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#![deny(unsafe_code)]
#![cfg_attr(feature = "nightly", feature(test))]
///! Impl of Scalable Bloom Filters
///! http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.62.7953&rep=rep1&type=pdf
#[cfg(feature = "nightly")]
extern crate test;
use serde_derive::{Deserialize, Serialize};
use std::{
hash::{Hash, Hasher},
iter::Iterator,
num::NonZeroU64,
};
mod stable_hasher;
/// Base Bloom Filter
#[derive(Deserialize, Serialize, PartialEq, Clone, Debug)]
struct Bloom {
/// The actual bit field. Set to 0 with `Bloom::new`.
#[serde(rename = "b", with = "serde_bytes")]
buffer: Box<[u8]>,
/// The number of slices in the partitioned bloom filter.
/// Equivalent to the number of hash function in the classic bloom filter.
/// An insertion will result in a bit being set in each slice.
#[serde(rename = "k")]
num_slices: NonZeroU64,
}
impl Bloom {
/// Create a new Bloom filter (specifically, a Partitioned Bloom filter)
///
/// # Arguments
///
/// * `capacity` - target capacity. Panics if `capacity` is zero.
/// * `error_ratio` - false positive ratio (0..1.0).
fn new(capacity: usize, error_ratio: f64) -> Bloom {
// Directly from paper:
// k = log2(1/P) (num_slices)
// n ≈ −m ln(1−p) (slice_len_bits)
// M = k * m (total_bits)
// for optimal filter p = 0.5, which gives:
// n ≈ −m ln(0.5), rearranging: m = -n / ln(0.5) = n / ln(2)
debug_assert!(capacity >= 1);
debug_assert!(0.0 < error_ratio && error_ratio < 1.0);
// We're using ceil instead of round in order to get an error rate <= the desired.
// Using round can result in significantly higher error rates.
let num_slices = ((1.0 / error_ratio).log2()).ceil() as u64;
let slice_len_bits = (capacity as f64 / 2f64.ln()).ceil() as u64;
let total_bits = num_slices * slice_len_bits;
// round up to the next byte
let buffer_bytes = ((total_bits + 7) / 8) as usize;
let mut buffer = Vec::with_capacity(buffer_bytes);
buffer.resize(buffer_bytes, 0);
Bloom {
buffer: buffer.into_boxed_slice(),
num_slices: NonZeroU64::new(num_slices).unwrap(),
}
}
/// Create an index iterator for a given item.
///
/// This creates an iterator of pairs `(byte, mask)` indices in the buffer.
/// The iterator will return one pair of indexes for each slice in the bloom filter.
///
/// The pairs `(byte idx, byte mask)` are:
/// byte idx: byte idx in `self.buffer` to be extract for usage with the mask
/// byte mask: bit mask with a single bit set, can be ANDed (`&`) with
/// self.buffer[idx] to yield a number != 0 if the specified bit was set.
/// The mask can also be ORed (`|`) with the self.buffer[idx]
/// to set the corresponding bit.
///
/// # Arguments
///
/// * `item` - The item to hash.
#[inline]
fn index_iterator(&self, mut h1: u64, mut h2: u64) -> impl Iterator<Item = (usize, u8)> {
// The _bit_ length (thus buffer.len() multiplied by 8) of each slice within buffer.
// We'll use a NonZero type so that the compiler can avoid checking for
// division/modulus by 0 inside the iterator.
let slice_len = NonZeroU64::new(self.buffer.len() as u64 * 8 / self.num_slices).unwrap();
// Generate `self.num_slices` hashes from 2 hashes, using enhanced double hashing.
// See https://en.wikipedia.org/wiki/Double_hashing#Enhanced_double_hashing for details.
// We choose to use 2x64 bit hashes instead of 2x32 ones as it gives significant better false positive ratios.
debug_assert_ne!(h2, 0, "Second hash can't be 0 for double hashing");
(0..self.num_slices.get()).map(move |i| {
// Calculate hash(i)
let hi = h1 % slice_len + i * slice_len.get();
// Advance enhanced double hashing state
h1 = h1.wrapping_add(h2);
h2 = h2.wrapping_add(i);
// Resulting index/mask based on hash(i)
let idx = (hi / 8) as usize;
let mask = 1u8 << (hi % 8);
(idx, mask)
})
}
/// Insert an item identified by two hashes is in the Bloom.
/// # Arguments
///
/// * `h1` - The main hash
/// * `h2` - The second hash (must be != 0)
///
/// # Example
///
///
/// use growable_bloom_filter::Bloom;
/// let bloom = Bloom::new(2, 128);
///
/// let (h1, h2) = double_hashing_hashes("my-item");
/// bloom.insert(h1, h2);
///
#[inline]
fn insert(&mut self, h1: u64, h2: u64) {
// Set all bits (one per slice) corresponding to this item.
//
// Setting the bit:
// 1000 0011 (self.buffer[idx])
// 0001 0000 (mask)
// |---------
// 1001 0011
//
for (byte, mask) in self.index_iterator(h1, h2) {
self.buffer[byte] |= mask;
}
}
/// Test if item identified by two hashes is in the Bloom.
///
/// # Arguments
///
/// * `h1` - The main hash
/// * `h2` - The second hash (must be != 0)
///
/// # Example
///
/// let bloom = Bloom:new(2, 128);
///
/// let (h1, h2) = double_hashing_hashes("my-item");
/// bloom.insert(h1, h2);
///
/// assert!(bloom.contains(h1, h2));
///
#[inline]
fn contains(&self, h1: u64, h2: u64) -> bool {
// Check if all bits (one per slice) corresponding to this item are set.
// See index_iterator comments for a detailed explanation.
//
// Potentially found case:
// 0111 1111 (self.buffer[idx])
// 0001 0000 (mask)
// &---------
// 0001 0000 != 0
//
// Definitely not found case:
// 1110 1111 (self.buffer[idx])
// 0001 0000 (mask)
// &---------
// 0000 0000 == 0
//
self.index_iterator(h1, h2)
.all(|(byte, mask)| self.buffer[byte] & mask != 0)
}
}
/// Return 2 hashes for `item` that can be used as h1 and h2 fordouble hashing.
/// See https://en.wikipedia.org/wiki/Double_hashing#Enhanced_double_hashing for details.
#[inline]
fn double_hashing_hashes<T: Hash>(item: T) -> (u64, u64) {
let mut hasher = stable_hasher::StableHasher::new();
item.hash(&mut hasher);
let h1 = hasher.finish();
// Write a nul byte to the existing state and get another hash.
// This is appropriate when using a very high quality hasher,
// which we know is the case.
0u8.hash(&mut hasher);
// h2 hash shouldn't be 0 for double hashing
let h2 = hasher.finish().max(1);
(h1, h2)
}
// From the paper:
// Considering the choice of s (GROWTH_FACTOR) = 2 for small expected growth and s = 4
// for larger growth, one can see that r (TIGHTENING_RATIO) around 0.8 – 0.9 is a sensible choice.
// Here we select good defaults for 10~1000x growth.
const DEFAULT_GROWTH_FACTOR: usize = 2;
const DEFAULT_TIGHTENING_RATIO: f64 = 0.8515625; // ~0.85 but has exact representation in f32/f64
const fn default_growth_factor() -> usize {
DEFAULT_GROWTH_FACTOR
}
const fn default_tightening_ratio() -> f64 {
DEFAULT_TIGHTENING_RATIO
}
/// A Growable Bloom Filter
///
/// # Overview
///
/// Implementation of [Scalable Bloom Filters](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.62.7953&rep=rep1&type=pdf)
/// which also provides serde serialization and deserialize.
///
/// A bloom filter lets you `insert` items, and then test association with `contains`.
/// It's space and time efficient, at the cost of false positives.
/// In particular, if `contains` returns `true`, it may be in filter.
/// But if `contains` returns false, it's definitely not in the bloom filter.
///
/// You can control the failure rate by setting `desired_error_prob` and `est_insertions` appropriately.
///
/// # Applications
///
/// Bloom filters are typically used as a pre-cache to avoid expensive operations.
/// For example, if you need to ask ten thousand servers if they have data XYZ,
/// you could use GrowableBloom to figure out which ones do NOT have XYZ.
///
/// # Example
///
/// ```rust
/// use growable_bloom_filter::GrowableBloom;
///
/// // Create and insert into the bloom filter
/// let mut gbloom = GrowableBloom::new(0.05, 1000);
/// gbloom.insert(&0);
/// assert!(gbloom.contains(&0));
///
/// // Serialize and Deserialize the bloom filter
/// use serde_json;
///
/// let s = serde_json::to_string(&gbloom).unwrap();
/// let des_gbloom: GrowableBloom = serde_json::from_str(&s).unwrap();
/// assert!(des_gbloom.contains(&0));
///
/// // Builder API
/// use growable_bloom_filter::GrowableBloomBuilder;
/// let mut gbloom = GrowableBloomBuilder::new()
/// .estimated_insertions(100)
/// .desired_error_ratio(0.05)
/// .build();
/// gbloom.insert(&0);
/// assert!(gbloom.contains(&0));
/// ```
#[derive(Deserialize, Serialize, PartialEq, Clone, Debug)]
pub struct GrowableBloom {
/// The constituent bloom filters
#[serde(rename = "b")]
blooms: Vec<Bloom>,
#[serde(rename = "e")]
desired_error_prob: f64,
#[serde(rename = "t")]
est_insertions: usize,
/// Number of items successfully inserted
#[serde(rename = "i")]
inserts: usize,
/// Item capacity
#[serde(rename = "c")]
capacity: usize,
/// Growth factor
#[serde(rename = "g", default = "default_growth_factor")]
growth_factor: usize,
#[serde(rename = "r", default = "default_tightening_ratio")]
tightening_ratio: f64,
}
impl GrowableBloom {
/// Create a new GrowableBloom filter.
///
/// # Arguments
///
/// * `desired_error_prob` - The desired error probability (eg. 0.05, 0.01)
/// * `est_insertions` - The estimated number of insertions (eg. 100, 1000).
///
/// Note: You really don't need to be accurate with est_insertions.
/// Power of 10 granularity should be fine (~1000 is decent).
///
/// # Example
///
/// ```rust
/// // 5% failure rate, estimated 100 elements to insert
/// use growable_bloom_filter::GrowableBloom;
/// let mut gbloom = GrowableBloom::new(0.05, 100);
/// ```
///
/// # Panics
///
/// * Panics if desired_error_prob is less then 0 or greater than 1.
/// * Panics if capacity is zero. If you're unsure, set it to 1000.
///
/// # Builder API
/// An alternative way to construct a GrowableBloom.
///
/// See [`GrowableBloomBuilder`] for documentation. It allows you to specify
/// other constants to control bloom filter behaviour.
///
/// ```rust
/// use growable_bloom_filter::GrowableBloomBuilder;
/// let mut gbloom = GrowableBloomBuilder::new()
/// .estimated_insertions(100)
/// .desired_error_ratio(0.05)
/// .build();
/// ```
#[inline]
pub fn new(desired_error_prob: f64, est_insertions: usize) -> GrowableBloom {
Self::new_with_internals(
desired_error_prob,
est_insertions,
DEFAULT_GROWTH_FACTOR,
DEFAULT_TIGHTENING_RATIO,
)
}
pub(crate) fn new_with_internals(
desired_error_prob: f64,
est_insertions: usize,
growth_factor: usize,
tightening_ratio: f64,
) -> GrowableBloom {
assert!(0.0 < desired_error_prob && desired_error_prob < 1.0);
assert!(growth_factor > 1);
GrowableBloom {
blooms: vec![],
desired_error_prob,
est_insertions,
inserts: 0,
capacity: 0,
growth_factor,
tightening_ratio,
}
}
/// Test if `item` in the Bloom filter.
///
/// If `true` is returned, it _may_ be in the filter.
/// If `false` is returned, it's NOT in the filter.
///
/// # Arguments
///
/// * `item` - The item to test
///
/// # Example
///
/// ```rust
/// use growable_bloom_filter::GrowableBloom;
/// let mut bloom = GrowableBloom::new(0.05, 10);
/// let item = 0;
///
/// bloom.insert(&item);
/// assert!(bloom.contains(&item));
/// ```
pub fn contains<T: Hash>(&self, item: T) -> bool {
let (h1, h2) = double_hashing_hashes(item);
self.blooms.iter().any(|bloom| bloom.contains(h1, h2))
}
/// Insert `item` into the filter.
///
/// This may resize the GrowableBloom.
///
/// # Arguments
///
/// * `item` - The item to insert
///
/// # Example
///
/// ```rust
/// use growable_bloom_filter::GrowableBloom;
/// let mut bloom = GrowableBloom::new(0.05, 10);
/// let item = 0;
///
/// bloom.insert(&item);
/// bloom.insert(&-1);
/// bloom.insert(&vec![1, 2, 3]);
/// bloom.insert("hello");
/// ```
pub fn insert<T: Hash>(&mut self, item: T) -> bool {
let (h1, h2) = double_hashing_hashes(item);
// Step 1: Ask if we already have it
if self.blooms.iter().any(|bloom| bloom.contains(h1, h2)) {
return false;
}
// Step 2: Grow if necessary
if self.inserts >= self.capacity {
self.grow();
}
// Step 3: Insert it into the last
self.inserts += 1;
let curr_bloom = self.blooms.last_mut().unwrap();
curr_bloom.insert(h1, h2);
true
}
/// Clear the bloom filter.
///
/// This does not resize the filter.
///
/// # Example
///
/// ```rust
/// use growable_bloom_filter::GrowableBloom;
/// let mut bloom = GrowableBloom::new(0.05, 10);
/// let item = 0;
///
/// bloom.insert(&item);
/// assert!(bloom.contains(&item));
/// bloom.clear();
/// assert!(!bloom.contains(&item)); // No longer contains item
/// ```
pub fn clear(&mut self) {
self.blooms.clear();
self.inserts = 0;
self.capacity = 0;
}
/// Whether this bloom filter contain any items.
#[inline]
pub fn is_empty(&self) -> bool {
self.inserts == 0
}
/// The current estimated number of elements added to the filter.
/// This is an estimation, so it may or may not increase after
/// an insertion in the filter.
///
/// # Example
///
/// ```rust
/// use growable_bloom_filter::GrowableBloom;
/// let mut bloom = GrowableBloom::new(0.05, 10);
///
/// bloom.insert(0);
/// assert_eq!(bloom.len(), 1);
/// ```
#[inline]
pub fn len(&self) -> usize {
self.inserts
}
/// The current estimated capacity of the filter.
/// A filter starts with a small capacity and will expand to accommodate more items.
/// The actual ratio of increase depends on the values used to construct the bloom filter.
///
/// Note: An empty filter has capacity zero as we haven't calculated
/// the necessary bloom filter size. Subsequent inserts will result
/// in the capacity updating.
///
/// # Example
///
/// ```rust
/// use growable_bloom_filter::GrowableBloom;
/// let mut bloom = GrowableBloom::new(0.05, 10);
///
/// assert_eq!(bloom.capacity(), 0);
///
/// bloom.insert(0);
/// // After an insert, our capacity is no longer zero.
/// assert_ne!(bloom.capacity(), 0);
/// ```
#[inline]
pub fn capacity(&self) -> usize {
self.capacity
}
/// Record if `item` already exists in the filter, and insert it if it doesn't already exist.
///
/// Returns `true` if the item already existed in the filter.
///
/// Note: This isn't faster than just inserting.
///
/// # Example
///
/// ```rust
/// use growable_bloom_filter::GrowableBloom;
/// let mut bloom = GrowableBloom::new(0.05, 10);
/// let item = 0;
///
/// let existed_before = bloom.check_and_set(&item);
/// assert!(existed_before == false);
///
/// let existed_before = bloom.check_and_set(&item);
/// assert!(existed_before == true);
/// ```
pub fn check_and_set<T: Hash>(&mut self, item: T) -> bool {
!self.insert(item)
}
/// Grow the GrowableBloom
fn grow(&mut self) {
// The paper gives an upper bound formula for the fp rate: fpUB <= fp0 * / (1-r)
// This is because each sub bloom filter is created with an ever smaller
// false-positive ratio, forming a geometric progression.
// let r = TIGHTENING_RATIO
// fpUB ~= fp0 * fp0*r * fp0*r*r * fp0*r*r*r ...
// fp(x) = fp0 * (r**x)
let error_ratio =
self.desired_error_prob * self.tightening_ratio.powi(self.blooms.len() as _);
// In order to have relatively small space overhead compared to a single appropriately sized bloom filter
// the sub filters should be created with increasingly bigger sizes.
// let s = GROWTH_FACTOR
// cap(x) = cap0 * (s**x)
let capacity = self.est_insertions * self.growth_factor.pow(self.blooms.len() as _);
let new_bloom = Bloom::new(capacity, error_ratio);
self.blooms.push(new_bloom);
self.capacity += capacity;
}
}
/// Builder API for GrowableBloom.
///
/// ```rust
/// use growable_bloom_filter::GrowableBloomBuilder;
/// let mut gbloom = GrowableBloomBuilder::new()
/// .estimated_insertions(100)
/// .desired_error_ratio(0.05)
/// .build();
/// ```
pub struct GrowableBloomBuilder {
desired_error_ratio: f64,
est_insertions: usize,
growth_factor: usize,
tightening_ratio: f64,
}
impl GrowableBloomBuilder {
/// Create a new GrowableBloomBuilder.
///
/// Builder API for GrowableBloom.
///
/// ```rust
/// use growable_bloom_filter::GrowableBloomBuilder;
/// let mut gbloom = GrowableBloomBuilder::new()
/// .estimated_insertions(1000)
/// .desired_error_ratio(0.01)
/// .growth_factor(2)
/// .tightening_ratio(0.85)
/// .build();
/// gbloom.insert("hello world");
/// assert!(gbloom.contains(&"hello world"));
/// ```
pub fn new() -> Self {
Self {
est_insertions: 1000,
desired_error_ratio: 0.01,
growth_factor: DEFAULT_GROWTH_FACTOR,
tightening_ratio: DEFAULT_TIGHTENING_RATIO,
}
}
/// Estimated number of insertions. A power of ten accuracy is good enough.
///
/// # Panics
///
/// This will panic in debug mode if count is zero.
pub fn estimated_insertions(self, count: usize) -> Self {
Self {
est_insertions: count,
..self
}
}
/// Desired error ratio (i.e. false positive rate).
///
/// Smaller error ratios will use more memory and might be a bit slower.
///
/// # Panics
///
/// This will panic if the error ratio is outside of (0, 1.0).
pub fn desired_error_ratio(self, ratio: f64) -> Self {
Self {
desired_error_ratio: ratio,
..self
}
}
/// Base for the exponential growth factor.
///
/// As more items are inserted into a GrowableBloom this growth_factor
/// number is used to exponentially grow the capacity of newly added
/// internal bloom filters. So this number is raised to some exponent proportional
/// to the number of bloom filters held internally.
///
/// Basically it'll control how quickly the bloom filter grows in capacity.
/// By default it's set to two.
pub fn growth_factor(self, factor: usize) -> Self {
Self {
growth_factor: factor,
..self
}
}
/// Control the downwards adjustment on the error ratio when growing.
///
/// When GrowableBloom adds a new internal bloom filter it uses
/// the tightening_ratio to adjust the desired_error_ratio on these
/// new, larger internal bloom filters. This is necessary to achieve decent
/// accuracy on the user's desired error_ratio while using larger and larger
/// bloom filters internally.
///
/// By default this library sets it to ~0.85, but for smaller growth factors
/// any number around 0.8 - 0.9 should be fine.
pub fn tightening_ratio(self, ratio: f64) -> Self {
assert!(0.0 < ratio && ratio < 1.0);
Self {
tightening_ratio: ratio,
..self
}
}
/// Consume the builder to create a GrowableBloom.
///
/// # Panics
///
/// This will panic if an invalid value is specified.
pub fn build(self) -> GrowableBloom {
GrowableBloom::new_with_internals(
self.desired_error_ratio,
self.est_insertions,
self.growth_factor,
self.tightening_ratio,
)
}
}
#[cfg(test)]
mod growable_bloom_tests {
mod test_bloom {
use crate::{double_hashing_hashes, Bloom};
#[test]
fn can_insert_bloom() {
let mut b = Bloom::new(100, 0.01);
let (h1, h2) = double_hashing_hashes(123);
b.insert(h1, h2);
assert!(b.contains(h1, h2))
}
#[test]
fn can_insert_string_bloom() {
let mut b = Bloom::new(100, 0.01);
let (h1, h2) = double_hashing_hashes("hello world".to_string());
b.insert(h1, h2);
assert!(b.contains(h1, h2))
}
#[test]
fn does_not_contain() {
let mut b = Bloom::new(100, 0.01);
let upper = 100;
for i in (0..upper).step_by(2) {
let (h1, h2) = double_hashing_hashes(i);
b.insert(h1, h2);
assert!(b.contains(h1, h2))
}
for i in (1..upper).step_by(2) {
let (h1, h2) = double_hashing_hashes(i);
assert!(!b.contains(h1, h2))
}
}
#[test]
fn can_insert_lots() {
let mut b = Bloom::new(100, 0.01);
for i in 0..1024 {
let (h1, h2) = double_hashing_hashes(i);
b.insert(h1, h2);
assert!(b.contains(h1, h2))
}
}
#[test]
fn test_refs() {
let item = String::from("Hello World");
let mut b = Bloom::new(100, 0.01);
let (h1, h2) = double_hashing_hashes(&item);
b.insert(h1, h2);
assert!(b.contains(h1, h2))
}
}
mod test_growable {
use crate::{GrowableBloom, DEFAULT_TIGHTENING_RATIO};
use serde_json;
#[test]
fn can_insert() {
let mut b = GrowableBloom::new(0.05, 1000);
let item = 20;
b.insert(&item);
assert!(b.contains(&item))
}
#[test]
fn len_capacity_clear() {
let mut b = GrowableBloom::new(0.05, 100);
assert_eq!(b.len(), 0);
assert_eq!(b.capacity(), 0);
let item = 20;
b.insert(&item);
assert_ne!(b.len(), 0);
assert_ne!(b.capacity(), 0);
b.clear();
assert_eq!(b.len(), 0);
assert_eq!(b.capacity(), 0);
}
#[test]
fn ensure_capacity() {
let mut b = GrowableBloom::new(0.05, 1);
assert_eq!(b.capacity(), 0);
b.insert("abc");
assert_eq!(b.capacity(), 1);
for i in 0..100 {
b.insert(i);
}
assert_eq!(b.capacity(), 127);
}
#[test]
fn can_insert_string() {
let mut b = GrowableBloom::new(0.05, 1000);
let item: String = "hello world".to_owned();
b.insert(&item);
assert!(b.contains(&item))
}
#[test]
fn does_not_contain() {
let mut b = GrowableBloom::new(0.05, 1000);
assert_eq!(b.contains(&"hello"), false);
b.insert(&0);
assert_eq!(b.contains(&"hello"), false);
b.insert(&1);
assert_eq!(b.contains(&"hello"), false);
b.insert(&2);
assert_eq!(b.contains(&"hello"), false);
}
#[test]
fn can_insert_a_lot_of_elements() {
let mut b = GrowableBloom::new(0.05, 1000);
for i in 0..1000 {
b.insert(&i);
assert!(b.contains(&i));
}
}
#[test]
fn can_serialize_deserialize() {
let mut b = GrowableBloom::new(0.05, 1000);
b.insert(&0);
let s = serde_json::to_string(&b).unwrap();
let b_s: GrowableBloom = serde_json::from_str(&s).unwrap();
assert!(b_s.contains(&0));
assert_ne!(b_s.contains(&1), true);
assert_ne!(b_s.contains(&1000), true);
}
#[test]
fn verify_saturation() {
for &fp in &[0.01, 0.001] {
// The paper gives an upper bound formula for the fp rate: fpUB <= fp0*/(1-r)
let fp_ub = fp / (1.0 - DEFAULT_TIGHTENING_RATIO);
let initial_cap = 100u64;
let growth = 1000u64;
let mut b = GrowableBloom::new(fp, initial_cap as usize);
// insert 1000x more elements than initially allocated
for i in 1u64..=initial_cap * growth {
b.insert(&i);
if i % (initial_cap * growth / 10) == 0
|| [1, 2, 5, 10, 25].iter().any(|&g| i == initial_cap * g)
{
// A lot of tests are required to get a good estimate
let est_fp_rate = (i + 1..).take(50_000).filter(|i| b.contains(i)).count()
as f64
/ 50_000.0;
// Uncomment the following to get good output for experiments
// println!(
// "{}x cap: {}fp ({}x)",
// i / initial_cap,
// est_fp_rate,
// est_fp_rate / fp
// );
assert!(est_fp_rate <= fp_ub);
}
}
for i in 1u64..=initial_cap * growth {
assert!(b.contains(&i));
}
}
}
#[test]
fn test_types_saturation() {
let mut b = GrowableBloom::new(0.50, 100);
b.insert(&vec![1, 2, 3]);
b.insert("hello");
b.insert(&-1);
b.insert(&0);
}
#[test]
fn can_check_and_set() {
let mut b = GrowableBloom::new(0.05, 1000);
let item = 20;
assert!(!b.check_and_set(&item));
assert!(b.check_and_set(&item));
}
}
mod test_builder {
use crate::GrowableBloomBuilder;
#[test]
fn can_build_bloom() {
let mut gbloom = GrowableBloomBuilder::new().build();
gbloom.insert(3);
assert!(gbloom.contains(&3));
}
#[test]
#[should_panic]
fn should_panic_on_bad_error_ratio() {
GrowableBloomBuilder::new()
.estimated_insertions(1000)
.desired_error_ratio(99.9)
.build();
}
#[test]
#[should_panic]
fn should_panic_on_too_small_tightening_ratio() {
GrowableBloomBuilder::new().tightening_ratio(0.0).build();
}
#[test]
#[should_panic]
fn should_panic_on_too_large_tightening_ratio() {
GrowableBloomBuilder::new().tightening_ratio(10.0).build();
}
#[test]
fn can_specify_all_values() {
// From https://github.com/dpbriggs/growable-bloom-filters/issues/7
let mut gbloom = GrowableBloomBuilder::new()
.estimated_insertions(3)
.desired_error_ratio(0.00001)
.tightening_ratio(0.5)
.growth_factor(2)
.build();
for i in 0..100 {
gbloom.insert(i);
}
for i in 0..100 {
assert!(gbloom.contains(&i));
}
}
}
#[cfg(feature = "nightly")]
mod bench {
use crate::GrowableBloom;
use test::Bencher;
#[bench]
fn bench_new(b: &mut Bencher) {
b.iter(|| GrowableBloom::new(0.01, 1000));
}
#[bench]
fn bench_insert_normal_prob(b: &mut Bencher) {
let mut gbloom = GrowableBloom::new(0.01, 1000);
b.iter(|| gbloom.insert(10));
}
#[bench]
fn bench_insert_small_prob(b: &mut Bencher) {
let mut gbloom = GrowableBloom::new(0.001, 1000);
b.iter(|| gbloom.insert(10));
}
#[bench]
fn bench_many(b: &mut Bencher) {
let mut gbloom = GrowableBloom::new(0.01, 100000);
b.iter(|| gbloom.insert(10));
}
#[bench]
fn bench_insert_medium(b: &mut Bencher) {
let s: String = (0..100).map(|_| 'X').collect();
let mut gbloom = GrowableBloom::new(0.01, 100000);
b.iter(|| gbloom.insert(&s))
}
#[bench]
fn bench_insert_large(b: &mut Bencher) {
let s: String = (0..10000).map(|_| 'X').collect();
let mut gbloom = GrowableBloom::new(0.01, 100000);
b.iter(|| gbloom.insert(&s))
}
#[bench]
fn bench_insert_large_very_small_prob(b: &mut Bencher) {
let s: String = (0..10000).map(|_| 'X').collect();
let mut gbloom = GrowableBloom::new(0.0001, 100000);
b.iter(|| gbloom.insert(&s))
}
#[bench]
fn bench_grow(b: &mut Bencher) {
b.iter(|| {
let mut gbloom = GrowableBloom::new(0.01, 100);
for i in 0..1000 {
gbloom.insert(&i);
assert!(gbloom.contains(&i));
}
})
}
}
}