lib.rs 6.82 KB
Newer Older
Tom Almeida's avatar
Tom Almeida committed
1 2 3 4 5 6 7 8
//! A concurrent implementation of Bloom filters.
//!
//! Bloom filters is a simple data structure, which is used in many different situations. It can
//! neatly solve certain problems heaurustically without need for extreme memory usage.
//!
//! This implementation is fairly standard, except that it uses atomic integers to work
//! concurrently.

Luca Bruno's avatar
Luca Bruno committed
9
#![deny(missing_debug_implementations)]
Tom Almeida's avatar
Tom Almeida committed
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

use std::cmp;
use std::sync::atomic::{self, AtomicU64};

/// The atomic ordering used throughout the crate.
const ORDERING: atomic::Ordering = atomic::Ordering::Relaxed;

/// Hash an integer.
///
/// This is a pseudorandom permutation of `u64` with high statistical quality. It can thus be used
/// as a hash function.
fn hash(mut x: u64) -> u64 {
    // The following is copied from SeaHash.

    x = x.wrapping_mul(0x6eed0e9da4d94a4f);
    let a = x >> 32;
    let b = x >> 60;
    x ^= a >> b;
    x = x.wrapping_mul(0x6eed0e9da4d94a4f);

    // We XOR with some constant to make it zero-sensitive.
    x ^ 0x11c92f7574d3e84f
}

/// A concurrent Bloom filter.
///
/// Bloom filters are a probabilistic data structure, which allows you to insert elements, and
/// later test if they were inserted. The filter will either know it doesn't contain the element,
/// or that it might. It will never be "sure", hence the name "filter".
///
/// It works by having an array of bits. Every element is hashed into a sequence of these bits. The
/// bits of the inserted elements are set to 1. When testing for membership, we simply AND the
/// bits.
Luca Bruno's avatar
Luca Bruno committed
43
#[derive(Debug)]
Tom Almeida's avatar
Tom Almeida committed
44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
pub struct Filter {
    /// The bit array.
    ///
    /// We use `u64` to improve performance of `Filter::clear()`.
    bits: Vec<AtomicU64>,
    /// The number of hash functions.
    hashers: usize,
}

impl Filter {
    /// Get the chunk of a particular hash.
    #[inline]
    fn get(&self, hash: u64) -> &AtomicU64 {
        &self.bits[(hash as usize / 64) % self.bits.len()]
    }

    /// Create a new Bloom filter with the optimal number of hash functions.
    ///
    /// This creates a Bloom filter with `bytes` bytes of internal data, and optimal number (for
    /// `expected_elements` number of elements) of hash functions.
    pub fn new(bytes: usize, expected_elements: usize) -> Filter {
        // The number of hashers are calculated by multiplying the bits per element by ln(2), which
        // we approximate through multiplying by an integer, then shifting. To make things more
        // precise, we add 0x8000 to round the shift.
        Filter::with_size_and_hashers(bytes, (bytes / expected_elements * 45426 + 0x8000) >> 16)
    }

    /// Create a new Bloom filter with some number of bytes and hashers.
    ///
    /// This creates a Bloom filter with at least `bytes` bytes of internal data and `hashers`
    /// number of hash functions.
    ///
    /// If `hashers` is 0, it will be rounded to 1.
    pub fn with_size_and_hashers(bytes: usize, hashers: usize) -> Filter {
        // Convert `bytes` to number of `u64`s, and ceil to avoid case where the output is 0.
        let len = (bytes + 7) / 8;
        // Initialize a vector with zeros.
        let mut vec = Vec::with_capacity(len);
        for _ in 0..len {
            vec.push(AtomicU64::new(0));
        }

        Filter {
            bits: vec,
            // Set hashers to 1, if it is 0, as there must be at least one hash function.
            hashers: cmp::max(hashers, 1),
        }
    }

    /// Clear the Bloom filter.
    ///
    /// This removes every element from the Bloom filter.
    ///
    /// Note that it will not do so atomically, and it can remove elements inserted simulatenously
    /// to this function being called.
    pub fn clear(&self) {
        for i in &self.bits {
            // Clear the bits of this chunk.
            i.store(0, ORDERING);
        }
    }

    /// Insert an element into the Bloom filter.
    pub fn insert(&self, x: u64) {
        // Start at `x`.
        let mut h = x;
        // Run over the hashers.
        for _ in 0..self.hashers {
            // We use the hash function to generate a pseudorandom sequence, defining the different
            // hashes.
            h = hash(h);
            // Create a mask and OR the chunk chosen by `hash`.
            self.get(h).fetch_or(1 << (h % 8), ORDERING);
        }
    }

    /// Check if the Bloom filter potentially contains an element.
    ///
    /// This returns `true` if we're not sure if the filter contains `x` or not, and `false` if we
    /// know that the filter does not contain `x`.
    pub fn maybe_contains(&self, x: u64) -> bool {
        // Start at `x`.
        let mut h = x;

        // Go over the hashers.
        for _ in 0..self.hashers {
            // Again, the hashes are defined by a cuckoo sequence of repeatedly hashing.
            h = hash(h);
            // Short-circuit if the bit is not set.
            if self.get(h).load(ORDERING) & 1 << (h % 8) == 0 {
                // Since the bit of this hash value was not set, it is impossible that the filter
                // contains `x`, so we return `false`.
                return false;
            }
        }

        // Every bit was set, so the element might be in the filter.
        true
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    use std::sync::Arc;
    use std::thread;

    #[test]
    fn insert() {
        let filter = Filter::new(400, 4);
        filter.insert(3);
        filter.insert(5);
        filter.insert(7);
        filter.insert(13);

        assert!(!filter.maybe_contains(0));
        assert!(!filter.maybe_contains(1));
        assert!(!filter.maybe_contains(2));
        assert!(filter.maybe_contains(3));
        assert!(filter.maybe_contains(5));
        assert!(filter.maybe_contains(7));
        assert!(filter.maybe_contains(13));

        for i in 14..60 {
            assert!(!filter.maybe_contains(!i));
        }
    }

    #[test]
    fn clear() {
        let filter = Filter::new(400, 4);
        filter.insert(3);
        filter.insert(5);
        filter.insert(7);
        filter.insert(13);

        filter.clear();

        assert!(!filter.maybe_contains(0));
        assert!(!filter.maybe_contains(1));
        assert!(!filter.maybe_contains(2));
        assert!(!filter.maybe_contains(3));
        assert!(!filter.maybe_contains(5));
        assert!(!filter.maybe_contains(7));
        assert!(!filter.maybe_contains(13));
    }

    #[test]
    fn spam() {
        let filter = Arc::new(Filter::new(2000, 100));
        let mut joins = Vec::new();

        for _ in 0..16 {
            let filter = filter.clone();
            joins.push(thread::spawn(move || for i in 0..100 {
                filter.insert(i)
            }));
        }

        for i in joins {
            i.join().unwrap();
        }

        for i in 0..100 {
            assert!(filter.maybe_contains(i));
        }
        for i in 100..200 {
            assert!(!filter.maybe_contains(i));
        }
    }
}