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

Tatsuya Mori <valdzone@gmail.com> (Original C implementation: vald.x0.com/sb/)