mirror of
https://github.com/go-gitea/gitea.git
synced 2024-08-25 21:57:29 +02:00
* update github.com/PuerkitoBio/goquery * update github.com/alecthomas/chroma * update github.com/blevesearch/bleve/v2 * update github.com/caddyserver/certmagic * update github.com/go-enry/go-enry/v2 * update github.com/go-git/go-billy/v5 * update github.com/go-git/go-git/v5 * update github.com/go-redis/redis/v8 * update github.com/go-testfixtures/testfixtures/v3 * update github.com/jaytaylor/html2text * update github.com/json-iterator/go * update github.com/klauspost/compress * update github.com/markbates/goth * update github.com/mattn/go-isatty * update github.com/mholt/archiver/v3 * update github.com/microcosm-cc/bluemonday * update github.com/minio/minio-go/v7 * update github.com/prometheus/client_golang * update github.com/unrolled/render * update github.com/xanzy/go-gitlab * update github.com/yuin/goldmark * update github.com/yuin/goldmark-highlighting Co-authored-by: techknowlogick <techknowlogick@gitea.io>
406 lines
15 KiB
Markdown
Vendored
406 lines
15 KiB
Markdown
Vendored
roaring [![Build Status](https://travis-ci.org/RoaringBitmap/roaring.png)](https://travis-ci.org/RoaringBitmap/roaring) [![GoDoc](https://godoc.org/github.com/RoaringBitmap/roaring/roaring64?status.svg)](https://godoc.org/github.com/RoaringBitmap/roaring/roaring64) [![Go Report Card](https://goreportcard.com/badge/RoaringBitmap/roaring)](https://goreportcard.com/report/github.com/RoaringBitmap/roaring)
|
|
[![Build Status](https://cloud.drone.io/api/badges/RoaringBitmap/roaring/status.svg)](https://cloud.drone.io/RoaringBitmap/roaring)
|
|
![Go-CI](https://github.com/RoaringBitmap/roaring/workflows/Go-CI/badge.svg)
|
|
![Go-ARM-CI](https://github.com/RoaringBitmap/roaring/workflows/Go-ARM-CI/badge.svg)
|
|
![Go-Windows-CI](https://github.com/RoaringBitmap/roaring/workflows/Go-Windows-CI/badge.svg)
|
|
=============
|
|
|
|
This is a go version of the Roaring bitmap data structure.
|
|
|
|
|
|
|
|
Roaring bitmaps are used by several major systems such as [Apache Lucene][lucene] and derivative systems such as [Solr][solr] and
|
|
[Elasticsearch][elasticsearch], [Apache Druid (Incubating)][druid], [LinkedIn Pinot][pinot], [Netflix Atlas][atlas], [Apache Spark][spark], [OpenSearchServer][opensearchserver], [Cloud Torrent][cloudtorrent], [Whoosh][whoosh], [Pilosa][pilosa], [Microsoft Visual Studio Team Services (VSTS)][vsts], and eBay's [Apache Kylin][kylin]. The YouTube SQL Engine, [Google Procella](https://research.google/pubs/pub48388/), uses Roaring bitmaps for indexing.
|
|
|
|
[lucene]: https://lucene.apache.org/
|
|
[solr]: https://lucene.apache.org/solr/
|
|
[elasticsearch]: https://www.elastic.co/products/elasticsearch
|
|
[druid]: https://druid.apache.org/
|
|
[spark]: https://spark.apache.org/
|
|
[opensearchserver]: http://www.opensearchserver.com
|
|
[cloudtorrent]: https://github.com/jpillora/cloud-torrent
|
|
[whoosh]: https://bitbucket.org/mchaput/whoosh/wiki/Home
|
|
[pilosa]: https://www.pilosa.com/
|
|
[kylin]: http://kylin.apache.org/
|
|
[pinot]: http://github.com/linkedin/pinot/wiki
|
|
[vsts]: https://www.visualstudio.com/team-services/
|
|
[atlas]: https://github.com/Netflix/atlas
|
|
|
|
Roaring bitmaps are found to work well in many important applications:
|
|
|
|
> Use Roaring for bitmap compression whenever possible. Do not use other bitmap compression methods ([Wang et al., SIGMOD 2017](http://db.ucsd.edu/wp-content/uploads/2017/03/sidm338-wangA.pdf))
|
|
|
|
|
|
The ``roaring`` Go library is used by
|
|
* [Cloud Torrent](https://github.com/jpillora/cloud-torrent)
|
|
* [runv](https://github.com/hyperhq/runv)
|
|
* [InfluxDB](https://www.influxdata.com)
|
|
* [Pilosa](https://www.pilosa.com/)
|
|
* [Bleve](http://www.blevesearch.com)
|
|
* [lindb](https://github.com/lindb/lindb)
|
|
* [Elasticell](https://github.com/deepfabric/elasticell)
|
|
* [SourceGraph](https://github.com/sourcegraph/sourcegraph)
|
|
* [M3](https://github.com/m3db/m3)
|
|
* [trident](https://github.com/NetApp/trident)
|
|
|
|
|
|
This library is used in production in several systems, it is part of the [Awesome Go collection](https://awesome-go.com).
|
|
|
|
|
|
There are also [Java](https://github.com/RoaringBitmap/RoaringBitmap) and [C/C++](https://github.com/RoaringBitmap/CRoaring) versions. The Java, C, C++ and Go version are binary compatible: e.g, you can save bitmaps
|
|
from a Java program and load them back in Go, and vice versa. We have a [format specification](https://github.com/RoaringBitmap/RoaringFormatSpec).
|
|
|
|
|
|
This code is licensed under Apache License, Version 2.0 (ASL2.0).
|
|
|
|
Copyright 2016-... by the authors.
|
|
|
|
When should you use a bitmap?
|
|
===================================
|
|
|
|
|
|
Sets are a fundamental abstraction in
|
|
software. They can be implemented in various
|
|
ways, as hash sets, as trees, and so forth.
|
|
In databases and search engines, sets are often an integral
|
|
part of indexes. For example, we may need to maintain a set
|
|
of all documents or rows (represented by numerical identifier)
|
|
that satisfy some property. Besides adding or removing
|
|
elements from the set, we need fast functions
|
|
to compute the intersection, the union, the difference between sets, and so on.
|
|
|
|
|
|
To implement a set
|
|
of integers, a particularly appealing strategy is the
|
|
bitmap (also called bitset or bit vector). Using n bits,
|
|
we can represent any set made of the integers from the range
|
|
[0,n): the ith bit is set to one if integer i is present in the set.
|
|
Commodity processors use words of W=32 or W=64 bits. By combining many such words, we can
|
|
support large values of n. Intersections, unions and differences can then be implemented
|
|
as bitwise AND, OR and ANDNOT operations.
|
|
More complicated set functions can also be implemented as bitwise operations.
|
|
|
|
When the bitset approach is applicable, it can be orders of
|
|
magnitude faster than other possible implementation of a set (e.g., as a hash set)
|
|
while using several times less memory.
|
|
|
|
However, a bitset, even a compressed one is not always applicable. For example, if
|
|
you have 1000 random-looking integers, then a simple array might be the best representation.
|
|
We refer to this case as the "sparse" scenario.
|
|
|
|
When should you use compressed bitmaps?
|
|
===================================
|
|
|
|
An uncompressed BitSet can use a lot of memory. For example, if you take a BitSet
|
|
and set the bit at position 1,000,000 to true and you have just over 100kB. That is over 100kB
|
|
to store the position of one bit. This is wasteful even if you do not care about memory:
|
|
suppose that you need to compute the intersection between this BitSet and another one
|
|
that has a bit at position 1,000,001 to true, then you need to go through all these zeroes,
|
|
whether you like it or not. That can become very wasteful.
|
|
|
|
This being said, there are definitively cases where attempting to use compressed bitmaps is wasteful.
|
|
For example, if you have a small universe size. E.g., your bitmaps represent sets of integers
|
|
from [0,n) where n is small (e.g., n=64 or n=128). If you are able to uncompressed BitSet and
|
|
it does not blow up your memory usage, then compressed bitmaps are probably not useful
|
|
to you. In fact, if you do not need compression, then a BitSet offers remarkable speed.
|
|
|
|
The sparse scenario is another use case where compressed bitmaps should not be used.
|
|
Keep in mind that random-looking data is usually not compressible. E.g., if you have a small set of
|
|
32-bit random integers, it is not mathematically possible to use far less than 32 bits per integer,
|
|
and attempts at compression can be counterproductive.
|
|
|
|
How does Roaring compares with the alternatives?
|
|
==================================================
|
|
|
|
|
|
Most alternatives to Roaring are part of a larger family of compressed bitmaps that are run-length-encoded
|
|
bitmaps. They identify long runs of 1s or 0s and they represent them with a marker word.
|
|
If you have a local mix of 1s and 0, you use an uncompressed word.
|
|
|
|
There are many formats in this family:
|
|
|
|
* Oracle's BBC is an obsolete format at this point: though it may provide good compression,
|
|
it is likely much slower than more recent alternatives due to excessive branching.
|
|
* WAH is a patented variation on BBC that provides better performance.
|
|
* Concise is a variation on the patented WAH. It some specific instances, it can compress
|
|
much better than WAH (up to 2x better), but it is generally slower.
|
|
* EWAH is both free of patent, and it is faster than all the above. On the downside, it
|
|
does not compress quite as well. It is faster because it allows some form of "skipping"
|
|
over uncompressed words. So though none of these formats are great at random access, EWAH
|
|
is better than the alternatives.
|
|
|
|
|
|
|
|
There is a big problem with these formats however that can hurt you badly in some cases: there is no random access. If you want to check whether a given value is present in the set, you have to start from the beginning and "uncompress" the whole thing. This means that if you want to intersect a big set with a large set, you still have to uncompress the whole big set in the worst case...
|
|
|
|
Roaring solves this problem. It works in the following manner. It divides the data into chunks of 2<sup>16</sup> integers
|
|
(e.g., [0, 2<sup>16</sup>), [2<sup>16</sup>, 2 x 2<sup>16</sup>), ...). Within a chunk, it can use an uncompressed bitmap, a simple list of integers,
|
|
or a list of runs. Whatever format it uses, they all allow you to check for the present of any one value quickly
|
|
(e.g., with a binary search). The net result is that Roaring can compute many operations much faster than run-length-encoded
|
|
formats like WAH, EWAH, Concise... Maybe surprisingly, Roaring also generally offers better compression ratios.
|
|
|
|
|
|
|
|
|
|
|
|
### References
|
|
|
|
- Daniel Lemire, Owen Kaser, Nathan Kurz, Luca Deri, Chris O'Hara, François Saint-Jacques, Gregory Ssi-Yan-Kai, Roaring Bitmaps: Implementation of an Optimized Software Library, Software: Practice and Experience 48 (4), 2018 [arXiv:1709.07821](https://arxiv.org/abs/1709.07821)
|
|
- Samy Chambi, Daniel Lemire, Owen Kaser, Robert Godin,
|
|
Better bitmap performance with Roaring bitmaps,
|
|
Software: Practice and Experience 46 (5), 2016.
|
|
http://arxiv.org/abs/1402.6407 This paper used data from http://lemire.me/data/realroaring2014.html
|
|
- Daniel Lemire, Gregory Ssi-Yan-Kai, Owen Kaser, Consistently faster and smaller compressed bitmaps with Roaring, Software: Practice and Experience 46 (11), 2016. http://arxiv.org/abs/1603.06549
|
|
|
|
|
|
### Dependencies
|
|
|
|
Dependencies are fetched automatically by giving the `-t` flag to `go get`.
|
|
|
|
they include
|
|
- github.com/bits-and-blooms/bitset
|
|
- github.com/mschoch/smat
|
|
- github.com/glycerine/go-unsnap-stream
|
|
- github.com/philhofer/fwd
|
|
- github.com/jtolds/gls
|
|
|
|
Note that the smat library requires Go 1.6 or better.
|
|
|
|
#### Installation
|
|
|
|
- go get -t github.com/RoaringBitmap/roaring
|
|
|
|
|
|
### Example
|
|
|
|
Here is a simplified but complete example:
|
|
|
|
```go
|
|
package main
|
|
|
|
import (
|
|
"fmt"
|
|
"github.com/RoaringBitmap/roaring"
|
|
"bytes"
|
|
)
|
|
|
|
|
|
func main() {
|
|
// example inspired by https://github.com/fzandona/goroar
|
|
fmt.Println("==roaring==")
|
|
rb1 := roaring.BitmapOf(1, 2, 3, 4, 5, 100, 1000)
|
|
fmt.Println(rb1.String())
|
|
|
|
rb2 := roaring.BitmapOf(3, 4, 1000)
|
|
fmt.Println(rb2.String())
|
|
|
|
rb3 := roaring.New()
|
|
fmt.Println(rb3.String())
|
|
|
|
fmt.Println("Cardinality: ", rb1.GetCardinality())
|
|
|
|
fmt.Println("Contains 3? ", rb1.Contains(3))
|
|
|
|
rb1.And(rb2)
|
|
|
|
rb3.Add(1)
|
|
rb3.Add(5)
|
|
|
|
rb3.Or(rb1)
|
|
|
|
// computes union of the three bitmaps in parallel using 4 workers
|
|
roaring.ParOr(4, rb1, rb2, rb3)
|
|
// computes intersection of the three bitmaps in parallel using 4 workers
|
|
roaring.ParAnd(4, rb1, rb2, rb3)
|
|
|
|
|
|
// prints 1, 3, 4, 5, 1000
|
|
i := rb3.Iterator()
|
|
for i.HasNext() {
|
|
fmt.Println(i.Next())
|
|
}
|
|
fmt.Println()
|
|
|
|
// next we include an example of serialization
|
|
buf := new(bytes.Buffer)
|
|
rb1.WriteTo(buf) // we omit error handling
|
|
newrb:= roaring.New()
|
|
newrb.ReadFrom(buf)
|
|
if rb1.Equals(newrb) {
|
|
fmt.Println("I wrote the content to a byte stream and read it back.")
|
|
}
|
|
// you can iterate over bitmaps using ReverseIterator(), Iterator, ManyIterator()
|
|
}
|
|
```
|
|
|
|
If you wish to use serialization and handle errors, you might want to
|
|
consider the following sample of code:
|
|
|
|
```go
|
|
rb := BitmapOf(1, 2, 3, 4, 5, 100, 1000)
|
|
buf := new(bytes.Buffer)
|
|
size,err:=rb.WriteTo(buf)
|
|
if err != nil {
|
|
t.Errorf("Failed writing")
|
|
}
|
|
newrb:= New()
|
|
size,err=newrb.ReadFrom(buf)
|
|
if err != nil {
|
|
t.Errorf("Failed reading")
|
|
}
|
|
if ! rb.Equals(newrb) {
|
|
t.Errorf("Cannot retrieve serialized version")
|
|
}
|
|
```
|
|
|
|
Given N integers in [0,x), then the serialized size in bytes of
|
|
a Roaring bitmap should never exceed this bound:
|
|
|
|
`` 8 + 9 * ((long)x+65535)/65536 + 2 * N ``
|
|
|
|
That is, given a fixed overhead for the universe size (x), Roaring
|
|
bitmaps never use more than 2 bytes per integer. You can call
|
|
``BoundSerializedSizeInBytes`` for a more precise estimate.
|
|
|
|
### 64-bit Roaring
|
|
|
|
By default, roaring is used to stored unsigned 32-bit integers. However, we also offer
|
|
an extension dedicated to 64-bit integers. It supports roughly the same functions:
|
|
|
|
```go
|
|
package main
|
|
|
|
import (
|
|
"fmt"
|
|
"github.com/RoaringBitmap/roaring/roaring64"
|
|
"bytes"
|
|
)
|
|
|
|
|
|
func main() {
|
|
// example inspired by https://github.com/fzandona/goroar
|
|
fmt.Println("==roaring64==")
|
|
rb1 := roaring64.BitmapOf(1, 2, 3, 4, 5, 100, 1000)
|
|
fmt.Println(rb1.String())
|
|
|
|
rb2 := roaring64.BitmapOf(3, 4, 1000)
|
|
fmt.Println(rb2.String())
|
|
|
|
rb3 := roaring64.New()
|
|
fmt.Println(rb3.String())
|
|
|
|
fmt.Println("Cardinality: ", rb1.GetCardinality())
|
|
|
|
fmt.Println("Contains 3? ", rb1.Contains(3))
|
|
|
|
rb1.And(rb2)
|
|
|
|
rb3.Add(1)
|
|
rb3.Add(5)
|
|
|
|
rb3.Or(rb1)
|
|
|
|
|
|
|
|
// prints 1, 3, 4, 5, 1000
|
|
i := rb3.Iterator()
|
|
for i.HasNext() {
|
|
fmt.Println(i.Next())
|
|
}
|
|
fmt.Println()
|
|
|
|
// next we include an example of serialization
|
|
buf := new(bytes.Buffer)
|
|
rb1.WriteTo(buf) // we omit error handling
|
|
newrb:= roaring64.New()
|
|
newrb.ReadFrom(buf)
|
|
if rb1.Equals(newrb) {
|
|
fmt.Println("I wrote the content to a byte stream and read it back.")
|
|
}
|
|
// you can iterate over bitmaps using ReverseIterator(), Iterator, ManyIterator()
|
|
}
|
|
```
|
|
|
|
Only the 32-bit roaring format is standard and cross-operable between Java, C++, C and Go. There is no guarantee that the 64-bit versions are compatible.
|
|
|
|
### Documentation
|
|
|
|
Current documentation is available at http://godoc.org/github.com/RoaringBitmap/roaring and http://godoc.org/github.com/RoaringBitmap/roaring64
|
|
|
|
### Goroutine safety
|
|
|
|
In general, it should not generally be considered safe to access
|
|
the same bitmaps using different goroutines--they are left
|
|
unsynchronized for performance. Should you want to access
|
|
a Bitmap from more than one goroutine, you should
|
|
provide synchronization. Typically this is done by using channels to pass
|
|
the *Bitmap around (in Go style; so there is only ever one owner),
|
|
or by using `sync.Mutex` to serialize operations on Bitmaps.
|
|
|
|
### Coverage
|
|
|
|
We test our software. For a report on our test coverage, see
|
|
|
|
https://coveralls.io/github/RoaringBitmap/roaring?branch=master
|
|
|
|
### Benchmark
|
|
|
|
Type
|
|
|
|
go test -bench Benchmark -run -
|
|
|
|
To run benchmarks on [Real Roaring Datasets](https://github.com/RoaringBitmap/real-roaring-datasets)
|
|
run the following:
|
|
|
|
```sh
|
|
go get github.com/RoaringBitmap/real-roaring-datasets
|
|
BENCH_REAL_DATA=1 go test -bench BenchmarkRealData -run -
|
|
```
|
|
|
|
### Iterative use
|
|
|
|
You can use roaring with gore:
|
|
|
|
- go get -u github.com/motemen/gore
|
|
- Make sure that ``$GOPATH/bin`` is in your ``$PATH``.
|
|
- go get github.com/RoaringBitmap/roaring
|
|
|
|
```go
|
|
$ gore
|
|
gore version 0.2.6 :help for help
|
|
gore> :import github.com/RoaringBitmap/roaring
|
|
gore> x:=roaring.New()
|
|
gore> x.Add(1)
|
|
gore> x.String()
|
|
"{1}"
|
|
```
|
|
|
|
|
|
### Fuzzy testing
|
|
|
|
You can help us test further the library with fuzzy testing:
|
|
|
|
go get github.com/dvyukov/go-fuzz/go-fuzz
|
|
go get github.com/dvyukov/go-fuzz/go-fuzz-build
|
|
go test -tags=gofuzz -run=TestGenerateSmatCorpus
|
|
go-fuzz-build github.com/RoaringBitmap/roaring
|
|
go-fuzz -bin=./roaring-fuzz.zip -workdir=workdir/ -timeout=200 -func FuzzSmat
|
|
|
|
Let it run, and if the # of crashers is > 0, check out the reports in
|
|
the workdir where you should be able to find the panic goroutine stack
|
|
traces.
|
|
|
|
You may also replace `-func FuzzSmat` by `-func FuzzSerializationBuffer` or `-func FuzzSerializationStream`.
|
|
|
|
### Alternative in Go
|
|
|
|
There is a Go version wrapping the C/C++ implementation https://github.com/RoaringBitmap/gocroaring
|
|
|
|
For an alternative implementation in Go, see https://github.com/fzandona/goroar
|
|
The two versions were written independently.
|
|
|
|
|
|
### Mailing list/discussion group
|
|
|
|
https://groups.google.com/forum/#!forum/roaring-bitmaps
|