Struct BloomFilter 

pub struct BloomFilter<T> { /* private fields */ }

BloomFilter

An implementation of a bloom filter

Implementations

impl<T: BloomHasher> BloomFilter<T>

pub fn new(hasher: T, k: u32, array_size: u64) -> Self

Create a new BloomFilter given a hasher, the number of hash functions to use, and the size of the underlying bit array.

Typically, this function should not be called directly unless, the optimal number of hash functions and optimal array size are already known.

pub fn optimal(hasher: T, max_elements: u64, error_rate: f64) -> Self

Create a BloomFilter by computing its optimal parameters.

This function computes the optimal array size using

-(n * ln(p)) / ln(2) ^ 2

and computes the optimal number of hash functions using

m / n * ln(2)

pub fn insert(&mut self, bytes: &[u8])

Insert a slice of bytes into the BloomFilter.

pub fn insert_all<B: AsRef<[u8]>>(&mut self, slice: &[B])

Insert a slice of slices of bytes into the BloomFilter.

pub fn contains<B: AsRef<[u8]>>(&self, bytes: B) -> bool

Check whether a slice of bytes exists in the BloomFilter.

This is a probabilistic function that may return a false positive but will never return a false negative.

Examples

extern crate bloom_filter_rs as bloom_filter;

use std::vec::Vec;
use bloom_filter::{BloomFilter, Murmur3};

let words = vec!["Hello", "I", "am", "some", "words"];

let mut bloom_filter = BloomFilter::optimal(Murmur3, words.len() as u64, 0.01);

bloom_filter.insert_all(&words);

for word in words.iter() {
    assert!(bloom_filter.contains(&word));
}

pub fn false_positive_rate(&self) -> f64

Calculate the expected false positive rate given the current state of the BloomFilter.