BloomFilter(s) in Ruby / Without Original Native Support

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

Resources

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

Redis-backed setbit/getbit 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

Memory footprint

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

Redis-backed counting bloom filter with TTL's

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

Credits

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