https://github.com/liangyaopei/bloom

High performance bloom filter in golang
https://github.com/liangyaopei/bloom

algorithms bloom-filter golang

Last synced: 7 months ago
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High performance bloom filter in golang

• Host: GitHub
• URL: https://github.com/liangyaopei/bloom
• Owner: liangyaopei
• License: mit
• Created: 2020-07-28T06:15:05.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-07-31T10:56:15.000Z (about 6 years ago)
• Last Synced: 2024-06-19T04:08:28.345Z (about 2 years ago)
• Topics: algorithms, bloom-filter, golang
• Language: Go
• Homepage:
• Size: 6.84 KB
• Stars: 20
• Watchers: 0
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# High-performance and user-friendly bloom filter in Golang

This library provides bloom filter which has high performance and is concurrent safe. 

## Install

```

go get -u github.com/liangyaopei/bloom

```

## Usage

Users only need to specify the parameter of `m`,`k`,`race` and then can use it.

Example.

```go

    filter := bloom.New(1024, 3, false)

	// chaining operation

	filter.Add([]byte("Hello")).

		AddString("World").

		AddUInt16(uint16(16)).

		AddUInt32(uint32(32)).

		AddUInt64(uint64(64)).

		AddUint16Batch([]uint16{1, 2, 3})

	t.Logf("Hello exist:%t", filter.Test([]byte("Hello")))

	t.Logf("World exist:%t", filter.TestString("World"))

	t.Logf("uint 16 exist:%t", filter.TestUInt16(uint16(16)))

	t.Logf("uint 32 exist:%t", filter.TestUInt32(uint32(32)))

	t.Logf("uint 64 exist:%t", filter.TestUInt64(uint64(64)))

```

## Performance: high performance with low false positive rate

In `_bloom_test`, the `TestFalsePositive` function demonstrates that after inserting 1 million random string key, whose string length is 30 each, this library has 

a false positive ratio of 2.82%(0.028200).