Copyright	Bryan O'Sullivan
License	BSD3
Maintainer	Bryan O'Sullivan <bos@serpentine.com>
Stability	unstable
Portability	portable
Safe Haskell	None
Language	Haskell98

Data.BloomFilter.Easy

Contents

Easy creation and querying
- Example: a spell checker
Useful defaults for creation

Description

An easy-to-use Bloom filter interface.

Synopsis

Easy creation and querying

data Bloom a Source

An immutable Bloom filter, suitable for querying from pure code.

Instances

Show (Bloom a)
NFData (Bloom a)

easyList Source

Arguments

:: Hashable a
=> Double	desired false positive rate (0 < e < 1)
-> [a]	values to populate with
-> Bloom a

Create a Bloom filter with the given false positive rate and members. The hash functions used are computed by the cheapHashes function from the Hash module.

elem :: a -> Bloom a -> Bool Source

Query an immutable Bloom filter for membership. If the value is present, return True. If the value is not present, there is still some possibility that True will be returned.

notElem :: a -> Bloom a -> Bool Source

Query an immutable Bloom filter for non-membership. If the value is present, return False. If the value is not present, there is still some possibility that True will be returned.

length :: Bloom a -> Int Source

Return the size of an immutable Bloom filter, in bits.

Example: a spell checker

This example reads a dictionary file containing one word per line, constructs a Bloom filter with a 1% false positive rate, and spellchecks its standard input. Like the Unix spell command, it prints each word that it does not recognize.

import Data.BloomFilter.Easy (easyList, elemB)

main = do
  filt <- (easyList 0.01 . words) `fmap` readFile "usrsharedictwords"
  let check word | elemB word filt = ""
                 | otherwise         = word ++ "\n"
  interact (concat . map check . lines)

Useful defaults for creation

safeSuggestSizing Source

Arguments

:: Int	expected maximum capacity
-> Double	desired false positive rate (0 < e < 1)
-> Either String (Int, Int)

Suggest a good combination of filter size and number of hash functions for a Bloom filter, based on its expected maximum capacity and a desired false positive rate.

The false positive rate is the rate at which queries against the filter should return True when an element is not actually present. It should be a fraction between 0 and 1, so a 1% false positive rate is represented by 0.01.

suggestSizing Source

Arguments

:: Int	expected maximum capacity
-> Double	desired false positive rate (0 < e < 1)
-> (Int, Int)

Behaves as safeSuggestSizing, but calls error if given invalid or out-of-range inputs.