https://github.com/theodesp/go-blooms

Simple and efficient bloom filter implementations in Go
https://github.com/theodesp/go-blooms

bloom-filter data-structures probabilistic-data-structures

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Simple and efficient bloom filter implementations in Go

• Host: GitHub
• URL: https://github.com/theodesp/go-blooms
• Owner: theodesp
• License: mit
• Created: 2017-12-16T18:53:11.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2018-09-26T14:30:59.000Z (about 6 years ago)
• Last Synced: 2024-10-03T07:23:36.357Z (3 months ago)
• Topics: bloom-filter, data-structures, probabilistic-data-structures
• Language: Go
• Homepage: https://godoc.org/github.com/theodesp/go-blooms
• Size: 7.81 KB
• Stars: 8
• Watchers: 1
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
go-blooms

---

From Wiki

>Bloom Filter: A space-efficient probabilistic data structure that is used to test whether an element is a member of

a set. False positive matches are possible, but false negatives are not; i.e. a query returns either "possibly in set"

or "definitely not in set". Elements can be added to the set, but not removed.

This bloom filter implementation is backed by bool slice for simplicity.

And the hashing functions used are fnv and murmur both 64 bit versions.

## Installation

```go

go get -u github.com/theodesp/go-blooms

```

## Usage

```go

package example

import "github.com/theodesp/go-blooms"

const (

  size = 64 * 1024

)

bf := go_blooms.New(size, go_blooms.DefaultHashFunctions)

value := "hello"

bf.Add([]byte(value)) // we accept only a byte slice

if bf.Test([]byte(value)) { // probably true, could be false

  // whatever

}

anotherValue := "world"

if bf.Test([]byte(anotherValue) { // Bloom filter guarantees that anotherValue is not in the set

  panic("This should never happen")

}

```

## Complexity

**Time**

If we are using a bloom filter with  bits and  hash function,

insertion and search will both take  time.

In both cases, we just need to run the input through all of

the hash functions. Then we just check the output bits.

|  Operation | Complexity  |

|---|---|

|  insertion |  O(k)  |

|  search |  O(k)  |

**Space**

The space of the actual data structure (what holds the data).

|  Complexity |

|---|

|  O(m) |

Where `m` is the size of the slice.

## Benchmarks

m=1024, k=3

```bash

PASS: bloomFilter_test.go:66: MySuite.BenchmarkAdd      10000000               231 ns/op

PASS: bloomFilter_test.go:74: MySuite.BenchmarkTest     10000000               231 ns/op

```

## License

Copyright © 2017 Theo Despoudis

MIT license