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README.md

Cuckoo Filter

GoDoc CodeHunt.io

Cuckoo filter is a Bloom filter replacement for approximated set-membership queries. While Bloom filters are well-known space-efficient data structures to serve queries like "if item x is in a set?", they do not support deletion. Their variances to enable deletion (like counting Bloom filters) usually require much more space.

Cuckoo filters provide the flexibility to add and remove items dynamically. A cuckoo filter is based on cuckoo hashing (and therefore named as cuckoo filter). It is essentially a cuckoo hash table storing each key's fingerprint. Cuckoo hash tables can be highly compact, thus a cuckoo filter could use less space than conventional Bloom filters, for applications that require low false positive rates (< 3%).

For details about the algorithm and citations please use this article for now

"Cuckoo Filter: Better Than Bloom" by Bin Fan, Dave Andersen and Michael Kaminsky

Note

This implementation uses a a static bucket size of 4 fingerprints and a fingerprint size of 1 byte based on my understanding of an optimal bucket/fingerprint/size ratio from the aforementioned paper.

Example usage:

package main

import "fmt"
import "github.com/seiflotfy/cuckoofilter"

func main() {
  cf := cuckoo.NewFilter(1000)
  cf.InsertUnique([]byte("geeky ogre"))

  // Lookup a string (and it a miss) if it exists in the cuckoofilter
  cf.Lookup([]byte("hello"))

  count := cf.Count()
  fmt.Println(count) // count == 1

  // Delete a string (and it a miss)
  cf.Delete([]byte("hello"))

  count = cf.Count()
  fmt.Println(count) // count == 1

  // Delete a string (a hit)
  cf.Delete([]byte("geeky ogre"))

  count = cf.Count()
  fmt.Println(count) // count == 0
  
  cf.Reset()    // reset
}

Documentation:

"Cuckoo Filter on GoDoc"

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Cuckoo Filter: Practically Better Than Bloom

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