As a reference, several fast compression algorithms were tested and compared to [zlib] 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].
Zstd can also offer stronger compression ratio 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].
The following test is 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 above chart is applicable to large files or large streams scenarios (200 MB in this case).
Small data (<64KB)comewithdifferentperspectives.
The smaller the amount of data to compress, the more difficult it is to achieve any significant compression.
On reaching the 1 KB region, it becomes almost impossible to compress anything.
This problem is common to all compression algorithms, and throwing CPU power at it achieves no significant gains.
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.
[Starting with 0.5](https://github.com/Cyan4973/zstd/releases), Zstd now offers [a _Dictionary Builder_ tool](https://github.com/Cyan4973/zstd/tree/master/dictBuilder).
It can be used to train the algorithm to fit a selected type of data, by providing it with some samples.
The result is a file (or a byte buffer) called "dictionary", which can be loaded before compression and decompression.
By using this dictionary, the compression ratio achievable on small data improves dramatically :
| Collection Name | Direct compression | Dictionary Compression | Gains | Average unit | Range |
Zstd has not yet reached "stable format" status. It doesn't guarantee yet that its current compression format will remain stable in future versions. During this period, it can still change to adapt new optimizations still being investigated. "Stable Format" is projected H1 2016, and will be tagged `v1.0`.
That being said, the library is now fairly robust, able to withstand hazards situations, including invalid inputs. It also features legacy support, so that documents compressed with current and previous version of zstd can still be decoded in the future.
Library reliability has been tested using [Fuzz Testing](https://en.wikipedia.org/wiki/Fuzz_testing), with both [internal tools](programs/fuzzer.c) and [external ones](http://lcamtuf.coredump.cx/afl). Therefore, Zstandard is considered safe for testings, even within production environments.
The "dev" branch is the one where all contributions will be 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.
Zstd entropy stage is provided by [Huff0 and FSE, from Finite State Entropy library](https://github.com/Cyan4973/FiniteStateEntropy).
Its memory requirement can be configured to fit into low-memory hardware configurations, or servers handling multiple connections/contexts in parallel.
