bloomfilter

Bloom Filter

This repository contains a simple implementation of a Bloom Filter using with roaring bitmaps

Overview

A Bloom Filter is a probabilistic data structure used for efficient membership testing. It can quickly determine whether an element is likely to be a member of a set or not. However, it may introduce false positives (indicating that an element is a member when it's not) with a certain probability.

This Bloom Filter implementation provides the following features:

• Add: Adds an element to the Bloom Filter.
• Contains: Checks if an element is likely to be a member of the Bloom Filter.
• FalsePositiveRate: Returns the target false positive rate of the Bloom Filter.
• CurrentFalsePositiveRate: Returns the current false positive rate of the Bloom Filter.
• IsEmpty: Checks if the Bloom Filter is empty.
• IsFull: Checks if the Bloom Filter is full.
• Size: Returns the number of elements inserted into the Bloom Filter.
• Len: Returns the number of bits set in the Bloom Filter.

Getting Started

Installation

To use this Bloom Filter implementation, you need to have Go installed. You can clone this repository or use Go modules to import it into your project.

go get github.com/username/repo/bloomfilter

Usage

Here is an example of how to use the Bloom Filter:

package main

import (
	"fmt"

	"github.com/selam/roaring-bloom"
)

func main() {
	maxSize := uint64(1000)
	falsePositiveRate := 0.001
	bf := bloomfilter.New(maxSize, falsePositiveRate)

	bf.Add([]byte("value1"))
	bf.Add([]byte("value2"))

	fmt.Println(bf.Contains([]byte("value1"))) // true
	fmt.Println(bf.Contains([]byte("value3"))) // false

}

Contributing

Contributions to this Bloom Filter implementation are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

Performance

The performance of the Bloom Filter can vary based on the size of the filter and the false positive rate. It provides a trade-off between space efficiency and accuracy. It is recommended to adjust the size and false positive rate according to the specific use case to achieve optimal performance.

License

This Bloom Filter implementation is licensed under the Apache License 2.0. See the LICENSE file for details.