github.com/jboelter/compress
This package provides various compression algorithms.
Jan 14, 2021 (v1.11.7)
Jan 7, 2021 (v1.11.6)
Dec 20, 2020 (v1.11.4)
Nov 15, 2020 (v1.11.3)
Oct 11, 2020 (v1.11.2)
Oct 1, 2020 (v1.11.1)
Sept 8, 2020 (v1.11.0)
July 8, 2020 (v1.10.11)
June 23, 2020 (v1.10.10)
June 16, 2020 (v1.10.9):
June 5, 2020 (v1.10.8):
June 1, 2020 (v1.10.7):
May 21, 2020: (v1.10.6)
April 12, 2020: (v1.10.5)
Apr 8, 2020: (v1.10.4)
Mar 11, 2020: (v1.10.3)
Feb 27, 2020: (v1.10.2)
Feb 18, 2020: (v1.10.1)
Feb 4, 2020: (v1.10.0)
nil for previous behaviour. #216-rm (remove source files) and -q (no output except errors) to s2c and s2d commandss2c and s2d commandline tools.The packages are drop-in replacements for standard libraries. Simply replace the import path to use them:
| old import | new import | Documentation |
|---|---|---|
compress/gzip | github.com/klauspost/compress/gzip | gzip |
compress/zlib | github.com/klauspost/compress/zlib | zlib |
archive/zip | github.com/klauspost/compress/zip | zip |
compress/flate | github.com/klauspost/compress/flate | flate |
You may also be interested in pgzip, which is a drop in replacement for gzip, which support multithreaded compression on big files and the optimized crc32 package used by these packages.
The packages contains the same as the standard library, so you can use the godoc for that: gzip, zip, zlib, flate.
Currently there is only minor speedup on decompression (mostly CRC32 calculation).
This package offers stateless compression as a special option for gzip/deflate. It will do compression but without maintaining any state between Write calls.
This means there will be no memory kept between Write calls, but compression and speed will be suboptimal.
This is only relevant in cases where you expect to run many thousands of compressors concurrently, but with very little activity. This is not intended for regular web servers serving individual requests.
Because of this, the size of actual Write calls will affect output size.
In gzip, specify level -3 / gzip.StatelessCompression to enable.
For direct deflate use, NewStatelessWriter and StatelessDeflate are available. See documentation
A bufio.Writer can of course be used to control write sizes. For example, to use a 4KB buffer:
// replace 'ioutil.Discard' with your output.
gzw, err := gzip.NewWriterLevel(ioutil.Discard, gzip.StatelessCompression)
if err != nil {
return err
}
defer gzw.Close()
w := bufio.NewWriterSize(gzw, 4096)
defer w.Flush()
// Write to 'w'
This will only use up to 4KB in memory when the writer is idle.
Compression is almost always worse than the fastest compression level and each write will allocate (a little) memory.
It has been a while since we have been looking at the speed of this package compared to the standard library, so I thought I would re-do my tests and give some overall recommendations based on the current state. All benchmarks have been performed with Go 1.10 on my Desktop Intel(R) Core(TM) i7-2600 CPU @3.40GHz. Since I last ran the tests, I have gotten more RAM, which means tests with big files are no longer limited by my SSD.
The raw results are in my updated spreadsheet. Due to cgo changes and upstream updates i could not get the cgo version of gzip to compile. Instead I included the zstd cgo implementation. If I get cgo gzip to work again, I might replace the results in the sheet.
The columns to take note of are: MB/s - the throughput. Reduction - the data size reduction in percent of the original. Rel Speed relative speed compared to the standard library at the same level. Smaller - how many percent smaller is the compressed output compared to stdlib. Negative means the output was bigger. Loss means the loss (or gain) in compression as a percentage difference of the input.
The gzstd (standard library gzip) and gzkp (this package gzip) only uses one CPU core. pgzip, bgzf uses all 4 cores. zstd uses one core, and is a beast (but not Go, yet).
There appears to be a roughly 5-10% speed advantage over the standard library when comparing at similar compression levels.
The biggest difference you will see is the result of re-balancing the compression levels. I wanted by library to give a smoother transition between the compression levels than the standard library.
This package attempts to provide a more smooth transition, where "1" is taking a lot of shortcuts, "5" is the reasonable trade-off and "9" is the "give me the best compression", and the values in between gives something reasonable in between. The standard library has big differences in levels 1-4, but levels 5-9 having no significant gains - often spending a lot more time than can be justified by the achieved compression.
There are links to all the test data in the spreadsheet in the top left field on each tab.
This test set aims to emulate typical use in a web server. The test-set is 4GB data in 53k files, and is a mixture of (mostly) HTML, JS, CSS.
Since level 1 and 9 are close to being the same code, they are quite close. But looking at the levels in-between the differences are quite big.
Looking at level 6, this package is 88% faster, but will output about 6% more data. For a web server, this means you can serve 88% more data, but have to pay for 6% more bandwidth. You can draw your own conclusions on what would be the most expensive for your case.
This test is for typical data files stored on a server. In this case it is a collection of Go precompiled objects. They are very compressible.
The picture is similar to the web content, but with small differences since this is very compressible. Levels 2-3 offer good speed, but is sacrificing quite a bit of compression.
The standard library seems suboptimal on level 3 and 4 - offering both worse compression and speed than level 6 & 7 of this package respectively.
This is a JSON file with very high redundancy. The reduction starts at 95% on level 1, so in real life terms we are dealing with something like a highly redundant stream of data, etc.
It is definitely visible that we are dealing with specialized content here, so the results are very scattered. This package does not do very well at levels 1-4, but picks up significantly at level 5 and levels 7 and 8 offering great speed for the achieved compression.
So if you know you content is extremely compressible you might want to go slightly higher than the defaults. The standard library has a huge gap between levels 3 and 4 in terms of speed (2.75x slowdown), so it offers little "middle ground".
This is a pretty common test corpus: enwik9. It contains the first 10^9 bytes of the English Wikipedia dump on Mar. 3, 2006. This is a very good test of typical text based compression and more data heavy streams.
We see a similar picture here as in "Web Content". On equal levels some compression is sacrificed for more speed. Level 5 seems to be the best trade-off between speed and size, beating stdlib level 3 in both.
I will combine two test sets, one 10GB file set and a VM disk image (~8GB). Both contain different data types and represent a typical backup scenario.
The most notable thing is how quickly the standard library drops to very low compression speeds around level 5-6 without any big gains in compression. Since this type of data is fairly common, this does not seem like good behavior.
This is mainly a test of how good the algorithms are at detecting un-compressible input. The standard library only offers this feature with very conservative settings at level 1. Obviously there is no reason for the algorithms to try to compress input that cannot be compressed. The only downside is that it might skip some compressible data on false detections.
This compression library adds a special compression level, named HuffmanOnly, which allows near linear time compression. This is done by completely disabling matching of previous data, and only reduce the number of bits to represent each character.
This means that often used characters, like 'e' and ' ' (space) in text use the fewest bits to represent, and rare characters like '¤' takes more bits to represent. For more information see wikipedia or this nice video.
Since this type of compression has much less variance, the compression speed is mostly unaffected by the input data, and is usually more than 180MB/s for a single core.
The downside is that the compression ratio is usually considerably worse than even the fastest conventional compression. The compression ratio can never be better than 8:1 (12.5%).
The linear time compression can be used as a "better than nothing" mode, where you cannot risk the encoder to slow down on some content. For comparison, the size of the "Twain" text is 233460 bytes (+29% vs. level 1) and encode speed is 144MB/s (4.5x level 1). So in this case you trade a 30% size increase for a 4 times speedup.
For more information see my blog post on Fast Linear Time Compression.
This is implemented on Go 1.7 as "Huffman Only" mode, though not exposed for gzip.
The standard snappy package has now been improved. This repo contains a copy of the snappy repo.
I would advise to use the standard package: https://github.com/golang/snappy
This code is licensed under the same conditions as the original Go code. See LICENSE file.
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