Reed-Solomon
Reed-Solomon Erasure Coding in Go, with speeds exceeding 1GB/s/cpu core implemented in pure Go.
This is a Go port of the JavaReedSolomon library released by Backblaze, with some additional optimizations.
For an introduction on erasure coding, see the post on the Backblaze blog.
Package home: https://github.com/klauspost/reedsolomon
Godoc: https://pkg.go.dev/github.com/klauspost/reedsolomon?tab=doc
Installation
To get the package use the standard:
go get -u github.com/klauspost/reedsolomon
Using Go modules recommended.
Changes
May 2020
- ARM64 optimizations, up to 2.5x faster.
- Added WithFastOneParityMatrix for faster operation with 1 parity shard.
- Much better performance when using a limited number of goroutines.
- AVX512 is now using multiple cores.
- Stream processing overhaul, big speedups in most cases.
- AVX512 optimizations
March 6, 2019
The pure Go implementation is about 30% faster. Minor tweaks to assembler implementations.
February 8, 2019
AVX512 accelerated version added for Intel Skylake CPUs. This can give up to a 4x speed improvement as compared to AVX2. See here for more details.
December 18, 2018
Assembly code for ppc64le has been contributed, this boosts performance by about 10x on this platform.
November 18, 2017
Added WithAutoGoroutines which will attempt to calculate the optimal number of goroutines to use based on your expected shard size and detected CPU.
October 1, 2017
-
Cauchy Matrix is now an option. Thanks to templexxx for the basis of this.
-
Default maximum number of goroutines has been increased for better multi-core scaling.
-
After several requests the Reconstruct and ReconstructData now slices of zero length but sufficient capacity to be used instead of allocating new memory.
August 26, 2017
-
The
Encoder()now contains anUpdatefunction contributed by chenzhongtao. -
Frank Wessels kindly contributed ARM 64 bit assembly, which gives a huge performance boost on this platform.
July 20, 2017
ReconstructData added to Encoder interface. This can cause compatibility issues if you implement your own Encoder. A simple workaround can be added:
func (e *YourEnc) ReconstructData(shards [][]byte) error {
return ReconstructData(shards)
}
You can of course also do your own implementation. The StreamEncoder handles this without modifying the interface. This is a good lesson on why returning interfaces is not a good design.
Usage
This section assumes you know the basics of Reed-Solomon encoding. A good start is this Backblaze blog post.
This package performs the calculation of the parity sets. The usage is therefore relatively simple.
First of all, you need to choose your distribution of data and parity shards. A 'good' distribution is very subjective, and will depend a lot on your usage scenario. A good starting point is above 5 and below 257 data shards (the maximum supported number), and the number of parity shards to be 2 or above, and below the number of data shards.
To create an encoder with 10 data shards (where your data goes) and 3 parity shards (calculated):
enc, err := reedsolomon.New(10, 3)
This encoder will work for all parity sets with this distribution of data and parity shards. The error will only be set if you specify 0 or negative values in any of the parameters, or if you specify more than 256 data shards.
If you will primarily be using it with one shard size it is recommended to use WithAutoGoroutines(shardSize) as an additional parameter. This will attempt to calculate the optimal number of goroutines to use for the best speed. It is not required that all shards are this size.
The you send and receive data is a simple slice of byte slices; [][]byte. In the example above, the top slice must have a length of 13.
data := make([][]byte, 13)
You should then fill the 10 first slices with equally sized data, and create parity shards that will be populated with parity data. In this case we create the data in memory, but you could for instance also use mmap to map files.
// Create all shards, size them at 50000 each
for i := range input {
data[i] := make([]byte, 50000)
}
// Fill some data into the data shards
for i, in := range data[:10] {
for j:= range in {
in[j] = byte((i+j)&0xff)
}
}
To populate the parity shards, you simply call Encode() with your data.
err = enc.Encode(data)
The only cases where you should get an error is, if the data shards aren't of equal size. The last 3 shards now contain parity data. You can verify this by calling Verify():
ok, err = enc.Verify(data)
The final (and important) part is to be able to reconstruct missing shards. For this to work, you need to know which parts of your data is missing. The encoder does not know which parts are invalid, so if data corruption is a likely scenario, you need to implement a hash check for each shard.
If a byte has changed in your set, and you don't know which it is, there is no way to reconstruct the data set.
To indicate missing data, you set the shard to nil before calling Reconstruct():
// Delete two data shards
data[3] = nil
data[7] = nil
// Reconstruct the missing shards
err := enc.Reconstruct(data)
The missing data and parity shards will be recreated. If more than 3 shards are missing, the reconstruction will fail.
If you are only interested in the data shards (for reading purposes) you can call ReconstructData():
// Delete two data shards
data[3] = nil
data[7] = nil
// Reconstruct just the missing data shards
err := enc.ReconstructData(data)
So to sum up reconstruction:
- The number of data/parity shards must match the numbers used for encoding.
- The order of shards must be the same as used when encoding.
- You may only supply data you know is valid.
- Invalid shards should be set to nil.
For complete examples of an encoder and decoder see the examples folder.
Splitting/Joining Data
You might have a large slice of data. To help you split this, there are some helper functions that can split and join a single byte slice.
bigfile, _ := ioutil.Readfile("myfile.data")
// Split the file
split, err := enc.Split(bigfile)
This will split the file into the number of data shards set when creating the encoder and create empty parity shards.
An important thing to note is that you have to keep track of the exact input size. If the size of the input isn't divisible by the number of data shards, extra zeros will be inserted in the last shard.
To join a data set, use the Join() function, which will join the shards and write it to the io.Writer you supply:
// Join a data set and write it to io.Discard.
err = enc.Join(io.Discard, data, len(bigfile))
Streaming/Merging
It might seem like a limitation that all data should be in memory, but an important property is that as long as the number of data/parity shards are the same, you can merge/split data sets, and they will remain valid as a separate set.
// Split the data set of 50000 elements into two of 25000
splitA := make([][]byte, 13)
splitB := make([][]byte, 13)
// Merge into a 100000 element set
merged := make([][]byte, 13)
for i := range data {
splitA[i] = data[i][:25000]
splitB[i] = data[i][25000:]
// Concatenate it to itself
merged[i] = append(make([]byte, 0, len(data[i])*2), data[i]...)
merged[i] = append(merged[i], data[i]...)
}
// Each part should still verify as ok.
ok, err := enc.Verify(splitA)
if ok && err == nil {
log.Println("splitA ok")
}
ok, err = enc.Verify(splitB)
if ok && err == nil {
log.Println("splitB ok")
}
ok, err = enc.Verify(merge)
if ok && err == nil {
log.Println("merge ok")
}
This means that if you have a data set that may not fit into memory, you can split processing into smaller blocks. For the best throughput, don't use too small blocks.
This also means that you can divide big input up into smaller blocks, and do reconstruction on parts of your data. This doesn't give the same flexibility of a higher number of data shards, but it will be much more performant.
Streaming API
There has been added support for a streaming API, to help perform fully streaming operations, which enables you to do the same operations, but on streams. To use the stream API, use NewStream function to create the encoding/decoding interfaces.
You can use WithConcurrentStreams to ready an interface that reads/writes concurrently from the streams.
You can specify the size of each operation using WithStreamBlockSize. This will set the size of each read/write operation.
Input is delivered as []io.Reader, output as []io.Writer, and functionality corresponds to the in-memory API. Each stream must supply the same amount of data, similar to how each slice must be similar size with the in-memory API. If an error occurs in relation to a stream, a StreamReadError or StreamWriteError will help you determine which stream was the offender.
There is no buffering or timeouts/retry specified. If you want to add that, you need to add it to the Reader/Writer.
For complete examples of a streaming encoder and decoder see the examples folder.
Advanced Options
You can modify internal options which affects how jobs are split between and processed by goroutines.
To create options, use the WithXXX functions. You can supply options to New, NewStream. If no Options are supplied, default options are used.
Example of how to supply options:
enc, err := reedsolomon.New(10, 3, WithMaxGoroutines(25))
Performance
Performance depends mainly on the number of parity shards. In rough terms, doubling the number of parity shards will double the encoding time.
Here are the throughput numbers with some different selections of data and parity shards. For reference each shard is 1MB random data, and 16 CPU cores are used for encoding.
| Data | Parity | Go MB/s | SSSE3 MB/s | AVX2 MB/s |
|---|---|---|---|---|
| 5 | 2 | 14287 | 66355 | 108755 |
| 8 | 8 | 5569 | 34298 | 70516 |
| 10 | 4 | 6766 | 48237 | 93875 |
| 50 | 20 | 1540 | 12130 | 22090 |
The throughput numbers here is the size of the encoded data and parity shards.
If runtime.GOMAXPROCS() is set to a value higher than 1, the encoder will use multiple goroutines to perform the calculations in Verify, Encode and Reconstruct.
Example of performance scaling on AMD Ryzen 3950X - 16 physical cores, 32 logical cores, AVX 2. The example uses 10 blocks with 1MB data each and 4 parity blocks.
| Threads | Speed |
|---|---|
| 1 | 9979 MB/s |
| 2 | 18870 MB/s |
| 4 | 33697 MB/s |
| 8 | 51531 MB/s |
| 16 | 59204 MB/s |
Benchmarking Reconstruct() followed by a Verify() (=all) versus just calling ReconstructData() (=data) gives the following result:
benchmark all MB/s data MB/s speedup
BenchmarkReconstruct10x2x10000-8 2011.67 10530.10 5.23x
BenchmarkReconstruct50x5x50000-8 4585.41 14301.60 3.12x
BenchmarkReconstruct10x2x1M-8 8081.15 28216.41 3.49x
BenchmarkReconstruct5x2x1M-8 5780.07 28015.37 4.85x
BenchmarkReconstruct10x4x1M-8 4352.56 14367.61 3.30x
BenchmarkReconstruct50x20x1M-8 1364.35 4189.79 3.07x
BenchmarkReconstruct10x4x16M-8 1484.35 5779.53 3.89x
Performance on AVX512
The performance on AVX512 has been accelerated for Intel CPUs. This gives speedups on a per-core basis typically up to 2x compared to AVX2 as can be seen in the following table:
[...]
This speedup has been achieved by computing multiple parity blocks in parallel as opposed to one after the other. In doing so it is possible to minimize the memory bandwidth required for loading all data shards. At the same time the calculations are performed in the 512-bit wide ZMM registers and the surplus of ZMM registers (32 in total) is used to keep more data around (most notably the matrix coefficients).
Performance on ARM64 NEON
By exploiting NEON instructions the performance for ARM has been accelerated. Below are the performance numbers for a single core on an EC2 m6g.16xlarge (Graviton2) instance (Amazon Linux 2):
BenchmarkGalois128K-64 119562 10028 ns/op 13070.78 MB/s
BenchmarkGalois1M-64 14380 83424 ns/op 12569.22 MB/s
BenchmarkGaloisXor128K-64 96508 12432 ns/op 10543.29 MB/s
BenchmarkGaloisXor1M-64 10000 100322 ns/op 10452.13 MB/s
Performance on ppc64le
The performance for ppc64le has been accelerated. This gives roughly a 10x performance improvement on this architecture as can been seen below:
benchmark old MB/s new MB/s speedup
BenchmarkGalois128K-160 948.87 8878.85 9.36x
BenchmarkGalois1M-160 968.85 9041.92 9.33x
BenchmarkGaloisXor128K-160 862.02 7905.00 9.17x
BenchmarkGaloisXor1M-160 784.60 6296.65 8.03x
asm2plan9s
asm2plan9s is used for assembling the AVX2 instructions into their BYTE/WORD/LONG equivalents.
Links
- Backblaze Open Sources Reed-Solomon Erasure Coding Source Code.
- JavaReedSolomon. Compatible java library by Backblaze.
- ocaml-reed-solomon-erasure. Compatible OCaml implementation.
- reedsolomon-c. C version, compatible with output from this package.
- Reed-Solomon Erasure Coding in Haskell. Haskell port of the package with similar performance.
- reed-solomon-erasure. Compatible Rust implementation.
- go-erasure. A similar library using cgo, slower in my tests.
- Screaming Fast Galois Field Arithmetic. Basis for SSE3 optimizations.
License
This code, as the original JavaReedSolomon is published under an MIT license. See LICENSE file for more information.
