README.md

🌺 Bloom

Nothing new here. Just another bloom filter implementation for:

• Classic bloom
• Counting bloom

Usage

Classic bloom with seahash

An optimal basic filter which makes makes use of SeaHasher as the underlying hash function. If you'll like to use a customer hashing function, check [here].

use bloom_filter::seahash_bloom_filter;

let bloom = seahash_bloom_filter(100_000, 0.01);
bloom.insert("John");
bloom.insert("Doe");

assert!(bloom.check("John"));
assert!(bloom.check("Doe"));
assert!(!bloom.check("Anderson"));

Counting Bloom with seahash

An optimal counting bloom filter which makes use of SeaHasher as the underlying hashing function.

use bloom_filter::seahash_counting_bloom_filter;

let bloom = seahash_counting_bloom_filter(100_000, 0.01);
bloom.insert("John");
bloom.insert("John"); // inserted second time
assert!(bloom.check("John"));

bloom.delete("John");
assert!(bloom.check("John"));

bloom.delete("John");
assert!(!bloom.check("John"));

Bloom with custom paramaters and hash functions.

There might be cases where you wouldn't want to rely on the in-build functions (e.g. seahash_*_bloom_filter()), but instead rely on a different m, k, or the hash function.

If you'll like to calculate the optimal values of bit mask size, m, or number of hash functions, k, you can rely on the utils::optimal_*() utils provided out of the box.

use bloom_filter::BloomFilter;
use bloom_filter::utils::*;
use std::hash::Hasher;

/// Build a total of k hashers.
fn build_hashers(k: usize) -> Vec<impl Hasher> {
    (0..k).iter().map(|_| { /* Build custom hashers here */ }).collect()
}

fn main() {
    let (m, false_positive_rate) = (100_000, 0.01);
    let bit_size = optimal_bit_size(m, false_positive_rate);
    let hash_count = optimal_hash_count(m, false_positive_rate); // also referred to as, k.

    let hashers = build_hashers(hash_count);
    let bloom = BloomFilter::new(bit_size, hashers);
    bloom.insert("hello");
}

Counting bloom into classic bloom

CountingBloomFilter implements the From<T> trait, and can easily be converted into the default BloomFilter without losing the underlying bit mask data. The only information that gets truncated is the counter which keeps track of inserted items (only needed within the confines of a counting bloom filter).

use bloom_filter::seahash_counting_bloom_filter;

let counting_bloom_filter = seahash_counting_bloom_filter(100_000, 0.01);

counting_bloom_filter.insert("John");
assert!(counting_bloom_filter.check("John"));

let bloom_filter = BloomFilter::from(counting_bloom_filter);
assert!(bloom_filter.check("John"));