General Usage : GZip is often a good choice for cold data, which is accessed infrequently. Snappy or LZO are a better choice for hot data, which is accessed frequently. Snappy often performs better than LZO.
It is worth running tests to see if you detect a significant difference. I'd say gzip wins outside of scenarios like streaming, where write-time latency would be important. It's important to keep in mind that speed is essentially compute cost. However, cloud compute is a one-time cost whereas cloud storage is a recurring cost.
The tradeoff depends on the retention period of the data. I agree with 1 answer Mark Adler and have some reserch info, but I do not agree with the second answer Garren S . Text separated fieldsnot compressed. Transformation was performed using Hive on an EMR consisting of 2 m4. Transformation - select all fields with ordering by several fields. This research, of course, is not standard, but at least a little shows the real comparison. With other datasets and computation results may be different.
Learn more. Asked 4 years, 7 months ago. Active 17 days ago. Viewed 25k times. I am trying to use Spark SQL to write parquet file. What is the difference between these compression formats? HashRocketSyntax 2, 1 1 gold badge 19 19 silver badges 42 42 bronze badges.Search everywhere only in this topic. Advanced Search. Classic List Threaded. I must be doing something wrong: I am writing out avro files with three options: a.
In my observation, the snappy file is larger than the original avro file? Serge Blazhievsky. Re: avro compression using snappy and deflate. How big is the original data you are trying to compress? Sent from my iPhone.
Thanks, Nikhil. Hello All, I think I figured our where I goofed up. I was flushing on every record, so basically this was compression per record, so it had a meta data with each record. This was adding more data to the output when compared to avro. So now I have better figures: atleast looks realistic, still need to find out of it is map-reduceable. Tatu Saloranta. Scott Carey In reply to this post by snikhil0. Have you checked out jvm-compressor-benchmark page?
While test data does not include Avro, I would not expect results to differ all that much. LZO isn't a particularly compelling codec in any of combinations tested. Snappy, LZF and LZ4 not yet included in public results, but there's code, and preliminary results are very good are the fastest Java codecs. If anyone has publically available set of Avro data, it would be quite easy to add Avro-data test to jvm compressor benchmark.
However, in my experience anything past level 6 is only very slightly smaller and much slower, while the difference between levels 1 to 3 is large on both fronts. I have not heard of anyone doing this. LZO is not Apache license compatible, and there are now several alternatives that are in the same class of compression algorithm available, including Snappy.
Free forum by Nabble. Edit this page.I think I first heard about the Zstandard compression algorithm at a Mercurial developer sprint in At one end of a large table a few people were uttering expletives out of sheer excitement.
At developer gatherings, that's the universal signal for something is awesome. Long story short, a Facebook engineer shared a link to the RealTime Data Compression blog operated by Yann Collet then known as the author of LZ4 - a compression algorithm known for its insane speeds and people were completely nerding out over the excellent articles and the data within showing the beginnings of a new general purpose lossless compression algorithm named Zstandard.
This being a Mercurial meeting, many of us were intrigued because zlib is used by Mercurial for various functionality including on-disk storage and compression over the wire protocol and zlib operations frequently appear as performance hot spots.
Before I continue, if you are interested in low-level performance and software optimization, I highly recommend perusing the RealTime Data Compression blog. There are some absolute nuggets of info in there. Anyway, over the months, the news about Zstandard zstd kept getting better and more promising. As the 1.
I was toying around with pre-release versions and was absolutely blown away by the performance and features. I believed the hype. Zstandard 1. A few days later, I started the python-zstandard project to provide a fully-featured and Pythonic interface to the underlying zstd C API while not sacrificing safety or performance.
The ulterior motive was to leverage those bindings in Mercurial so Zstandard could be a first class citizen in Mercurial, possibly replacing zlib as the default compression algorithm for all operations. Fast forward six months and I've achieved many of those goals.
It even exposes some primitives not in the C API, such as batch compression operations that leverage multiple threads and use minimal memory allocations to facilitate insanely fast execution.
Expect a dedicated post on python-zstandard from me soon. Mercurial 4. Two Mercurial 4. When cloning from hg. And, work is ongoing for Mercurial to support Zstandard for on-disk storage, which should bring considerable performance wins over zlib for local operations. I've learned a lot working on python-zstandard and integrating Zstandard into Mercurial.
My primary takeaway is Zstandard is awesome. In this post, I'm going to extol the virtues of Zstandard and provide reasons why I think you should use it. This trade-off is usually made because data - either at rest in storage or in motion over a network or even through a machine via software and memory - is a limiting factor for performance.
At scale, better and more efficient compression can translate to substantial cost savings in infrastructure. It can also lead to improved application performance, translating to better end-user engagement, sales, productivity, etc.
This is why companies like Facebook ZstandardGoogle brotli, snappy, zopfliand Pied Piper middle-out invest in compression. Computers are completely different today than they were in The Pentium microprocessor debuted in For comparison, a modern NVMe M.
And of course CPU and network speeds have increased as well. We also have completely different instruction sets on CPUs for well-designed algorithms and software to take advantage of. What I'm trying to say is the market is ripe for DEFLATE and zlib to be dethroned by algorithms and software that take into account the realities of modern computers. Zstandard initially piqued my attention by promising better-than-zlib compression and performance in both the compression and decompression directions.Snappy does not use inline assembler except some optimizations  and is portable.
Snappy encoding is not bit-oriented, but byte-oriented only whole bytes are emitted or consumed from a stream.
The format uses no entropy encoderlike Huffman tree or arithmetic encoder. The first bytes of the stream are the length of uncompressed data, stored as a little-endian varint which allows for variable-length encoding. The lower seven bits of each byte are used for data and the high bit is a flag to indicate the end of the length field.
The remaining bytes in the stream are encoded using one of four element types. The element type is encoded in the lower two bits of the first byte tag byte of the element: . The copy refers to the dictionary just-decompressed data.
The offset is the shift from the current position back to the already decompressed stream. The length is the number of bytes to copy from the dictionary. The size of the dictionary was limited by the 1. Wikipedia is a free, web-based, collaborative, multilingual encyclopedia project supported by the non-profit Wikimedia Foundation. Its 19 million articles over 3.
The first 2 bytes, ca02 are the length, as a little-endian varint see Protocol Buffers for the varint specification . Thus the most-significant byte is '02'.
In this example, all common substrings with four or more characters were eliminated by the compression process. More common compressors can compress this better. Unlike compression methods such as gzip and bzip2, there is no entropy encoding used to pack alphabet into the bit stream. From Wikipedia, the free encyclopedia. Not to be confused with Snappii. Free and open-source software portal.
Retrieved 21 August — via GitHub. Retrieved August 1, Google Code. Archived from the original on September 8, MariaDB KnowledgeBase. November 11, — via GitHub.
Data compression methods.Snap is a software packaging and deployment system developed by Canonical for the operating systems that use the Linux kernel.
The packages, called snapsand the tool for using them, snapdwork across a range of Linux distributions and allow upstream software developers to distribute their applications directly to users. Snaps are self-contained applications running in a sandbox with mediated access to the host system. Snap was originally released for cloud applications  but was later ported to work for Internet of Things devices   and desktop   applications too. The Snap Store allows developers to publish their applications directly to users.
This creates a delay between application development and its deployment for end-users. All apps uploaded to the Snap Store undergo automatic testing, including a malware scan. However, Snap apps do not receive the same level of verification as software in the regular Ubuntu archives.
Apache commons compress | TAR, GZip, BZip2, XZ, Snappy, Deflate Examples | un-GZip, UnTar Examples
In one case in Maytwo applications by the same developer were found to contain a cryptocurrency miner which ran in the background during application execution. When this issue was found, Canonical removed the applications from the Snap Store and transferred ownership of the Snaps to a trusted third-party which re-published the Snaps without the miner present.
Because packages in the Snap Store are maintained by developers themselves, distribution maintainers cannot ensure packages meet quality standards and are timely updated. In one case, Microsoft left an outdated version of Skype in the Snapcraft store for over a year.
Although the Snap Store by Canonical is currently the only existing store for snaps, Snap itself can be used without a store. Snap packages can be obtained from any source, including the website of a developer. Snaps are self-contained packages that work across a range of Linux distributions. This is unlike traditional Linux package management approaches, which require specifically adapted packages for each Linux distribution. The snap file format is a single compressed filesystem using the SquashFS format with the extension.
This filesystem contains the application, libraries it depends on, and declarative metadata. This metadata is interpreted by snapd to set up an appropriately shaped secure sandbox for that application. After installation, the snap is mounted by the host operating system and decompressed on the fly when the files are used. A significant difference between Snap and other universal Linux packaging formats such as Flatpak is that Snap supports any class of Linux application such as desktop applications, server tools, IoT apps and even system services such as the printer driver stack.
Applications in a Snap run in a container with limited access to the host system. Using InterfacesUsers can give an application mediated access to additional features of the host such as recording audio, accessing USB devices and recording video.
Desktop applications can also use the XDG Desktop Portals, a standardized API originally created by the Flatpak project to give sandboxed desktop applications access to host resources. The downside is that applications and toolkits need to be rewritten in order to use these newer API's.
The Snap sandbox also supports sharing data and Unix sockets between Snaps. As a result, on distributions such as Fedora which enable SELinux by default, the Snap sandbox is heavily degraded. Although Canonical is working with many other developers and companies to make it possible for multiple LSM's to run at the same time, this solution is still a long time away.
The Snap sandbox prevents snapped desktop applications from accessing the themes of the host operating system to prevent compatibility issues. In order for Snaps to use a theme, it also needs to be packaged in a separate Snap. Many popular themes are packaged by the Snap developers  but some themes are not supported yet  and uncommon themes have to be installed manually. If a theme is not available as a Snap package, users have to resort to choosing the best matching theme available.
Multiple times a day, snapd checks for available updates of all Snaps and installs them in the background using atomic operation. Updates can be reverted   and use delta encoding to reduce their download size. Publishers can release and update multiple versions of their software in parallel using channels. Each channel has a specific track and riskwhich indicate the version and stability of the software released on that channel.
Publishers can create additional channels to give users the possibility to stick to specific major releases of their software. For example, a 2. When the publisher releases a new major version in a new channel, users can manually update to the next version when they choose.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Have a question about this project?
Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. Switching to a faster algorithm would bring that down. I think LZ4 is that algorithm:. I think the eventual ideal move here is to compress each item's metadata and ast separately and fix how we load metadata to only decompress the necessary items. But picking a different compressor, or different encoding, is certainly also fine.
Yeah, transitioning away from ebml would be great. Decoding it and especially converting from big endian is a sore spot. Of course, reading as little endian right now isn't any better because it still does conversion and doesn't optimize for little endian systems, but that optimization is at least possible.
Alright so given the numbersI don't think LZ4 is worth it, and neither does brson. We use optional third-party analytics cookies to understand how you use GitHub.Pinarello Grevil Gravel Bike Review
Learn more. You can always update your selection by clicking Cookie Preferences at the bottom of the page. For more information, see our Privacy Statement. We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e. Skip to content. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I understand the LZ77 and LZ78 algorithms. I read about LZ4 here and here and found code for it. Those links described the LZ4 block format.
But it would be great if someone could explain or direct me to some resource explaining :. It's a fit for applications where you want compression that's very cheap: for example, you're trying to make a network or on-disk format more compact but can't afford to spend a bunch of CPU time on compression.
It's in a family with, for example, snappy and LZO. ZIP and. PNG formats, and too many other places to count. There's a lot of variation among the high-compression algorithms, but broadly, they tend to capture redundancies over longer distances, take more advantage of context to determine what bytes are likely, and use more compact but slower ways to express their results in bits.
LZ4HC is a "high-compression" variant of LZ4 that, I believe, changes point 1 above--the compressor finds more than one match between current and past data and looks for the best match to ensure the output is small.
Snap (package manager)
This improves compression ratio but lowers compression speed compared to LZ4. Decompression speed isn't hurt, though, so if you compress once and decompress many times and mostly want extremely cheap decompression, LZ4HC would make sense. Note that even a fast compressor might not allow one core to saturate a large amount of bandwidth, like that provided by SSDs or fast in-datacenter links.
There are even quicker compressors with lower ratios, sometimes used to temporarily pack data in RAM. WKdm and Density are two such compressors; one trait they share is acting on 4-byte machine words of input at a time rather than individual bytes.
Sometimes specialized hardware can achieve very fast compression, like in Samsung's Exynos chips or Intel's QuickAssist technology.
If you're interested in compressing more than LZ4 but with less CPU time than deflate, the author of LZ4 Yann Collet wrote a library called Zstd --here's a blog post from Facebook at its stable releasebackground on the finite state machines it uses for entropy coding, and a detailed description in an RFC. Its fast modes could work in some LZ4-ish use cases. Other, less popular "medium" packers are Apple's lzfse and Google's gipfeli. Compared to the fastest compressors, those "medium" packers add a form of entropy encodingwhich is to say they take advantage of how some bytes are more common than others and in effect put fewer bits in the output for the more common byte values.