Distributed bloom filters for very large sets using redis for persistence and consensus.

Goals:

• Plugable as a module with a minimal, clean interface
• Transparent for the client using the standard semantics for bloom filters
• Agents can join and leave the distributed bloom filter anytime without effort
• Minimal data transference over the network

Usage:

Import the required libraries

import (
	"gopkg.in/redis.v3"
	"github.com/kpacha/bloomis"
)

Create your redis client

	client := redis.NewClient(&redis.Options{
		Addr:     "localhost:6379",
		Password: "",
		DB:       0,
	})

Calculate the best m & k values for your use case with one of the currently available online like the Thomas Hurst's implementation and create a single bloom filter (this example uses n = 100,000,000 & p = 1e-9)

	m := 4313276270
	k := 30
	bf := bloomis.NewSingleFilter(m, k, client)

Start adding and testing values

	fmt.Println(bf.AddToDefault([]byte("foo")))
	fmt.Println(bf.TestToDefault([]byte("foo")))
	fmt.Println(bf.TestToDefault([]byte("baz")))
	// Output:
	// <nil>
	// true <nil>
	// false <nil>

Check the bloom_test.go for more examples

Max capacity:

The max capacity of a single bloomis filter is defined by the max size of a string value in redis (512MB) - m - and the probability of false positives p selected. As seen before, it allows you to store more than 99M elements with p = 1e-9. It could be extended by sharding several bloom filters, so go ahead and use the bloomis filter with any consistent hashing lib like go-jump or with a naive hashing function like this one:

func NaiveHash(key string) string {
	return key[:4]
}

Customization:

Since the BloomFilter is just a proxy with a list of filters and a simple config persistence, you can create your custom filters with your own Hasher implementations and/or personalize how to store/load the filter metadata, etc.