@ably/bloomit

bloomit

bloomit is a Space efficient bloom filter based on the bloom-filters npm package.

The main motivation for this package was to reduce the memory usage of the bloom filter by using a bitmap instead of an array of javascript numbers. This should result in a theoretical memory reduction by a factor of 64.

I have also edited the export to use a Uint8Array which encodes all needed values and can be used to send it over the web efficiently.

Methods

• 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).
• export() -> Uint8Array: export the filter as an Uint8Array
• inport(filterUint8Array: Uint8Array) -> BloomFilter: Create a filter from a exporterd Uint8Array

const { BloomFilter } = require('bloomit');
// create a Bloom Filter with a size of 10 and 4 hash functions
let filter = new BloomFilter(10, 4);
// insert data
filter.add('paul');
filter.add('kolja');
filter.add('carl');

// lookup for some data
console.log(filter.has('paul')); // output: true
console.log(filter.has('xiaomei')); // output: false

// print the error rate
console.log(filter.rate());

// alternatively, create a bloom filter optimal for a number of items and a desired error rate
const items = ['paul', 'kolja', 'carl'];
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);

// Export the filter
const exportedFilter = filter.export();

// Import the filter
filter = BloomFilter.import(exportedFilter);