You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
202 lines
9.8 KiB
202 lines
9.8 KiB
# Reed-Solomon
|
|
[![GoDoc][1]][2] [![Build Status][3]][4]
|
|
|
|
[1]: https://godoc.org/github.com/klauspost/reedsolomon?status.svg
|
|
[2]: https://godoc.org/github.com/klauspost/reedsolomon
|
|
[3]: https://travis-ci.org/klauspost/reedsolomon.svg?branch=master
|
|
[4]: https://travis-ci.org/klauspost/reedsolomon
|
|
|
|
Reed-Solomon Erasure Coding in Go, with speeds exceeding 1GB/s/cpu core implemented in pure Go.
|
|
|
|
This is a golang port of the [JavaReedSolomon](https://github.com/Backblaze/JavaReedSolomon) library released by [Backblaze](http://backblaze.com), with some additional optimizations.
|
|
|
|
For an introduction on erasure coding, see the post on the [Backblaze blog](https://www.backblaze.com/blog/reed-solomon/).
|
|
|
|
Package home: https://github.com/klauspost/reedsolomon
|
|
|
|
Godoc: https://godoc.org/github.com/klauspost/reedsolomon
|
|
|
|
# Installation
|
|
To get the package use the standard:
|
|
```bash
|
|
go get github.com/klauspost/reedsolomon
|
|
```
|
|
|
|
# Usage
|
|
|
|
This section assumes you know the basics of Reed-Solomon encoding. A good start is this [Backblaze blog post](https://www.backblaze.com/blog/reed-solomon/).
|
|
|
|
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):
|
|
```Go
|
|
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.
|
|
|
|
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.
|
|
```Go
|
|
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](https://github.com/edsrzf/mmap-go) to map files.
|
|
|
|
```Go
|
|
// 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.
|
|
```Go
|
|
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()`:
|
|
|
|
```Go
|
|
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()`:
|
|
|
|
```Go
|
|
// 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.
|
|
|
|
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](https://github.com/klauspost/reedsolomon/tree/master/examples).
|
|
|
|
# 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.
|
|
|
|
```Go
|
|
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 diviable 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:
|
|
```Go
|
|
// 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.
|
|
|
|
```Go
|
|
// 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:]
|
|
|
|
// Concencate 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 a fully 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`](https://godoc.org/github.com/klauspost/reedsolomon#NewStream) function to create the encoding/decoding interfaces. You can use [`NewStreamC`](https://godoc.org/github.com/klauspost/reedsolomon#NewStreamC) to ready an interface that reads/writes concurrently from the streams.
|
|
|
|
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`](https://godoc.org/github.com/klauspost/reedsolomon#StreamReadError) or [`StreamWriteError`](https://godoc.org/github.com/klauspost/reedsolomon#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](https://github.com/klauspost/reedsolomon/tree/master/examples).
|
|
|
|
|
|
# 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 2 CPU cores are used for encoding.
|
|
|
|
| Data | Parity | Parity | MB/s | SSSE3 MB/s | SSSE3 Speed | Rel. Speed |
|
|
|------|--------|--------|--------|-------------|-------------|------------|
|
|
| 5 | 2 | 40% | 576,11 | 2599,2 | 451% | 100,00% |
|
|
| 10 | 2 | 20% | 587,73 | 3100,28 | 528% | 102,02% |
|
|
| 10 | 4 | 40% | 298,38 | 2470,97 | 828% | 51,79% |
|
|
| 50 | 20 | 40% | 59,81 | 713,28 | 1193% | 10,38% |
|
|
|
|
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 Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz - 4 physical cores, 8 logical cores. The example uses 10 blocks with 16MB data each and 4 parity blocks.
|
|
|
|
| Threads | MB/s | Speed |
|
|
|---------|---------|-------|
|
|
| 1 | 1355,11 | 100% |
|
|
| 2 | 2339,78 | 172% |
|
|
| 4 | 3179,33 | 235% |
|
|
| 8 | 4346,18 | 321% |
|
|
|
|
# asm2plan9s
|
|
|
|
[asm2plan9s](https://github.com/fwessels/asm2plan9s) is used for assembling the AVX2 instructions into their BYTE/WORD/LONG equivalents.
|
|
|
|
# Links
|
|
* [Backblaze Open Sources Reed-Solomon Erasure Coding Source Code](https://www.backblaze.com/blog/reed-solomon/).
|
|
* [JavaReedSolomon](https://github.com/Backblaze/JavaReedSolomon). Compatible java library by Backblaze.
|
|
* [go-erasure](https://github.com/somethingnew2-0/go-erasure). A similar library using cgo, slower in my tests.
|
|
* [rsraid](https://github.com/goayame/rsraid). A similar library written in Go. Slower, but supports more shards.
|
|
* [Screaming Fast Galois Field Arithmetic](http://www.snia.org/sites/default/files2/SDC2013/presentations/NewThinking/EthanMiller_Screaming_Fast_Galois_Field%20Arithmetic_SIMD%20Instructions.pdf). Basis for SSE3 optimizations.
|
|
|
|
# License
|
|
|
|
This code, as the original [JavaReedSolomon](https://github.com/Backblaze/JavaReedSolomon) is published under an MIT license. See LICENSE file for more information.
|
|
|