bloom-filters/src/bloom/counting-bloom-filter.ts

JS implementation of probabilistic data structures: Bloom Filter (and its derived), HyperLogLog, Count-Min Sketch, Top-K and MinHash

/* file : counting-bloom-filter.ts
MIT License

Copyright (c) 2017 Thomas Minier & Arnaud Grall

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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*/

import BaseFilter from '../base-filter'
import WritableFilter from '../interfaces/writable-filter'
import {AutoExportable, Field, Parameter} from '../exportable'
import {optimalFilterSize, optimalHashes} from '../formulas'
import {HashableInput, allocateArray} from '../utils'

/**
 * A Counting Bloom filter works in a similar manner as a regular Bloom filter; however, it is able to keep track of insertions and deletions. In a counting Bloom filter, each entry in the Bloom filter is a small counter associated with a basic Bloom filter bit.
 *
 * Reference: F. Bonomi, M. Mitzenmacher, R. Panigrahy, S. Singh, and G. Varghese, “An Improved Construction for Counting Bloom Filters,” in 14th Annual European Symposium on Algorithms, LNCS 4168, 2006, pp.
684–695.
 * @author Thomas Minier & Arnaud Grall
 */
@AutoExportable('CountingBloomFilter', ['_seed'])
export default class CountingBloomFilter
  extends BaseFilter
  implements WritableFilter<HashableInput>
{
  @Field()
  public _size: number
  @Field()
  public _nbHashes: number
  @Field()
  public _filter: Array<Array<number>>
  @Field()
  public _length: number
  /**
   * Constructor
   * @param size - The size of the filter
   * @param nbHashes - The number of hash functions
   */
  constructor(
    @Parameter('_size') size: number,
    @Parameter('_nbHashes') nbHashes: number
  ) {
    super()
    if (nbHashes < 1) {
      throw new Error(
        `A CountingBloomFilter must used at least one hash function, but you tried to use ${nbHashes} functions. Consider increasing it.`
      )
    }
    this._size = size // fm.optimalFilterSize(capacity, errorRate)
    this._nbHashes = nbHashes // fm.optimalHashes(this._size, capacity)
    // the filter contains tuples [bit, counter]
    this._filter = allocateArray(this._size, () => [0, 0])
    this._length = 0
  }

  /**
   * Allocate a CountingBloomFilter with a target maximum capacity and error rate
   * @param  capacity - The maximum capacity of the filter
   * @param  errorRate - The error rate of the filter
   * @return A new {@link CountingBloomFilter}
   */
  public static create(
    capacity: number,
    errorRate: number
  ): CountingBloomFilter {
    const s = optimalFilterSize(capacity, errorRate)
    return new CountingBloomFilter(s, optimalHashes(s, capacity))
  }

  /**
   * Build a new Bloom Filter from an iterable with a fixed error rate
   * @param items - Iterable used to populate the filter
   * @param errorRate - The error rate of the filter
   * @return A new Bloom Filter filled with the iterable's elements
   * @example
   * ```js
   * // create a filter with a false positive rate of 0.1
   * const filter = CountingBloomFilter.from(['alice', 'bob', 'carl'], 0.1);
   * ```
   */
  public static from(
    items: Iterable<HashableInput>,
    errorRate: number
  ): CountingBloomFilter {
    const array = Array.from(items)
    const filter = CountingBloomFilter.create(array.length, errorRate)
    array.forEach(element => filter.add(element))
    return filter
  }

  /**
   * Get the optimal size of the filter
   */
  public get size(): number {
    return this._size
  }

  /**
   * Get the number of elements currently in the filter
   */
  public get length(): number {
    return this._length
  }

  /**
   * Add an element to the filter
   * @param element - The element to add
   * @example
   * ```js
   * const filter = new CountingBloomFilter(15, 0.1);
   * filter.add('foo');
   * ```
   */
  public add(element: HashableInput): void {
    const indexes = this._hashing.getIndexes(
      element,
      this._size,
      this._nbHashes,
      this.seed
    )
    for (let i = 0; i < indexes.length; i++) {
      // increment counter
      this._filter[indexes[i]][1] += 1
      // set bit if necessary
      if (this._filter[indexes[i]][1] > 0) {
        this._filter[indexes[i]][0] = 1
      }
    }
    this._length++
  }

  /**
   * Remove an element from the filter
   * @param element - The element to delete
   * @example
   * ```js
   * const filter = new CountingBloomFilter(15, 0.1);
   * filter.remove('foo');
   * ```
   */
  public remove(element: HashableInput): boolean {
    const indexes = this._hashing.getIndexes(
      element,
      this._size,
      this._nbHashes,
      this.seed
    )
    const success = true
    for (let i = 0; i < indexes.length; i++) {
      // decrement counter
      this._filter[indexes[i]][1] -= 1
      // set bit if necessary
      if (this._filter[indexes[i]][1] <= 0) {
        this._filter[indexes[i]][0] = 0
      }
    }
    this._length--
    return success
  }

  /**
   * Test an element for membership
   * @param element - The element to look for in the filter
   * @return False if the element is definitively not in the filter, True is the element might be in the filter
   * @example
   * ```js
   * const filter = new CountingBloomFilter(15, 0.1);
   * filter.add('foo');
   * console.log(filter.has('foo')); // output: true
   * console.log(filter.has('bar')); // output: false
   * ```
   */
  public has(element: HashableInput): boolean {
    const indexes = this._hashing.getIndexes(
      element,
      this._size,
      this._nbHashes,
      this.seed
    )
    for (let i = 0; i < indexes.length; i++) {
      if (!this._filter[indexes[i]][0]) {
        return false
      }
    }
    return true
  }

  /**
   * Get the current false positive rate (or error rate) of the filter
   * @return The current false positive rate of the filter
   * @example
   * ```js
   * const filter = new BloomFilter(15, 0.1);
   * console.log(filter.rate()); // output: something around 0.1
   * ```
   */
  public rate(): number {
    return Math.pow(
      1 - Math.exp((-this._nbHashes * this._length) / this._size),
      this._nbHashes
    )
  }

  /**
   * Check if another Counting Bloom Filter is equal to this one
   * @param  filter - The filter to compare to this one
   * @return True if they are equal, false otherwise
   */
  public equals(other: CountingBloomFilter): boolean {
    if (
      this._size !== other._size ||
      this._nbHashes !== other._nbHashes ||
      this._length !== other._length
    ) {
      return false
    }
    return this._filter.every(
      (value, index) =>
        other._filter[index][0] === value[0] &&
        other._filter[index][1] === value[1]
    )
  }
}