commit | f299fa39ac07b9de897a5f0e930459528fec0bb5 | [log] [tgz] |
---|---|---|
author | Yann Collet <cyan@fb.com> | Tue Dec 19 09:43:03 2017 +0100 |
committer | Yann Collet <cyan@fb.com> | Tue Dec 19 09:43:03 2017 +0100 |
tree | 70de8c635a1de7966e8192aa7c40d587cac1eb64 | |
parent | 569e06b91e1cdbf1a6b275cf18a3d0667efbd1aa [diff] |
fix a subtle issue in continue mode The deep fuzzer tests caught a subtle bug that was probably there for a long time. The impact of the bug is not a crash, or any other clear error signal, rather, it reduces performance, by cutting data into smaller blocks. Eventually, the following test would fail because it produces too many 1-byte blocks, requiring more space than buffer can provide : `./zstreamtest_asan --mt -s3514 -t1678312 -i1678314` The root scenario is as follows : - Create context, initialize it using explicit parameters or a `cdict` to pin them down, set `pledgedSrcSize=1` - The compression parameters will not be adapted, but `windowSize` and `blockSize` will be automatically set to `1`. `windowSize` and `blockSize` are dynamic values, set within `ZSTD_resetCCtx_internal()`. The automatic adaptation makes it possible to generate smaller contexts for smaller input sizes. - Complete compression - New compression with same context, using same parameters, but `pledgedSrcSize=ZSTD_CONTENTSIZE_UNKNOWN` trigger "continue mode" - Continue mode doesn't modify blockSize, because it used to depend on `windowLog` only, but in fact, it also depends on `pledgedSrcSize`. - The "old" blocksize (1) is still there, next compression will use this value to cut input into blocks, resulting in more blocks and worse performance than necessary performance. Given the scenario, and its possible variants, I'm surprised it did not show up before. But I suspect it did show up, it's just that it never triggered an error, because "worse performance" is not a trigger. The above test is a special corner case, where performance is so impacted that it reaches an error case. The fix works, but I'm not completely pleased. I think the current code relies too much on implied relations between variables. This will likely break again in the future when some related part of the code change. Unfortunately, no time to make larger changes if we want to keep the release target for zstd v1.3.3. So a longer term fix will have to be considered after the release. To do : create a reliable test case which triggers this scenario for CI tests.
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.
The project is provided as an open-source BSD-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.
For reference, several fast compression algorithms were tested and compared on a server running Linux Debian (Linux version 4.8.0-1-amd64
), with a Core i7-6700K CPU @ 4.0GHz, using lzbench, an open-source in-memory benchmark by @inikep compiled with GCC 6.3.0, on the Silesia compression corpus.
Compressor name | Ratio | Compression | Decompress. |
---|---|---|---|
zstd 1.1.3 -1 | 2.877 | 430 MB/s | 1110 MB/s |
zlib 1.2.8 -1 | 2.743 | 110 MB/s | 400 MB/s |
brotli 0.5.2 -0 | 2.708 | 400 MB/s | 430 MB/s |
quicklz 1.5.0 -1 | 2.238 | 550 MB/s | 710 MB/s |
lzo1x 2.09 -1 | 2.108 | 650 MB/s | 830 MB/s |
lz4 1.7.5 | 2.101 | 720 MB/s | 3600 MB/s |
snappy 1.1.3 | 2.091 | 500 MB/s | 1650 MB/s |
lzf 3.6 -1 | 2.077 | 400 MB/s | 860 MB/s |
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 4.8.0-1-amd64
) with a Core i7-6700K CPU @ 4.0GHz, using lzbench, an open-source in-memory benchmark by @inikep compiled with GCC 6.3.0, 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.
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.
zstd --train FullPathToTrainingSet/* -o dictionaryName
zstd -D dictionaryName FILE
zstd -D dictionaryName --decompress FILE.zst
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 pagesmake check
: create and run zstd
, tests its behavior on local platformA 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.
A Meson project is provided within contrib/meson
.
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.Zstandard is currently deployed within Facebook. It is used 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 and GPLv2.
The "dev" branch is the one where all contributions are merged before reaching "master". If you plan to propose a patch, please commit into the "dev" branch, or its own feature branch. Direct commit to "master" are not permitted. For more information, please read CONTRIBUTING.