsufsort) and divsufsort() directly, linked against both libzstd.a
builds, produces byte-identical dictionaries (80,288 B and full
112,640 B capacity) and byte-identical suffix arrays on a 1 MB source
set and an 11 MB binary/repetitive set; a differential driver over 148
random and structured buffers (sizes 3..6000, alphabets 1..256,
Fibonacci word, sawtooth, 6 KB near-constant) shows zero mismatches.
The CLI --train path could not be exercised because the Rust CLI
frontend rejects --train in both the pristine and ported builds (a
pre-existing migration gap unrelated to this change).
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:
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 20.04 (Linux ubu20 5.15.0-101-generic),
using lzbench, an open-source in-memory benchmark by @inikep
compiled with gcc 9.4.0,
on the Silesia compression corpus.
| Compressor name | Ratio | Compression | Decompress. |
|---|---|---|---|
| zstd 1.5.6 -1 | 2.887 | 510 MB/s | 1580 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 | 430 MB/s |
| zstd 1.5.6 --fast=1 | 2.437 | 545 MB/s | 1890 MB/s |
| zstd 1.5.6 --fast=3 | 2.239 | 650 MB/s | 2000 MB/s |
| quicklz 1.5.0 -1 | 2.238 | 525 MB/s | 750 MB/s |
| lzo1x 2.10 -1 | 2.106 | 650 MB/s | 825 MB/s |
| lz4 1.9.4 | 2.101 | 700 MB/s | 4000 MB/s |
| lzf 3.6 -1 | 2.077 | 420 MB/s | 830 MB/s |
| snappy 1.1.9 | 2.073 | 530 MB/s | 1660 MB/s |
The negative compression levels, specified with --fast=#,
offer faster compression and decompression speed
at the cost of compression ratio.
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.
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, created from github public API.
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:
-
Create the dictionary
zstd --train FullPathToTrainingSet/* -o dictionaryName -
Compress with dictionary
zstd -D dictionaryName FILE -
Decompress with dictionary
zstd -D dictionaryName --decompress FILE.zst
Build instructions
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.
Makefile
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 runzstd, test its behavior on local platform
The 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.
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.
Support for Fat (Universal2) Output
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
Meson
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.
VCPKG
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.
Conan
You can install pre-built binaries for zstd or build it from source using Conan. Use the following command:
conan install --requires="zstd/[*]" --build=missing
The zstd Conan recipe is kept up to date by Conan maintainers and community contributors. If the version is out of date, please create an issue or pull request on the ConanCenterIndex repository.
Visual Studio (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, VS2015 and VS2017.
- Automated build scripts for Visual compiler by @KrzysFR, in
build/VS_scripts, which will buildzstdcli andlibzstdlibrary without any need to open Visual Studio solution.
Buck
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/.
Bazel
You easily can integrate zstd into your Bazel project by using the module hosted on the Bazel Central Repository.
Testing
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.
Status
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.
License
Zstandard is dual-licensed under BSD OR GPLv2.
Contributing
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.