THIS IS A FORK OF https://github.com/igrigorik/bloomfilter-rb

• Redis-backed getbit/setbit non-counting bloom filter
• Redis-backed set-based counting (+TTL) bloom filter

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, check the wikipedia article. 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.

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

• Determining parameters: Scalable Datasets: Bloom Filters in Ruby
• Applications & reasons behind bloom filter: Flow analysis: Time based bloom filter

Uses getbit/setbit on Redis strings - efficient, fast, can be shared by multiple/concurrent processes.

bf = BloomFilter::Redis.new

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

bf.delete('test')
bf.include?('test')     # => false

• 1.0% error rate for 1M items, 10 bits/item: 2.5 mb
• 1.0% error rate for 150M items, 10 bits per item: 358.52 mb
• 0.1% error rate for 150M items, 15 bits per item: 537.33 mb

Uses regular Redis get/set counters to implement a counting filter with optional TTL expiry. Because each "bit" requires its own key in Redis, you do incur a much larger memory overhead.

bf = BloomFilter::CountingRedis.new(:ttl => 2)

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

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

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

MIT License - Copyright (c) 2011 Ilya Grigorik