1
2
3
4
5
6
7
8
9
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
43
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
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
// (C)opyleft 2013-2021 Frank Denis
// Licensed under the ICS license (https://opensource.org/licenses/ISC)

//! Bloom filter for Rust
//!
//! This is a simple but fast Bloom filter implementation, that requires only
//! 2 hash functions, generated with SipHash-1-3 using randomized keys.

#![warn(non_camel_case_types, non_upper_case_globals, unused_qualifications)]
#![allow(clippy::unreadable_literal, clippy::bool_comparison)]

use std::cmp;
use std::convert::TryFrom;
use std::f64;
use std::hash::{Hash, Hasher};
use std::marker::PhantomData;

use bit_vec::BitVec;
#[cfg(feature = "random")]
use getrandom::getrandom;
#[cfg(feature = "serde")]
use siphasher::reexports::serde;
use siphasher::sip::SipHasher13;

pub mod reexports {
    #[cfg(feature = "random")]
    pub use ::getrandom;
    pub use bit_vec;
    pub use siphasher;
    #[cfg(feature = "serde")]
    pub use siphasher::reexports::serde;
}

/// Bloom filter structure
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
#[cfg_attr(feature = "serde", serde(crate = "serde"))]
#[derive(Clone, Debug)]
pub struct Bloom<T: ?Sized> {
    bit_vec: BitVec,
    bitmap_bits: u64,
    k_num: u32,
    sips: [SipHasher13; 2],

    _phantom: PhantomData<T>,
}

impl<T: ?Sized> Bloom<T> {
    /// Create a new bloom filter structure.
    /// bitmap_size is the size in bytes (not bits) that will be allocated in
    /// memory items_count is an estimation of the maximum number of items
    /// to store. seed is a random value used to generate the hash
    /// functions.
    pub fn new_with_seed(bitmap_size: usize, items_count: usize, seed: &[u8; 32]) -> Self {
        assert!(bitmap_size > 0 && items_count > 0);
        let bitmap_bits = u64::try_from(bitmap_size)
            .unwrap()
            .checked_mul(8u64)
            .unwrap();
        let k_num = Self::optimal_k_num(bitmap_bits, items_count);
        let bitmap = BitVec::from_elem(usize::try_from(bitmap_bits).unwrap(), false);
        let mut k1 = [0u8; 16];
        let mut k2 = [0u8; 16];
        k1.copy_from_slice(&seed[0..16]);
        k2.copy_from_slice(&seed[16..32]);
        let sips = [Self::sip_new(&k1), Self::sip_new(&k2)];
        Self {
            bit_vec: bitmap,
            bitmap_bits,
            k_num,
            sips,
            _phantom: PhantomData,
        }
    }

    /// Create a new bloom filter structure.
    /// bitmap_size is the size in bytes (not bits) that will be allocated in
    /// memory items_count is an estimation of the maximum number of items
    /// to store.
    #[cfg(feature = "random")]
    pub fn new(bitmap_size: usize, items_count: usize) -> Self {
        let mut seed = [0u8; 32];
        getrandom(&mut seed).unwrap();
        Self::new_with_seed(bitmap_size, items_count, &seed)
    }

    /// Create a new bloom filter structure.
    /// items_count is an estimation of the maximum number of items to store.
    /// fp_p is the wanted rate of false positives, in ]0.0, 1.0[
    #[cfg(feature = "random")]
    pub fn new_for_fp_rate(items_count: usize, fp_p: f64) -> Self {
        let bitmap_size = Self::compute_bitmap_size(items_count, fp_p);
        Bloom::new(bitmap_size, items_count)
    }

    /// Create a new bloom filter structure.
    /// items_count is an estimation of the maximum number of items to store.
    /// fp_p is the wanted rate of false positives, in ]0.0, 1.0[
    pub fn new_for_fp_rate_with_seed(items_count: usize, fp_p: f64, seed: &[u8; 32]) -> Self {
        let bitmap_size = Self::compute_bitmap_size(items_count, fp_p);
        Bloom::new_with_seed(bitmap_size, items_count, seed)
    }

    /// Create a bloom filter structure from a previous state given as a
    /// `ByteVec` structure. The state is assumed to be retrieved from an
    /// existing bloom filter.
    pub fn from_bit_vec(
        bit_vec: BitVec,
        bitmap_bits: u64,
        k_num: u32,
        sip_keys: [(u64, u64); 2],
    ) -> Self {
        let sips = [
            SipHasher13::new_with_keys(sip_keys[0].0, sip_keys[0].1),
            SipHasher13::new_with_keys(sip_keys[1].0, sip_keys[1].1),
        ];
        Self {
            bit_vec,
            bitmap_bits,
            k_num,
            sips,
            _phantom: PhantomData,
        }
    }

    /// Create a bloom filter structure with an existing state given as a byte
    /// array. The state is assumed to be retrieved from an existing bloom
    /// filter.
    pub fn from_existing(
        bytes: &[u8],
        bitmap_bits: u64,
        k_num: u32,
        sip_keys: [(u64, u64); 2],
    ) -> Self {
        Self::from_bit_vec(BitVec::from_bytes(bytes), bitmap_bits, k_num, sip_keys)
    }

    /// Compute a recommended bitmap size for items_count items
    /// and a fp_p rate of false positives.
    /// fp_p obviously has to be within the ]0.0, 1.0[ range.
    pub fn compute_bitmap_size(items_count: usize, fp_p: f64) -> usize {
        assert!(items_count > 0);
        assert!(fp_p > 0.0 && fp_p < 1.0);
        let log2 = f64::consts::LN_2;
        let log2_2 = log2 * log2;
        ((items_count as f64) * f64::ln(fp_p) / (-8.0 * log2_2)).ceil() as usize
    }

    /// Record the presence of an item.
    pub fn set(&mut self, item: &T)
    where
        T: Hash,
    {
        let mut hashes = [0u64, 0u64];
        for k_i in 0..self.k_num {
            let bit_offset = (self.bloom_hash(&mut hashes, item, k_i) % self.bitmap_bits) as usize;
            self.bit_vec.set(bit_offset, true);
        }
    }

    /// Check if an item is present in the set.
    /// There can be false positives, but no false negatives.
    pub fn check(&self, item: &T) -> bool
    where
        T: Hash,
    {
        let mut hashes = [0u64, 0u64];
        for k_i in 0..self.k_num {
            let bit_offset = (self.bloom_hash(&mut hashes, item, k_i) % self.bitmap_bits) as usize;
            if self.bit_vec.get(bit_offset).unwrap() == false {
                return false;
            }
        }
        true
    }

    /// Record the presence of an item in the set,
    /// and return the previous state of this item.
    pub fn check_and_set(&mut self, item: &T) -> bool
    where
        T: Hash,
    {
        let mut hashes = [0u64, 0u64];
        let mut found = true;
        for k_i in 0..self.k_num {
            let bit_offset = (self.bloom_hash(&mut hashes, item, k_i) % self.bitmap_bits) as usize;
            if self.bit_vec.get(bit_offset).unwrap() == false {
                found = false;
                self.bit_vec.set(bit_offset, true);
            }
        }
        found
    }

    /// Return the bitmap as a vector of bytes
    pub fn bitmap(&self) -> Vec<u8> {
        self.bit_vec.to_bytes()
    }

    /// Return the bitmap as a "BitVec" structure
    pub fn bit_vec(&self) -> &BitVec {
        &self.bit_vec
    }

    /// Return the number of bits in the filter
    pub fn number_of_bits(&self) -> u64 {
        self.bitmap_bits
    }

    /// Return the number of hash functions used for `check` and `set`
    pub fn number_of_hash_functions(&self) -> u32 {
        self.k_num
    }

    /// Return the keys used by the sip hasher
    pub fn sip_keys(&self) -> [(u64, u64); 2] {
        [self.sips[0].keys(), self.sips[1].keys()]
    }

    #[allow(dead_code)]
    fn optimal_k_num(bitmap_bits: u64, items_count: usize) -> u32 {
        let m = bitmap_bits as f64;
        let n = items_count as f64;
        let k_num = (m / n * f64::ln(2.0f64)).ceil() as u32;
        cmp::max(k_num, 1)
    }

    fn bloom_hash(&self, hashes: &mut [u64; 2], item: &T, k_i: u32) -> u64
    where
        T: Hash,
    {
        if k_i < 2 {
            let sip = &mut self.sips[k_i as usize].clone();
            item.hash(sip);
            let hash = sip.finish();
            hashes[k_i as usize] = hash;
            hash
        } else {
            (hashes[0]).wrapping_add((k_i as u64).wrapping_mul(hashes[1]))
                % 0xFFFF_FFFF_FFFF_FFC5u64 //largest u64 prime
        }
    }

    /// Clear all of the bits in the filter, removing all keys from the set
    pub fn clear(&mut self) {
        self.bit_vec.clear()
    }

    /// Set all of the bits in the filter, making it appear like every key is in the set
    pub fn fill(&mut self) {
        self.bit_vec.set_all()
    }

    /// Test if there are no elements in the set
    pub fn is_empty(&self) -> bool {
        !self.bit_vec.any()
    }

    #[inline]
    fn sip_new(key: &[u8; 16]) -> SipHasher13 {
        SipHasher13::new_with_key(key)
    }
}