moka/common/frequency_sketch.rs
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// License and Copyright Notice:
//
// Some of the code and doc comments in this module were ported or copied from
// a Java class `com.github.benmanes.caffeine.cache.FrequencySketch` of Caffeine.
// https://github.com/ben-manes/caffeine/blob/master/caffeine/src/main/java/com/github/benmanes/caffeine/cache/FrequencySketch.java
//
// The original code/comments from Caffeine are licensed under the Apache License,
// Version 2.0 <https://github.com/ben-manes/caffeine/blob/master/LICENSE>
//
// Copyrights of the original code/comments are retained by their contributors.
// For full authorship information, see the version control history of
// https://github.com/ben-manes/caffeine/
/// A probabilistic multi-set for estimating the popularity of an element within
/// a time window. The maximum frequency of an element is limited to 15 (4-bits)
/// and an aging process periodically halves the popularity of all elements.
#[derive(Default)]
pub(crate) struct FrequencySketch {
sample_size: u32,
table_mask: u64,
table: Box<[u64]>,
size: u32,
}
// A mixture of seeds from FNV-1a, CityHash, and Murmur3. (Taken from Caffeine)
static SEED: [u64; 4] = [
0xc3a5_c85c_97cb_3127,
0xb492_b66f_be98_f273,
0x9ae1_6a3b_2f90_404f,
0xcbf2_9ce4_8422_2325,
];
static RESET_MASK: u64 = 0x7777_7777_7777_7777;
static ONE_MASK: u64 = 0x1111_1111_1111_1111;
// -------------------------------------------------------------------------------
// Some of the code and doc comments in this module were ported or copied from
// a Java class `com.github.benmanes.caffeine.cache.FrequencySketch` of Caffeine.
// https://github.com/ben-manes/caffeine/blob/master/caffeine/src/main/java/com/github/benmanes/caffeine/cache/FrequencySketch.java
// -------------------------------------------------------------------------------
//
// FrequencySketch maintains a 4-bit CountMinSketch [1] with periodic aging to
// provide the popularity history for the TinyLfu admission policy [2].
// The time and space efficiency of the sketch allows it to cheaply estimate the
// frequency of an entry in a stream of cache access events.
//
// The counter matrix is represented as a single dimensional array holding 16
// counters per slot. A fixed depth of four balances the accuracy and cost,
// resulting in a width of four times the length of the array. To retain an
// accurate estimation the array's length equals the maximum number of entries
// in the cache, increased to the closest power-of-two to exploit more efficient
// bit masking. This configuration results in a confidence of 93.75% and error
// bound of e / width.
//
// The frequency of all entries is aged periodically using a sampling window
// based on the maximum number of entries in the cache. This is referred to as
// the reset operation by TinyLfu and keeps the sketch fresh by dividing all
// counters by two and subtracting based on the number of odd counters
// found. The O(n) cost of aging is amortized, ideal for hardware pre-fetching,
// and uses inexpensive bit manipulations per array location.
//
// [1] An Improved Data Stream Summary: The Count-Min Sketch and its Applications
// http://dimacs.rutgers.edu/~graham/pubs/papers/cm-full.pdf
// [2] TinyLFU: A Highly Efficient Cache Admission Policy
// https://dl.acm.org/citation.cfm?id=3149371
//
// -------------------------------------------------------------------------------
impl FrequencySketch {
/// Initializes and increases the capacity of this `FrequencySketch` instance,
/// if necessary, to ensure that it can accurately estimate the popularity of
/// elements given the maximum size of the cache. This operation forgets all
/// previous counts when resizing.
pub(crate) fn ensure_capacity(&mut self, cap: u32) {
// The max byte size of the table, Box<[u64; table_size]>
//
// | Pointer width | Max size |
// |:-----------------|---------:|
// | 16 bit | 8 KiB |
// | 32 bit | 128 MiB |
// | 64 bit or bigger | 8 GiB |
let maximum = if cfg!(target_pointer_width = "16") {
cap.min(1024)
} else if cfg!(target_pointer_width = "32") {
cap.min(2u32.pow(24)) // about 16 millions
} else {
// Same to Caffeine's limit:
// `Integer.MAX_VALUE >>> 1` with `ceilingPowerOfTwo()` applied.
cap.min(2u32.pow(30)) // about 1 billion
};
let table_size = if maximum == 0 {
1
} else {
maximum.next_power_of_two()
};
if self.table.len() as u32 >= table_size {
return;
}
self.table = vec![0; table_size as usize].into_boxed_slice();
self.table_mask = 0.max(table_size - 1) as u64;
self.sample_size = if cap == 0 {
10
} else {
maximum.saturating_mul(10).min(i32::MAX as u32)
};
}
/// Takes the hash value of an element, and returns the estimated number of
/// occurrences of the element, up to the maximum (15).
pub(crate) fn frequency(&self, hash: u64) -> u8 {
if self.table.is_empty() {
return 0;
}
let start = ((hash & 3) << 2) as u8;
let mut frequency = u8::MAX;
for i in 0..4 {
let index = self.index_of(hash, i);
let count = (self.table[index] >> ((start + i) << 2) & 0xF) as u8;
frequency = frequency.min(count);
}
frequency
}
/// Take a hash value of an element and increments the popularity of the
/// element if it does not exceed the maximum (15). The popularity of all
/// elements will be periodically down sampled when the observed events
/// exceeds a threshold. This process provides a frequency aging to allow
/// expired long term entries to fade away.
pub(crate) fn increment(&mut self, hash: u64) {
if self.table.is_empty() {
return;
}
let start = ((hash & 3) << 2) as u8;
let mut added = false;
for i in 0..4 {
let index = self.index_of(hash, i);
added |= self.increment_at(index, start + i);
}
if added {
self.size += 1;
if self.size >= self.sample_size {
self.reset();
}
}
}
/// Takes a table index (each entry has 16 counters) and counter index, and
/// increments the counter by 1 if it is not already at the maximum value
/// (15). Returns `true` if incremented.
fn increment_at(&mut self, table_index: usize, counter_index: u8) -> bool {
let offset = (counter_index as usize) << 2;
let mask = 0xF_u64 << offset;
if self.table[table_index] & mask != mask {
self.table[table_index] += 1u64 << offset;
true
} else {
false
}
}
/// Reduces every counter by half of its original value.
fn reset(&mut self) {
let mut count = 0u32;
for entry in self.table.iter_mut() {
// Count number of odd numbers.
count += (*entry & ONE_MASK).count_ones();
*entry = (*entry >> 1) & RESET_MASK;
}
self.size = (self.size >> 1) - (count >> 2);
}
/// Returns the table index for the counter at the specified depth.
fn index_of(&self, hash: u64, depth: u8) -> usize {
let i = depth as usize;
let mut hash = hash.wrapping_add(SEED[i]).wrapping_mul(SEED[i]);
hash = hash.wrapping_add(hash >> 32);
(hash & self.table_mask) as usize
}
#[cfg(feature = "unstable-debug-counters")]
pub(crate) fn table_size(&self) -> u64 {
(self.table.len() * std::mem::size_of::<u64>()) as u64
}
}
// Methods only available for testing.
#[cfg(test)]
impl FrequencySketch {
pub(crate) fn table_len(&self) -> usize {
self.table.len()
}
}
// Some test cases were ported from Caffeine at:
// https://github.com/ben-manes/caffeine/blob/master/caffeine/src/test/java/com/github/benmanes/caffeine/cache/FrequencySketchTest.java
//
// To see the debug prints, run test as `cargo test -- --nocapture`
#[cfg(test)]
mod tests {
use super::FrequencySketch;
use once_cell::sync::Lazy;
use std::hash::{BuildHasher, Hash, Hasher};
static ITEM: Lazy<u32> = Lazy::new(|| {
let mut buf = [0; 4];
getrandom::getrandom(&mut buf).unwrap();
unsafe { std::mem::transmute::<[u8; 4], u32>(buf) }
});
// This test was ported from Caffeine.
#[test]
fn increment_once() {
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(512);
let hasher = hasher();
let item_hash = hasher(*ITEM);
sketch.increment(item_hash);
assert_eq!(sketch.frequency(item_hash), 1);
}
// This test was ported from Caffeine.
#[test]
fn increment_max() {
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(512);
let hasher = hasher();
let item_hash = hasher(*ITEM);
for _ in 0..20 {
sketch.increment(item_hash);
}
assert_eq!(sketch.frequency(item_hash), 15);
}
// This test was ported from Caffeine.
#[test]
fn increment_distinct() {
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(512);
let hasher = hasher();
sketch.increment(hasher(*ITEM));
sketch.increment(hasher(ITEM.wrapping_add(1)));
assert_eq!(sketch.frequency(hasher(*ITEM)), 1);
assert_eq!(sketch.frequency(hasher(ITEM.wrapping_add(1))), 1);
assert_eq!(sketch.frequency(hasher(ITEM.wrapping_add(2))), 0);
}
// This test was ported from Caffeine.
#[test]
fn index_of_around_zero() {
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(512);
let mut indexes = std::collections::HashSet::new();
let hashes = [u64::MAX, 0, 1];
for hash in hashes.iter() {
for depth in 0..4 {
indexes.insert(sketch.index_of(*hash, depth));
}
}
assert_eq!(indexes.len(), 4 * hashes.len())
}
// This test was ported from Caffeine.
#[test]
fn reset() {
let mut reset = false;
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(64);
let hasher = hasher();
for i in 1..(20 * sketch.table.len() as u32) {
sketch.increment(hasher(i));
if sketch.size != i {
reset = true;
break;
}
}
assert!(reset);
assert!(sketch.size <= sketch.sample_size / 2);
}
// This test was ported from Caffeine.
#[test]
fn heavy_hitters() {
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(65_536);
let hasher = hasher();
for i in 100..100_000 {
sketch.increment(hasher(i));
}
for i in (0..10).step_by(2) {
for _ in 0..i {
sketch.increment(hasher(i));
}
}
// A perfect popularity count yields an array [0, 0, 2, 0, 4, 0, 6, 0, 8, 0]
let popularity = (0..10)
.map(|i| sketch.frequency(hasher(i)))
.collect::<Vec<_>>();
for (i, freq) in popularity.iter().enumerate() {
match i {
2 => assert!(freq <= &popularity[4]),
4 => assert!(freq <= &popularity[6]),
6 => assert!(freq <= &popularity[8]),
8 => (),
_ => assert!(freq <= &popularity[2]),
}
}
}
fn hasher<K: Hash>() -> impl Fn(K) -> u64 {
let build_hasher = std::collections::hash_map::RandomState::default();
move |key| {
let mut hasher = build_hasher.build_hasher();
key.hash(&mut hasher);
hasher.finish()
}
}
}
// Verify that some properties hold such as no panic occurs on any possible inputs.
#[cfg(kani)]
mod kani {
use super::FrequencySketch;
const CAPACITIES: &[u32] = &[
0,
1,
1024,
1025,
2u32.pow(24),
2u32.pow(24) + 1,
2u32.pow(30),
2u32.pow(30) + 1,
u32::MAX,
];
#[kani::proof]
fn verify_ensure_capacity() {
// Check for arbitrary capacities.
let capacity = kani::any();
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(capacity);
}
#[kani::proof]
fn verify_frequency() {
// Check for some selected capacities.
for capacity in CAPACITIES {
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(*capacity);
// Check for arbitrary hashes.
let hash = kani::any();
let frequency = sketch.frequency(hash);
assert!(frequency <= 15);
}
}
#[kani::proof]
fn verify_increment() {
// Only check for small capacities. Because Kani Rust Verifier is a model
// checking tool, it will take much longer time (exponential) to check larger
// capacities here.
for capacity in &[0, 1, 128] {
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(*capacity);
// Check for arbitrary hashes.
let hash = kani::any();
sketch.increment(hash);
}
}
#[kani::proof]
fn verify_index_of() {
// Check for arbitrary capacities.
let capacity = kani::any();
let mut sketch = FrequencySketch::default();
sketch.ensure_capacity(capacity);
// Check for arbitrary hashes.
let hash = kani::any();
for i in 0..4 {
let index = sketch.index_of(hash, i);
assert!(index < sketch.table.len());
}
}
}