https://www.cs.cmu.edu/~dga/papers/cuckoo-conext2014.pdf
In many networking systems, Bloom filters are used for highspeed set membership tests. They permit a small fraction of false positive answers with very good space efficiency. However, they do not permit deletion of items from the set, and previous attempts to extend “standard” Bloom filters to support deletion all degrade either space or performance. We propose a new data structure called the cuckoo filter that can replace Bloom filters for approximate set membership tests. Cuckoo filters support adding and removing items dynamically while achieving even higher performance than Bloom filters. For applications that store many items and target moderately low false positive rates, cuckoo filters have lower space overhead than space-optimized Bloom filters. Our experimental results also show that cuckoo filters outperform previous data structures that extend Bloom filters to support deletions substantially in both time and space.
npm install cuckoo-filter
const CuckooFilter = require('cuckoo-filter').CuckooFilter
const ScalableFilter = require('cuckoo-filter').ScalableCuckooFilter
let cuckoo= new CuckooFilter(200, 4, 2) // (Size, Bucket Size, Finger Print Size)
let cuckoo2 = new ScalableCuckooFilter(2000, 4, 2, 2) // (Size, Bucket Size, Finger Print Size, Exponential Scale)
console.log(cuckoo.add('Your Momma'))//(buffer|string|number) returns true if successful
console.log(cuckoo.contains('Your Momma'))// true: She's definately in there
console.log(cuckoo.count) // 1
console.log(cuckoo.remove('Your daddy'))//false He's not home
console.log(cuckoo.reliable) // true less than 95% full
let json = cuckoo.toJSON() // serialize to json object
let cbor = cuckoo.toCBOR() // serialize to cbor
Size your buckets and fingerprints to avoid collisions. Scalable cuckoo filters scale exponentially to hold your data.