cuckoofilter alternatives and similar packages
Based on the "Data Structures" category.
Alternatively, view cuckoofilter alternatives based on common mentions on social networks and blogs.
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gods
GoDS (Go Data Structures) - Sets, Lists, Stacks, Maps, Trees, Queues, and much more -
go-datastructures
A collection of useful, performant, and threadsafe Go datastructures. -
golang-set
A simple, battle-tested and generic set type for the Go language. Trusted by Docker, 1Password, Ethereum and Hashicorp. -
roaring
Roaring bitmaps in Go (golang), used by InfluxDB, Bleve, DataDog -
willf/bloom
Go package implementing Bloom filters, used by Milvus and Beego. -
gocache
☔️ A complete Go cache library that brings you multiple ways of managing your caches -
boomfilters
Probabilistic data structures for processing continuous, unbounded streams. -
gostl
Data structure and algorithm library for go, designed to provide functions similar to C++ STL -
hyperloglog
HyperLogLog with lots of sugar (Sparse, LogLog-Beta bias correction and TailCut space reduction) brought to you by Axiom -
trie
Data structure and relevant algorithms for extremely fast prefix/fuzzy string searching. -
ttlcache
An in-memory cache with item expiration and generics [Moved to: https://github.com/jellydator/ttlcache] -
go-geoindex
Go native library for fast point tracking and K-Nearest queries -
Bloomfilter
Face-meltingly fast, thread-safe, marshalable, unionable, probability- and optimal-size-calculating Bloom filter in go -
go-adaptive-radix-tree
Adaptive Radix Trees implemented in Go -
goconcurrentqueue
Go concurrent-safe, goroutine-safe, thread-safe queue -
hilbert
Go package for mapping values to and from space-filling curves, such as Hilbert and Peano curves. -
levenshtein
Go implementation to calculate Levenshtein Distance. -
cuckoo-filter
Cuckoo Filter go implement, better than Bloom Filter, configurable and space optimized 布谷鸟过滤器的Go实现,优于布隆过滤器,可以定制化过滤器参数,并进行了空间优化 -
ring
Package ring provides a high performance and thread safe Go implementation of a bloom filter. -
go-rquad
:pushpin: State of the art point location and neighbour finding algorithms for region quadtrees, in Go -
set
A simple Set data structure implementation in Go (Golang) using LinkedHashMap. -
nan
Zero allocation Nullable structures in one library with handy conversion functions, marshallers and unmarshallers -
goset
Set is a useful collection but there is no built-in implementation in Go lang. -
hide
ID type with marshalling to/from hash to prevent sending IDs to clients.
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README
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
Implementation details
The paper cited above leaves several parameters to choose. In this implementation
- Every element has 2 possible bucket indices
- Buckets have a static size of 4 fingerprints
- Fingerprints have a static size of 8 bits
1 and 2 are suggested to be the optimum by the authors. The choice of 3 comes down to the desired false positive rate. Given a target false positive rate of r
and a bucket size b
, they suggest choosing the fingerprint size f
using
f >= log2(2b/r) bits
With the 8 bit fingerprint size in this repository, you can expect r ~= 0.03
.
Other implementations use 16 bit, which correspond to a false positive rate of r ~= 0.0001
.
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
}