README
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Bloom Filter
Bloom Filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970.
This implement is experimental to written Golang to prove the basic concept of Bloom Filter.
Install
go get github.com/kkdai/bloomfilter
Usage
//Create a couting bloom filter expect size 100, false detect rate 0.01
cbf := NewCountingBloomFilter(100, 0.01)
//Add item into cbf
cbf.Add([]byte("foo"))
cbf.Add([]byte("john"))
cbf.Add([]byte("tom"))
//test
fmt.Println("Test cbf:", cbf.Test([]byte("tom"))) //return "true"
//Remvoe item from cbf
cbf.Remove([]byte("john"))
//test again
fmt.Println("Test cbf:", cbf.Test([]byte("john"))) //return "false"
Inspired
Project52
It is one of my project 52.
License
This package is licensed under MIT license. See LICENSE for details.
Documentation
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Index ¶
Examples ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func CreateBloomFilter ¶
CreateBloomFilter the basic function to create bloom filter.
Types ¶
type CBF ¶
type CBF struct {
// contains filtered or unexported fields
}
CBF :Counting Bloom Filter
Example ¶
m := 5
h1 := func(input int) int {
return (input % m) + 1
}
var hashList [](func(index int) int)
hashList = append(hashList, h1)
var inputList = []int{100, 121, 456, 121, 987}
fmt.Println("Bloom Filter:", CreateBloomFilter(inputList, hashList, m))
Output:
func NewCountingBloomFilter ¶
NewCountingBloomFilter : Create a counting bloom filter with assigned expect element count and false detect rate.
Source Files
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