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0.1.3 • Public • Published


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This is a fork of Callidon/bloom-filters using lemire/FastBitSet.js as a data structure. Original project uses plain JS arrays filled with numbers which could be quite heavy on memory.

JavaScript/TypeScript implementation of probabilistic data structures: Bloom Filter (and its derived), HyperLogLog, Count-Min Sketch, Top-K and MinHash. This package rely on non-cryptographic hash functions.

📕Online documentation

Keywords: bloom filter, cuckoo filter, KyperLogLog, MinHash, Top-K, probabilistic data-structures.

Table of contents


npm install bitset-bloom-filters --save

Supported platforms

Data structures

Classic Bloom Filter

A Bloom filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not.

Reference: Bloom, B. H. (1970). Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13(7), 422-426. (Full text article)


  • add(element: string) -> void: add an element into the filter.
  • has(element: string) -> boolean: Test an element for membership, returning False if the element is definitively not in the filter and True is the element might be in the filter.
  • equals(other: BloomFilter) -> boolean: Test if two filters are equals.
  • rate() -> number: compute the filter's false positive rate (or error rate).
const { BloomFilter } = require('bloom-filters')
// create a Bloom Filter with a size of 10 and 4 hash functions
let filter = new BloomFilter(10, 4)
// insert data

// lookup for some data
console.log(filter.has('bob')) // output: true
console.log(filter.has('daniel')) // output: false

// print the error rate

// alternatively, create a bloom filter optimal for a number of items and a desired error rate
const items = ['alice', 'bob']
const errorRate = 0.04 // 4 % error rate
filter = BloomFilter.create(items.length, errorRate)

// or create a bloom filter optimal for a collections of items and a desired error rate
filter = BloomFilter.from(items, errorRate)

Every hash function is seeded

By default every hash function is seeded with an internal seed which is equal to 0x1234567890. If you want to change it:

const { BloomFilter } = require('bloom-filter')
const bl = new BloomFilter(...)
console.log(bl.seed) // 78187493520
bl.seed = 0xABCD
console.log(bl.seed) // 43981


See documentation online or generate it in directory doc/ with: npm run doc


Running with Mocha + Chai

# run tests
npm test


  • Classic Bloom Filter: Bloom, B. H. (1970). Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13(7), 422-426.


Version Release date Major changes
v0.1.0 08/05/2021 Classic only implementation with FastBitSet


MIT License

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