README
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Cuckoo Filter
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"
Documentation
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Overview ¶
Package cuckoofilter provides a Cuckoo Filter, 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:
"Cuckoo Filter: Better Than Bloom" by Bin Fan, Dave Andersen and Michael Kaminsky (https://www.cs.cmu.edu/~dga/papers/cuckoo-conext2014.pdf)
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.
Index ¶
Constants ¶
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Variables ¶
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Functions ¶
This section is empty.
Types ¶
type CuckooFilter ¶
type CuckooFilter struct {
// contains filtered or unexported fields
}
CuckooFilter represents a probabalistic counter
func NewCuckooFilter ¶
func NewCuckooFilter(capacity uint) *CuckooFilter
NewCuckooFilter returns a new cuckoofilter with a given capacity
func NewDefaultCuckooFilter ¶
func NewDefaultCuckooFilter() *CuckooFilter
NewDefaultCuckooFilter returns a new cuckoofilter with the default capacity of 1000000
func (*CuckooFilter) Count ¶
func (cf *CuckooFilter) Count() uint
GetCount returns the number of items in the counter
func (*CuckooFilter) Delete ¶
func (cf *CuckooFilter) Delete(data []byte) bool
Delete data from counter if exists and return if deleted or not
func (*CuckooFilter) Insert ¶
func (cf *CuckooFilter) Insert(data []byte) bool
Insert inserts data into the counter and returns true upon success
func (*CuckooFilter) InsertUnique ¶
func (cf *CuckooFilter) InsertUnique(data []byte) bool
InsertUnique inserts data into the counter if not exists and returns true upon success
func (*CuckooFilter) Lookup ¶
func (cf *CuckooFilter) Lookup(data []byte) bool
Lookup returns true if data is in the counter
Source Files