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roaring

This is a go version of the Roaring bitmap data structure.

Roaring bitmaps are used by several major systems such as Apache Lucene and derivative systems such as Solr and Elasticsearch, Apache Druid (Incubating), LinkedIn Pinot, Netflix Atlas, Apache Spark, OpenSearchServer, Cloud Torrent, Whoosh, Pilosa, Microsoft Visual Studio Team Services (VSTS), and eBay's Apache Kylin. The YouTube SQL Engine, Google Procella, uses Roaring bitmaps for indexing.

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)

The roaring Go library is used by

This library is used in production in several systems, it is part of the Awesome Go collection.

There are also Java and C/C++ 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.

This code is licensed under Apache License, Version 2.0 (ASL2.0).

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
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/willf/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:

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:

	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:

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 run the following:

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

$ 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

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.

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

10 KiB Raw Blame History