theodesp / go-blooms

Simple and efficient bloom filter implementations in Go

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

Usage

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

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