igrigorik / bloomfilter

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Counting Bloom Filter implemented in Ruby: C version, Redis version (with TTL's) — Read more

http://www.igvita.com/2008/12/27/scalable-datasets-bloom-filters-in-ruby/

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marshall/unmarshall bloomfilter logic (C version)

igrigorik (author)

Wed Aug 25 20:16:23 -0700 2010

commit 625eace1c64fdf2ccb1d
tree be6630250b351fdd233d
parent 42810663bcf5e181eb41

bloomfilter /

name	age	history message
.gitignore	Mon Dec 29 09:54:53 -0800 2008	adding a test task and a test [tenderlove]
README.rdoc	Wed Jan 06 09:55:55 -0800 2010	correct link in the readme [igrigorik]
Rakefile	Sat May 22 00:43:27 -0700 2010	using rake-compiler is much easier [joshbuddy]
VERSION	Wed Aug 25 20:16:23 -0700 2010	marshall/unmarshall bloomfilter logic (C version) [igrigorik]
bloomfilter.gemspec	Wed Aug 25 20:16:23 -0700 2010	marshall/unmarshall bloomfilter logic (C version) [igrigorik]
examples/	Tue Jan 05 21:40:11 -0800 2010	update readme with redis examples [igrigorik]
ext/	Wed Aug 25 20:16:23 -0700 2010	marshall/unmarshall bloomfilter logic (C version) [igrigorik]
lib/	Wed Aug 25 20:16:23 -0700 2010	marshall/unmarshall bloomfilter logic (C version) [igrigorik]
spec/	Wed Aug 25 20:16:23 -0700 2010	marshall/unmarshall bloomfilter logic (C version) [igrigorik]

README.rdoc

BloomFilter

Counting Bloom Filter implemented in Ruby.

Bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. False positives are possible, but false negatives are not. For more detail: en.wikipedia.org/wiki/Bloom_filter

Performance of the Bloom filter depends on a number of variables:

size of the bit array
size of the counter bucket
number of hash functions

To figure out the values for these parameters, refer to:

www.igvita.com/2008/12/27/scalable-datasets-bloom-filters-in-ruby/

To learn about applications and reasons for the time based bloom filters, refer to:

www.igvita.com/2010/01/06/flow-analysis-time-based-bloom-filters/

Implementation

Instead of using k different hash functions, this implementation seeds the CRC32 hash with k different initial values (0, 1, …, k-1). This may or may not give you a good distribution, it all depends on the data.

Example

  require 'bloomfilter'

  bf = BloomFilter.new(:size => 100, :hashes => 2, :seed => 1, :bucket => 3, :raise => false)
  bf.insert("test")
  bf.include?("test")
  => true
  bf.include?("test2")
  => false
  bf.delete("test")
  bf.include?("test")
  => false

  # Hash with a bloom filter!
  bf["test2"] = "bar"
  bf["test2"]
  => "bar"
  bf["test3"]
  => nil

  bf.stats
  Number of filter bits (m): 10
  Number of filter elements (n): 2
  Number of filter hashes (k) : 2
  Predicted false positive rate = 10.87%

Redis-backed counting Bloom Filter with TTL’s

  bf = BloomFilter.new(:type => :redis, :ttl => 2, :server => {:host => 'localhost'})

  bf.insert('test')
  bf.include?('test')
  => true

  sleep(2)
  bf.include?('test')
  => false

Credits