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/

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.

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%

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

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

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

Tatsuya Mori <[email protected]> (Original C implementation: vald.x0.com/sb/)