In these cases, performance is often affected by overhead, such as checksumming. While pure algorithm performance is important when compression is embedded within a larger application, it is extremely common to also use command line tools for compression - say, for compressing log files, tarballs, or other similar data meant for storage or transfer. However, Zstandard, at the default setting, shows substantial improvements in both compression speed and decompression speed, while compressing at the same ratio as zlib. The fastest algorithm, lz4, results in lower compression ratios xz, which has the highest compression ratio, suffers from a slow compression speed. The table below indicates the rough trade-offs of the algorithms' default compression ratio and speed for the Silesia corpus by comparing the algorithms per lzbench, a pure in-memory benchmark meant to model raw algorithm performance.Īs outlined, there are often drastic compromises between speed and size. Each of these algorithms offers different trade-offs: lz4 aims for speed, xz aims for higher compression ratios, and zlib aims for a good balance of speed and size. Some algorithms and implementations commonly used today are zlib, lz4, and xz. To represent the algorithms that Zstandard is expected to work on, in this post we'll use the Silesia corpus, a data set of files that represent the typical data types used every day. However, Zstandard, like zlib, is meant for general-purpose compression for a variety of data types. The type of data being compressed can affect these metrics, so many algorithms are tuned for specific types of data, such as English text, genetic sequences, or rasterized images. Decompression speed: How quickly we can reconstruct the original data from the compressed data, measured in MB/s for the rate at which data is produced from compressed data.Compression speed: How quickly we can make the data smaller, measured in MB/s of input data consumed.Compression ratio: The original size (numerator) compared with the compressed size (denominator), measured in unitless data as a size ratio of 1.0 or greater.There are three standard metrics for comparing compression algorithms and implementations: Zstandard, available now under the BSD license, is designed to be used in nearly every lossless compression scenario, including many where current algorithms aren't applicable. ![]() As a result, it improves upon the trade-offs made by other compression algorithms and has a wide range of applicability with very high decompression speed. Zstandard combines recent compression breakthroughs, like Finite State Entropy, with a performance-first design - and then optimizes the implementation for the unique properties of modern CPUs. We're thrilled to announce Zstandard 1.0, a new compression algorithm and implementation designed to scale with modern hardware and compress smaller and faster. Over the years, other algorithms have offered either better compression or faster compression, but rarely both. For two decades, it has provided an impressive balance between speed and space, and, as a result, it is used in almost every modern electronic device (and, not coincidentally, used to transmit every byte of the very blog post you are reading). Today, the reigning data compression standard is Deflate, the core algorithm inside Zip, gzip, and zlib. The more time spent compressing to a smaller file, the slower the data is to process. There's a trade-off to compressing and decompressing information, though: time. ![]() These smaller files take up less space on hard drives and are transmitted faster to other systems. Software helps with data processing through compression, which encodes information, like text, pictures, and other forms of digital data, using fewer bits than the original. ![]() When it comes to innovating on storing and transmitting that data, at Facebook we're making advancements not only in hardware - such as larger hard drives and faster networking equipment - but in software as well. People are creating, sharing, and storing data at a faster rate than at any other time in history.
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