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count-min-sketch

v0.1.1

Published

Count-Min Sketch Data Structure

Downloads

249

Readme

count-min-sketch

An implementation of Coromode and Muthukrishnan's Count-Min sketch data structure for JavaScript. The count-min sketch is basically a high powered generalization of the bloom filter. While a bloom filter gives an efficient way to approximate membership of a set, a count-min sketch can give approximate data about the relative frequency of items in the set.

For more information see the following references:

Example

//Import library
var createCountMinSketch = require("count-min-sketch")

//Create data structure
var sketch = createCountMinSketch()

//Increment counters
sketch.update("foo", 1)
sketch.update(1515, 104)

//Query results
console.log(sketch.query(1515))  //Prints 104
console.log(sketch.query("foo")) //Prints 1

Install

npm install count-min-sketch

API

module.exports is a constructor for the data structure, and you import it like so:

var createCountMinSketch = require("count-min-sketch")

var sketch = createCountMinSketch(epsilon, probError[, hashFunc])

Creates a count-min sketch data structure.

  • epsilon is the accuracy of the data structure (ie the size of bins that we are computing frequencies of)

  • probError is the probability of incorrectly computing a value

  • hashFunc(key, hashes) is a hash function for the data structure. (optional) the parameters to this function are as follows:

    • key is the item that is being hashed
    • hashes is an array of k hashes which are required to be pairwise independent.

Returns A count-min sketch data structure

sketch.update(key, v)

Adds v to key

  • key is the item in the table to increment.
  • v is the amount to add to it

sketch.query(key)

Returns the frequency of the item key

  • key is the item whose frequency we are counting

Returns An estimate of the frequency of key

Credits

(c) 2013 Mikola Lysenko. MIT License