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back to listing indexfacebook/zstd: Zstandard - Fast real-time compression algorithm
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README.md
For reference, several fast compression algorithms were tested and compared
on a server running Arch Linux (Linux version 5.0.5-arch1-1),
with a Core i9-9900K CPU @ 5.0GHz,
using lzbench, an open-source in-memory benchmark by @inikep
compiled with gcc 8.2.1,
on the Silesia compression corpus.
| Compressor name | Ratio | Compression | Decompress. |
|---|---|---|---|
| zstd 1.4.4 -1 | 2.884 | 520 MB/s | 1600 MB/s |
| zlib 1.2.11 -1 | 2.743 | 110 MB/s | 440 MB/s |
| brotli 1.0.7 -0 | 2.701 | 430 MB/s | 470 MB/s |
| quicklz 1.5.0 -1 | 2.238 | 600 MB/s | 800 MB/s |
| lzo1x 2.09 -1 | 2.106 | 680 MB/s | 950 MB/s |
| lz4 1.8.3 | 2.101 | 800 MB/s | 4220 MB/s |
| snappy 1.1.4 | 2.073 | 580 MB/s | 2020 MB/s |
| lzf 3.6 -1 | 2.077 | 440 MB/s | 930 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 4.14.0-3-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 | |||
|---|---|---|---|---|
 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
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