bloomfilter

README

Bloom Filter Implementation in Go

I have implemented a Bloom filter data structure in Go. A Bloom filter is a space-efficient probabilistic data structure used to test whether an element is a member of a set.

Features

• Thread-safe operations
• Customizable size and number of hash functions
• Estimated false positive rate calculation
• Union operation for combining filters

Running the Demo

To run the demonstration program:

go run cmd/main.go

Running Tests

To run the unit tests:

go test ./...

License

This project is available under the MIT License.

Documentation

Index

• type BloomFilter
  • func Deserialize(data []byte) *BloomFilter
  • func New(size uint, numHashes int) *BloomFilter
  • func (bf *BloomFilter) Add(item []byte)
  • func (bf *BloomFilter) Contains(item []byte) bool
  • func (bf *BloomFilter) Count() uint
  • func (bf *BloomFilter) EstimatedFalsePositiveRate() float64
  • func (bf *BloomFilter) OptimalNumHashes(expectedElements uint) int
  • func (bf *BloomFilter) Reset()
  • func (bf *BloomFilter) Serialize() []byte
  • func (bf *BloomFilter) Union(other *BloomFilter) *BloomFilter

Types

type BloomFilter

type BloomFilter struct {
	// contains filtered or unexported fields
}

func Deserialize

func Deserialize(data []byte) *BloomFilter

func New

func New(size uint, numHashes int) *BloomFilter

func (*BloomFilter) Add

func (bf *BloomFilter) Add(item []byte)

func (*BloomFilter) Contains

func (bf *BloomFilter) Contains(item []byte) bool

func (*BloomFilter) Count

func (bf *BloomFilter) Count() uint

func (*BloomFilter) EstimatedFalsePositiveRate

func (bf *BloomFilter) EstimatedFalsePositiveRate() float64

func (*BloomFilter) OptimalNumHashes

func (bf *BloomFilter) OptimalNumHashes(expectedElements uint) int

func (*BloomFilter) Reset

func (bf *BloomFilter) Reset()

func (*BloomFilter) Serialize

func (bf *BloomFilter) Serialize() []byte

func (*BloomFilter) Union

func (bf *BloomFilter) Union(other *BloomFilter) *BloomFilter