21 KiB
zstd
Zstandard is a real-time compression algorithm, providing high compression ratios. It offers a very wide range of compression / speed trade-off, while being backed by a very fast decoder. A high performance compression algorithm is implemented. For now focused on speed.
This package provides compression to and decompression of Zstandard content.
This package is pure Go and without use of “unsafe”.
The zstd package is provided as open source software
using a Go standard license.
Currently the package is heavily optimized for 64 bit processors and will be significantly slower on 32 bit processors.
For seekable zstd streams, see this excellent package.
Installation
Install using go get -u github.com/klauspost/compress.
The package is located in
github.com/klauspost/compress/zstd.
Compressor
Status:
STABLE - there may always be subtle bugs, a wide variety of content has been tested and the library is actively used by several projects. This library is being fuzz-tested for all updates.
There may still be specific combinations of data types/size/settings that could lead to edge cases, so as always, testing is recommended.
For now, a high speed (fastest) and medium-fast (default) compressor has been implemented.
- The “Fastest” compression ratio is roughly equivalent to zstd level 1.
- The “Default” compression ratio is roughly equivalent to zstd level 3 (default).
- The “Better” compression ratio is roughly equivalent to zstd level 7.
- The “Best” compression ratio is roughly equivalent to zstd level 11.
In terms of speed, it is typically 2x as fast as the stdlib deflate/gzip in its fastest mode. The compression ratio compared to stdlib is around level 3, but usually 3x as fast.
Usage
An Encoder can be used for either compressing a stream via the
io.WriteCloser interface supported by the Encoder or as
multiple independent tasks via the EncodeAll function.
Smaller encodes are encouraged to use the EncodeAll function. Use
NewWriter to create a new instance that can be used for
both.
To create a writer with default options, do like this:
// Compress input to output.
func Compress(in io.Reader, out io.Writer) error {
enc, err := zstd.NewWriter(out)
if err != nil {
return err
}
_, err = io.Copy(enc, in)
if err != nil {
enc.Close()
return err
}
return enc.Close()
}Now you can encode by writing data to enc. The output
will be finished writing when Close() is called. Even if
your encode fails, you should still call Close() to release
any resources that may be held up.
The above is fine for big encodes. However, whenever possible try to reuse the writer.
To reuse the encoder, you can use the Reset(io.Writer)
function to change to another output. This will allow the encoder to
reuse all resources and avoid wasteful allocations.
Currently stream encoding has ‘light’ concurrency, meaning up to 2
goroutines can be working on part of a stream. This is independent of
the WithEncoderConcurrency(n), but that is likely to change
in the future. So if you want to limit concurrency for future updates,
specify the concurrency you would like.
If you would like stream encoding to be done without spawning async
goroutines, use WithEncoderConcurrency(1) which will
compress input as each block is completed, blocking on writes until each
has completed.
You can specify your desired compression level using
WithEncoderLevel() option. Currently only pre-defined
compression settings can be specified.
Future Compatibility Guarantees
This will be an evolving project. When using this package it is important to note that both the compression efficiency and speed may change.
The goal will be to keep the default efficiency at the default zstd (level 3). However the encoding should never be assumed to remain the same, and you should not use hashes of compressed output for similarity checks.
The Encoder can be assumed to produce the same output from the exact same code version. However, the may be modes in the future that break this, although they will not be enabled without an explicit option.
This encoder is not designed to (and will probably never) output the exact same bitstream as the reference encoder.
Also note, that the cgo decompressor currently does not report all errors on invalid input, omits error checks, ignores checksums and seems to ignore concatenated streams, even though it is part of the spec.
Blocks
For compressing small blocks, the returned encoder has a function
called EncodeAll(src, dst []byte) []byte.
EncodeAll will encode all input in src and append it to
dst. This function can be called concurrently. Each call will only run
on a same goroutine as the caller.
Encoded blocks can be concatenated and the result will be the
combined input stream. Data compressed with EncodeAll can be decoded
with the Decoder, using either a stream or DecodeAll.
Especially when encoding blocks you should take special care to reuse the encoder. This will effectively make it run without allocations after a warmup period. To make it run completely without allocations, supply a destination buffer with space for all content.
import "github.com/klauspost/compress/zstd"
// Create a writer that caches compressors.
// For this operation type we supply a nil Reader.
var encoder, _ = zstd.NewWriter(nil)
// Compress a buffer.
// If you have a destination buffer, the allocation in the call can also be eliminated.
func Compress(src []byte) []byte {
return encoder.EncodeAll(src, make([]byte, 0, len(src)))
} You can control the maximum number of concurrent encodes using the
WithEncoderConcurrency(n) option when creating the
writer.
Using the Encoder for both a stream and individual blocks concurrently is safe.
Performance
I have collected some speed examples to compare speed and compression against other compressors.
fileis the input file.outis the compressor used.zskpis this package.zstdis the Datadog cgo library.gzstd/gzkpis gzip standard and this library.levelis the compression level used. Forzskplevel 1 is “fastest”, level 2 is “default”; 3 is “better”, 4 is “best”.insize/outsizeis the input/output size.millisis the number of milliseconds used for compression.mb/sis megabytes (2^20 bytes) per second.
Silesia Corpus:
http://sun.aei.polsl.pl/~sdeor/corpus/silesia.zip
This package:
file out level insize outsize millis mb/s
silesia.tar zskp 1 211947520 73821326 634 318.47
silesia.tar zskp 2 211947520 67655404 1508 133.96
silesia.tar zskp 3 211947520 64746933 3000 67.37
silesia.tar zskp 4 211947520 60073508 16926 11.94
cgo zstd:
silesia.tar zstd 1 211947520 73605392 543 371.56
silesia.tar zstd 3 211947520 66793289 864 233.68
silesia.tar zstd 6 211947520 62916450 1913 105.66
silesia.tar zstd 9 211947520 60212393 5063 39.92
gzip, stdlib/this package:
silesia.tar gzstd 1 211947520 80007735 1498 134.87
silesia.tar gzkp 1 211947520 80088272 1009 200.31
GOB stream of binary data. Highly compressible.
https://files.klauspost.com/compress/gob-stream.7z
file out level insize outsize millis mb/s
gob-stream zskp 1 1911399616 233948096 3230 564.34
gob-stream zskp 2 1911399616 203997694 4997 364.73
gob-stream zskp 3 1911399616 173526523 13435 135.68
gob-stream zskp 4 1911399616 162195235 47559 38.33
gob-stream zstd 1 1911399616 249810424 2637 691.26
gob-stream zstd 3 1911399616 208192146 3490 522.31
gob-stream zstd 6 1911399616 193632038 6687 272.56
gob-stream zstd 9 1911399616 177620386 16175 112.70
gob-stream gzstd 1 1911399616 357382013 9046 201.49
gob-stream gzkp 1 1911399616 359136669 4885 373.08
The test data for the Large Text Compression Benchmark is the first
10^9 bytes of the English Wikipedia dump on Mar. 3, 2006.
http://mattmahoney.net/dc/textdata.html
file out level insize outsize millis mb/s
enwik9 zskp 1 1000000000 343833605 3687 258.64
enwik9 zskp 2 1000000000 317001237 7672 124.29
enwik9 zskp 3 1000000000 291915823 15923 59.89
enwik9 zskp 4 1000000000 261710291 77697 12.27
enwik9 zstd 1 1000000000 358072021 3110 306.65
enwik9 zstd 3 1000000000 313734672 4784 199.35
enwik9 zstd 6 1000000000 295138875 10290 92.68
enwik9 zstd 9 1000000000 278348700 28549 33.40
enwik9 gzstd 1 1000000000 382578136 8608 110.78
enwik9 gzkp 1 1000000000 382781160 5628 169.45
Highly compressible JSON file.
https://files.klauspost.com/compress/github-june-2days-2019.json.zst
file out level insize outsize millis mb/s
github-june-2days-2019.json zskp 1 6273951764 697439532 9789 611.17
github-june-2days-2019.json zskp 2 6273951764 610876538 18553 322.49
github-june-2days-2019.json zskp 3 6273951764 517662858 44186 135.41
github-june-2days-2019.json zskp 4 6273951764 464617114 165373 36.18
github-june-2days-2019.json zstd 1 6273951764 766284037 8450 708.00
github-june-2days-2019.json zstd 3 6273951764 661889476 10927 547.57
github-june-2days-2019.json zstd 6 6273951764 642756859 22996 260.18
github-june-2days-2019.json zstd 9 6273951764 601974523 52413 114.16
github-june-2days-2019.json gzstd 1 6273951764 1164397768 26793 223.32
github-june-2days-2019.json gzkp 1 6273951764 1120631856 17693 338.16
VM Image, Linux mint with a few installed applications:
https://files.klauspost.com/compress/rawstudio-mint14.7z
file out level insize outsize millis mb/s
rawstudio-mint14.tar zskp 1 8558382592 3718400221 18206 448.29
rawstudio-mint14.tar zskp 2 8558382592 3326118337 37074 220.15
rawstudio-mint14.tar zskp 3 8558382592 3163842361 87306 93.49
rawstudio-mint14.tar zskp 4 8558382592 2970480650 783862 10.41
rawstudio-mint14.tar zstd 1 8558382592 3609250104 17136 476.27
rawstudio-mint14.tar zstd 3 8558382592 3341679997 29262 278.92
rawstudio-mint14.tar zstd 6 8558382592 3235846406 77904 104.77
rawstudio-mint14.tar zstd 9 8558382592 3160778861 140946 57.91
rawstudio-mint14.tar gzstd 1 8558382592 3926234992 51345 158.96
rawstudio-mint14.tar gzkp 1 8558382592 3960117298 36722 222.26
CSV data:
https://files.klauspost.com/compress/nyc-taxi-data-10M.csv.zst
file out level insize outsize millis mb/s
nyc-taxi-data-10M.csv zskp 1 3325605752 641319332 9462 335.17
nyc-taxi-data-10M.csv zskp 2 3325605752 588976126 17570 180.50
nyc-taxi-data-10M.csv zskp 3 3325605752 529329260 32432 97.79
nyc-taxi-data-10M.csv zskp 4 3325605752 474949772 138025 22.98
nyc-taxi-data-10M.csv zstd 1 3325605752 687399637 8233 385.18
nyc-taxi-data-10M.csv zstd 3 3325605752 598514411 10065 315.07
nyc-taxi-data-10M.csv zstd 6 3325605752 570522953 20038 158.27
nyc-taxi-data-10M.csv zstd 9 3325605752 517554797 64565 49.12
nyc-taxi-data-10M.csv gzstd 1 3325605752 928654908 21270 149.11
nyc-taxi-data-10M.csv gzkp 1 3325605752 922273214 13929 227.68Decompressor
Staus: STABLE - there may still be subtle bugs, but a wide variety of content has been tested.
This library is being continuously fuzz-tested, kindly supplied by fuzzit.dev. The main purpose of the fuzz testing is to ensure that it is not possible to crash the decoder, or run it past its limits with ANY input provided.
Usage
The package has been designed for two main usages, big streams of
data and smaller in-memory buffers. There are two main usages of the
package for these. Both of them are accessed by creating a
Decoder.
For streaming use a simple setup could look like this:
import "github.com/klauspost/compress/zstd"
func Decompress(in io.Reader, out io.Writer) error {
d, err := zstd.NewReader(in)
if err != nil {
return err
}
defer d.Close()
// Copy content...
_, err = io.Copy(out, d)
return err
}It is important to use the “Close” function when you no longer need
the Reader to stop running goroutines, when running with default
settings. Goroutines will exit once an error has been returned,
including io.EOF at the end of a stream.
Streams are decoded concurrently in 4 asynchronous stages to give the
best possible throughput. However, if you prefer synchronous
decompression, use WithDecoderConcurrency(1) which will
decompress data as it is being requested only.
For decoding buffers, it could look something like this:
import "github.com/klauspost/compress/zstd"
// Create a reader that caches decompressors.
// For this operation type we supply a nil Reader.
var decoder, _ = zstd.NewReader(nil, WithDecoderConcurrency(0))
// Decompress a buffer. We don't supply a destination buffer,
// so it will be allocated by the decoder.
func Decompress(src []byte) ([]byte, error) {
return decoder.DecodeAll(src, nil)
} Both of these cases should provide the functionality needed. The decoder can be used for concurrent decompression of multiple buffers. By default 4 decompressors will be created.
It will only allow a certain number of concurrent operations to run.
To tweak that yourself use the WithDecoderConcurrency(n)
option when creating the decoder. It is possible to use
WithDecoderConcurrency(0) to create GOMAXPROCS
decoders.
Dictionaries
Data compressed with dictionaries can be decompressed.
Dictionaries are added individually to Decoders. Dictionaries are
generated by the zstd --train command and contains an
initial state for the decoder. To add a dictionary use the
WithDecoderDicts(dicts ...[]byte) option with the
dictionary data. Several dictionaries can be added at once.
The dictionary will be used automatically for the data that specifies them. A re-used Decoder will still contain the dictionaries registered.
When registering multiple dictionaries with the same ID, the last one will be used.
It is possible to use dictionaries when compressing data.
To enable a dictionary use WithEncoderDict(dict []byte).
Here only one dictionary will be used and it will likely be used even if
it doesn’t improve compression.
The used dictionary must be used to decompress the content.
For any real gains, the dictionary should be built with similar data. If an unsuitable dictionary is used the output may be slightly larger than using no dictionary. Use the zstd commandline tool to build a dictionary from sample data. For information see zstd dictionary information.
For now there is a fixed startup performance penalty for compressing content with dictionaries. This will likely be improved over time. Just be aware to test performance when implementing.
Allocation-less operation
The decoder has been designed to operate without allocations after a warmup.
This means that you should store the decoder for best
performance. To re-use a stream decoder, use the
Reset(r io.Reader) error to switch to another stream. A
decoder can safely be re-used even if the previous stream failed.
To release the resources, you must call the Close()
function on a decoder. After this it can no longer be reused,
but all running goroutines will be stopped. So you must use
this if you will no longer need the Reader.
For decompressing smaller buffers a single decoder can be used. When decoding buffers, you can supply a destination slice with length 0 and your expected capacity. In this case no unneeded allocations should be made.
Concurrency
The buffer decoder does everything on the same goroutine and does
nothing concurrently. It can however decode several buffers
concurrently. Use WithDecoderConcurrency(n) to limit
that.
The stream decoder will create goroutines that:
- Reads input and splits the input into blocks.
- Decompression of literals.
- Decompression of sequences.
- Reconstruction of output stream.
So effectively this also means the decoder will “read ahead” and prepare data to always be available for output.
The concurrency level will, for streams, determine how many blocks ahead the compression will start.
Since “blocks” are quite dependent on the output of the previous block stream decoding will only have limited concurrency.
In practice this means that concurrency is often limited to utilizing about 3 cores effectively.
Benchmarks
The first two are streaming decodes and the last are smaller inputs.
Running on AMD Ryzen 9 3950X 16-Core Processor. AMD64 assembly used.
BenchmarkDecoderSilesia-32 5 206878840 ns/op 1024.50 MB/s 49808 B/op 43 allocs/op
BenchmarkDecoderEnwik9-32 1 1271809000 ns/op 786.28 MB/s 72048 B/op 52 allocs/op
Concurrent blocks, performance:
BenchmarkDecoder_DecodeAllParallel/kppkn.gtb.zst-32 67356 17857 ns/op 10321.96 MB/s 22.48 pct 102 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/geo.protodata.zst-32 266656 4421 ns/op 26823.21 MB/s 11.89 pct 19 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/plrabn12.txt.zst-32 20992 56842 ns/op 8477.17 MB/s 39.90 pct 754 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/lcet10.txt.zst-32 27456 43932 ns/op 9714.01 MB/s 33.27 pct 524 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/asyoulik.txt.zst-32 78432 15047 ns/op 8319.15 MB/s 40.34 pct 66 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/alice29.txt.zst-32 65800 18436 ns/op 8249.63 MB/s 37.75 pct 88 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/html_x_4.zst-32 102993 11523 ns/op 35546.09 MB/s 3.637 pct 143 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/paper-100k.pdf.zst-32 1000000 1070 ns/op 95720.98 MB/s 80.53 pct 3 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/fireworks.jpeg.zst-32 749802 1752 ns/op 70272.35 MB/s 100.0 pct 5 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/urls.10K.zst-32 22640 52934 ns/op 13263.37 MB/s 26.25 pct 1014 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/html.zst-32 226412 5232 ns/op 19572.27 MB/s 14.49 pct 20 B/op 0 allocs/op
BenchmarkDecoder_DecodeAllParallel/comp-data.bin.zst-32 923041 1276 ns/op 3194.71 MB/s 31.26 pct 0 B/op 0 allocs/opThis reflects the performance around May 2022, but this may be out of date.
Zstd inside ZIP files
It is possible to use zstandard to compress individual files inside zip archives. While this isn’t widely supported it can be useful for internal files.
To support the compression and decompression of these files you must register a compressor and decompressor.
It is highly recommended registering the (de)compressors on individual zip Reader/Writer and NOT use the global registration functions. The main reason for this is that 2 registrations from different packages will result in a panic.
It is a good idea to only have a single compressor and decompressor, since they can be used for multiple zip files concurrently, and using a single instance will allow reusing some resources.
See this example for how to compress and decompress files inside zip archives.
Contributions
Contributions are always welcome. For new features/fixes, remember to add tests and for performance enhancements include benchmarks.
For general feedback and experience reports, feel free to open an issue or write me on Twitter.
This package includes the excellent github.com/cespare/xxhash
package Copyright (c) 2016 Caleb Spare.