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Merge pull request #346 from brendankirby/dev

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Yann Collet 2016-09-06 03:52:59 +02:00 committed by GitHub
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@ -10,7 +10,7 @@ you can consult a list of known ports on [Zstandard homepage](http://www.zstd.ne
|master | [![Build Status](https://travis-ci.org/facebook/zstd.svg?branch=master)](https://travis-ci.org/facebook/zstd) |
|dev | [![Build Status](https://travis-ci.org/facebook/zstd.svg?branch=dev)](https://travis-ci.org/facebook/zstd) |
As a reference, several fast compression algorithms were tested and compared on a Core i7-3930K CPU @ 4.5GHz, using [lzbench], an open-source in-memory benchmark by @inikep compiled with gcc 5.4.0, with the [Silesia compression corpus].
As a reference, several fast compression algorithms were tested and compared on a Core i7-3930K CPU @ 4.5GHz, using [lzbench], an open-source in-memory benchmark by @inikep compiled with GCC 5.4.0, with the [Silesia compression corpus].
[lzbench]: https://github.com/inikep/lzbench
[Silesia compression corpus]: http://sun.aei.polsl.pl/~sdeor/index.php?page=silesia
@ -32,32 +32,32 @@ As a reference, several fast compression algorithms were tested and compared on
[LZ4]: http://www.lz4.org/
Zstd can also offer stronger compression ratios at the cost of compression speed.
Speed vs Compression trade-off 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.
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 Core i7-3930K CPU @ 4.5GHz, using [lzbench], an open-source in-memory benchmark by @inikep compiled with gcc 5.2.1, on the [Silesia compression corpus].
The following tests were run on a Core i7-3930K CPU @ 4.5GHz, using [lzbench], an open-source in-memory benchmark by @inikep compiled with GCC 5.2.1, on the [Silesia compression corpus].
Compression Speed vs Ratio | Decompression Speed
---------------------------|--------------------
![Compression Speed vs Ratio](images/Cspeed4.png "Compression Speed vs Ratio") | ![Decompression Speed](images/Dspeed4.png "Decompression Speed")
Several algorithms can produce higher compression ratio but at slower speed, falling outside of the graph.
Several algorithms can produce higher compression ratios, but at slower speeds, falling outside of the graph.
For a larger picture including very slow modes, [click on this link](images/DCspeed5.png) .
### The case for Small Data compression
Previous charts provide results applicable to typical files and streams scenarios (several MB). Small data come with different perspectives. The smaller the amount of data to compress, the more difficult it is to achieve any significant 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 achieve any significant compression.
This problem is common to any compression algorithm. The reason is, compression algorithms learn from past data how to compress future data. But at the beginning of a new file, 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, by providing it with a few samples. The result of the training is stored in a file called "dictionary", which can be loaded before compression and decompression. Using this dictionary, the compression ratio achievable on small data improves dramatically :
To solve this situation, Zstd offers a __training mode__, which can be used to tune the algorithm for a selected type of data, by providing it with a few samples. The result of the training is stored in a file called "dictionary", which can be loaded before compression and decompression. Using this dictionary, the compression ratio achievable on small data improves dramatically:
![Compressing Small Data](images/smallData.png "Compressing Small Data")
These compression gains are achieved while simultaneously providing faster compression and decompression speeds.
Dictionary work if there is some correlation in a family of small data (there is no _universal dictionary_).
Hence, deploying one dictionary per type of data will provide the greater benefits. Dictionary gains are mostly effective in the first few KB. Then, the compression algorithm will rely more and more on previously decoded content to compress the rest of the file.
Dictionary works if there is some correlation in a family of small data (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 rely more and more on previously decoded content to compress the rest of the file.
#### Dictionary compression How To :
@ -75,7 +75,7 @@ Hence, deploying one dictionary per type of data will provide the greater benefi
### Status
Zstandard is currently deployed within Facebook. It is used daily to compress and decompress very large amount of data in multiple formats and use cases.
Zstandard is currently deployed within Facebook. It is used daily to compress and decompress very large amounts of data in multiple formats and use cases.
Zstandard is considered safe for production environments.
### License