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https://github.com/igrigorik/bloomfilter-rb
BloomFilter(s) in Ruby: Native counting filter + Redis counting/non-counting filters
https://github.com/igrigorik/bloomfilter-rb
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BloomFilter(s) in Ruby: Native counting filter + Redis counting/non-counting filters
- Host: GitHub
- URL: https://github.com/igrigorik/bloomfilter-rb
- Owner: igrigorik
- Created: 2008-12-27T18:07:07.000Z (about 16 years ago)
- Default Branch: master
- Last Pushed: 2024-03-26T22:22:14.000Z (10 months ago)
- Last Synced: 2025-01-06T07:02:20.545Z (20 days ago)
- Language: C
- Homepage: http://www.igvita.com/2008/12/27/scalable-datasets-bloom-filters-in-ruby/
- Size: 99.6 KB
- Stars: 473
- Watchers: 16
- Forks: 60
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-ruby - bloomfilter-rb - BloomFilter(s) in Ruby: Native counting filter + Redis counting/non-counting filters. (Scientific)
README
# BloomFilter(s) in Ruby
- Native (MRI/C) counting bloom filter
- 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](http://en.wikipedia.org/wiki/Bloom_filter). 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](http://www.igvita.com/2008/12/27/scalable-datasets-bloom-filters-in-ruby/)
- Applications & reasons behind bloom filter: [Flow analysis: Time based bloom filter](http://www.igvita.com/2010/01/06/flow-analysis-time-based-bloom-filters/)
***
## MRI/C API Example
MRI/C implementation which creates an in-memory filter which can be saved and reloaded from disk.
```ruby
require 'bloomfilter-rb'
bf = BloomFilter::Native.new(:size => 100, :hashes => 2, :seed => 1, :bucket => 3, :raise => false)
bf.insert("test")
bf.include?("test") # => true
bf.include?("blah") # => false
bf.delete("test")
bf.include?("test") # => false
# Hash with a bloom filter!
bf["test2"] = "bar"
bf["test2"] # => true
bf["test3"] # => false
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 setbit/getbit bloom filter
Uses [getbit](http://redis.io/commands/getbit)/[setbit](http://redis.io/commands/setbit) on Redis strings - efficient, fast, can be shared by multiple/concurrent processes.
```ruby
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 TTLs
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.
```ruby
bf = BloomFilter::CountingRedis.new(:ttl => 2)
bf.insert('test')
bf.include?('test') # => true
sleep(2)
bf.include?('test') # => false
```
## Credits
Tatsuya Mori (Original C implementation: http://vald.x0.com/sb/)
## License
MIT License - Copyright (c) 2011 Ilya Grigorik