The net result is your mobile device loads pages faster, uses less battery, and consumes less of your data plan. Zstandard in particular suits the mobile scenarios much better than other algorithms because of how it handles small data. Possibly never. While seemingly counterintuitive, it is often the case that a piece of data — such as backups or log files — will never be decompressed but can be read if needed. On the rare occasion it does need to be decompressed, you don't want the compression to slow down the operational use case.
Fast decompression is beneficial because it is often a small part of the data such as a specific file in the backup or message in a log file that needs to be found quickly.
In all of these cases, Zstandard brings the ability to compress and decompress many times faster than gzip, with the resulting compressed data being smaller. There is another use case for compression that gets less attention but can be quite important: small data.
Data Compression/Evaluating Compression Effectiveness
These are use patterns where data is produced and consumed in small quantities, such as JSON messages between a web server and browser typically hundreds of bytes or pages of data in a database a few kilobytes. Databases provide an interesting use case. Recent hardware advantages, particularly around the proliferation of flash SSD devices, have fundamentally changed the balance between size and throughput — we now live in a world where IOPs IO operations per second are quite high, but the capacity of our storage devices is lower than it was when hard drives ruled the data center.
In addition, flash has an interesting property regarding write endurance — after thousands of writes to the same section of the device, that section can no longer accept writes, often leading to the device being removed from service. Therefore it is natural to seek out ways to reduce the quantity of data being written because it can mean more data per server and burning out the device at a slower rate. Data compression is a strategy for this, and databases also are often optimized for performance, meaning read and write performance are equally important.
There is a complication for using data compression with databases, though. Databases like to randomly access data, whereas most typical use cases for compression read an entire file in linear order. This is a problem because data compression essentially works by predicting the future based on the past — the algorithms look at your data sequentially and predict what it might see in the future.
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The more accurate the predictions, the smaller it can make the data. Compression algorithms have attempted to address this by using pre-shared dictionaries to effectively jump-start.
Minification: preprocessing & context-specific optimizations
This is done by pre-sharing a static set of "past" data as a seed for the compression. Zstandard builds on this approach with highly optimized algorithms and APIs for dictionary compression. In addition, Zstandard includes tooling zstd --train for easily making dictionaries for custom applications and provisions for registering standard dictionaries for sharing with larger communities. While compression varies based on the data samples, small data compression can range anywhere from 2x to 5x better than compression without dictionaries.
While it can be hard to play with a dictionary in the context of a running database it requires significant modifications to the database, after all , you can see dictionaries in action with other types of small data. JSON, the lingua franca of small data in the modern world, tends to be small, repetitive records.
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- Understanding Compression: Data Compression for Modern Developers by Colt McAnlis.
Here is a sample entry from this data set:. As you can see, there is quite a bit of repetition here — we can compress these nicely! But each user is a bit under 1 KB, and most compression algorithms really need more data to stretch their legs. A set of 1, users takes roughly KB to store uncompressed. Naively applying either gzip or zstd individually to each file cuts this down to just over KB; not bad! But if we create a one-time, pre-shared dictionary, with zstd the size drops to KB — taking the original compression ratio from 2.
This is a significant improvement, available out-of-box with zstd :. As shown above, Zstandard provides a substantial number of levels. This customization is powerful but leads to tough choices. The best way to decide is to review your data and measure, deciding what trade-offs you want to make. At Facebook, we find the default level 3 suitable for many use cases, but from time to time, we will adjust this slightly depending upon what our bottleneck is often we are trying to saturate a network connection or disk spindle ; other times, we care more about the stored size and will use a higher level.
Ultimately, for the results most tailored to your needs, you will need to consider both the hardware you use and the data you care about — there are no hard and fast prescriptions that can be made without context. Zstandard is both a command line tool zstd and a library. It is written in highly portable C, making it suitable for practically every platform used today — be it the servers that run your business, your laptop, or even the phone in your pocket.
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You can grab it from our github repository, compile it with a simple make install , and begin using it like you would use gzip :. As you might expect, you can use it as part of a command pipeline, for example, to back up your critical MySQL database:. The tar command supports different compression implementations out-of-box, so once you install Zstandard, you can immediately work with tarballs compressed with Zstandard.
Here's a simple example that shows it in use with tar and the speed difference compared with gzip:. Beyond command line use, there are the APIs, documented in the header files in the repository start here for an overview of the APIs. We also include a zlib-compatible wrapper API libWrapper for easier integration with tools that already have zlib interfaces. Finally, we include a number of examples , both of basic use and of more advanced use such as dictionaries and streaming, also in the GitHub repository.
While we have hit 1. Coming in future versions:. We would like to thank all contributors, both of code and of feedback, who helped us get to 1. This is just the beginning. We know that for Zstandard to live up to its potential, we need your help. As mentioned above, you can try Zstandard today by grabbing the source or pre-built binaries from our GitHub project , or, for Mac users, installing via homebrew brew install zstd.
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Learn more, including about available controls: Cookies Policy. Skip to content. Yann Collet. Comparing compression There are three standard metrics for comparing compression algorithms and implementations: Compression ratio: The original size numerator compared with the compressed size denominator , measured in unitless data as a size ratio of 1. These were the commands which use the default compression levels for both tools : zstd -c -3 silesia. At the same compression speed, it is substantially smaller: percent smaller.
It is almost 2x faster at decompression, regardless of compression ratio; the command line tooling numbers show an even bigger difference: more than 3x faster. It scales to much higher compression ratios, while sustaining lightning-fast decompression speeds. Under the hood Zstandard improves upon zlib by combining several recent innovations and targeting modern hardware: Memory By design, zlib is limited to a 32 KB window, which was a sensible choice in the early '90s. A format designed for parallel execution Today's CPUs are very powerful and can issue several instructions per cycle, thanks to multiple ALUs arithmetic logic units and increasingly advanced out-of-order execution design.
This is possible only if there is no relation between them. Branchless design New CPUs are more powerful and reach very high frequencies, but this is only possible thanks to a multi-stage approach, where an instruction is split into a pipeline of multiple steps. This turns out to be difficult. Consider the following simple situation: if condition doSomething else doSomethingElse When it encounters this, the CPU does not know what to do, since it depends on the value of condition.
Finite State Entropy: A next-generation probability compressor In compression, data is first transformed into a set of symbols the modeling stage , and then these symbols are encoded using a minimum number of bits. Repcode modeling Repcode modeling efficiently compresses structured data, which features sequences of almost equivalent content, differing by just one or a few bytes.
Zstandard in practice As mentioned before, there are several typical use cases of compression. Small data There is another use case for compression that gets less attention but can be quite important: small data.
Understanding Compression: Data Compression for Modern Developers by Colt McAnl | Trade Me
Dictionaries in action While it can be hard to play with a dictionary in the context of a running database it requires significant modifications to the database, after all , you can see dictionaries in action with other types of small data. Save dictionary of size into file.. Picking a compression level As shown above, Zstandard provides a substantial number of levels. Try it out Zstandard is both a command line tool zstd and a library. More to come While we have hit 1. Coming in future versions: Multi-threaded command line compression for even faster throughput on large data sets, similar to the pigz tool for zlib.
New compression levels, in both directions, allowing for even faster compression and higher ratios. Footnotes While lossless data compression is the focus of this post, there exists a related but very different field of lossy data compression, used primarily for images, audio, and video. Deflate, zlib, gzip — three names intertwined. Deflate is the algorithm used by the zlib and gzip implementations. Zlib is a library providing Deflate, and gzip is a command line tool that uses zlib for Deflating data as well as checksumming. This checksumming can have significant overhead.
All benchmarks were performed on an Intel E v3 running at 2. Command line tools zstd and gzip were built with the system GCC, 4. Algorithm benchmarks performed by lzbench were built with GCC 6. Leave a Reply Cancel reply You must be logged in to post a comment. Stay Connected Facebook Engineering. Facebook Research. Facebook Developers. Brand new: lowest price The lowest-priced, brand-new, unused, unopened, undamaged item in its original packaging where packaging is applicable.
Read full description. See details and exclusions. See all 6 brand new listings. Qty: 1 2. Buy it now. Add to basket. Be the first to write a review About this product. All listings for this product Buy it now Buy it now. New New. See all 6. About this product Product Information This witty book helps you understand how data compression algorithms work-in theory and practice-so you can choose the best solution among all the available compression tools. Additional Product Features Author s. Colt is a Developer Advocate at Google focusing on Games, compression, and Performance; Before that, he was a graphics programmer in the games industry working at Blizzard, Microsoft Ensemble , and Petroglyph.
Recently, he's been teaching Android Devs the Zen of Performance. When he's not working with developers, Colt spends his time preparing for an invasion of giant ants from outer space.