b100m-filter

A very fast and accurate Bloom Filter implementation in Rust.

Usage

# Cargo.toml

[dependencies]

b100m_filter = "0.1.2"

use b100m_filter::BloomFilter;

let num_blocks = 4; // each block is 64 bytes, 512 bits
let values = vec!["42", "qwerty", "bloom"];

let filter = BloomFilter::builder(num_blocks).items(values.iter());
assert!(filter.contains("42"));
assert!(filter.contains("bloom"));

b100m-filter is blazingly fast because it uses L1 cache friendly blocks and efficiently derives many index bits from only two hashes per value. Compared to traditional implementations, this bloom filter is 2.5-6.5 faster for a small number of contained items, and hundreds of times faster for more items. In all cases b100m_filter maintains competitive false positive rates.

Runtime Performance

Runtime comparison to a popular traditonal bloom filter crate:

Sampled False Postive Rate: 0.000000%
BloomFilter (1000 items, 65536 bytes): get existing 1000
                        time:   [140.84 µs 141.30 µs 141.85 µs]
+                       change: [-85.090% -85.025% -84.936%] (p = 0.00 < 0.05)

Sampled False Postive Rate: 0.000000%
BloomFilter (1000 items, 2097152 bytes): get existing 1000
                        time:   [141.30 µs 141.46 µs 141.65 µs]
+                       change: [-99.686% -99.686% -99.686%] (p = 0.00 < 0.05)

Sampled False Postive Rate: 0.000000%
BloomFilter (1000 items, 65536 bytes): get non-existing 1000
                        time:   [27.722 µs 27.754 µs 27.787 µs]
+                       change: [-62.268% -62.027% -61.757%] (p = 0.00 < 0.05)

Sampled False Postive Rate: 0.000000%
BloomFilter (1000 items, 2097152 bytes): get non-existing 1000
                        time:   [27.209 µs 27.223 µs 27.241 µs]
+                       change: [-99.002% -99.000% -98.996%] (p = 0.00 < 0.05)

As the memory size and set size increase, bloom filters need to perform more hashes to keep false positive rates low. Bloom filter speed is directly proportional to number of hashes. b100m-filter's optimal number of hashes is bounded and keeps near zero rates even for large sizes:

False Positive Performance

b100m-filter does not sacrifice accuracy. Below is a comparison false positive rate with a traditional bloom filter:

License

Licensed under either of

Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution