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:
import "github.com/seiflotfy/cuckoofilter"
cf := cuckoofilter.NewDefaultCuckooFilter()
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()
// count == 1
// Delete a string (and it a miss)
cf.Delete([]byte{"hello"})
count := cf.Count()
// count == 1
// Delete a string (a hit)
cf.Delete([]byte{"geeky ogre"})
count := cf.Count()
// count == 0##Documentation: "Cuckoo Filter on GoDoc"