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// This Source Code Form is subject to the terms of the Mozilla Public
// License, v. 2.0. If a copy of the MPL was not distributed with this
use std::collections::HashMap;
use serde::{Deserialize, Serialize};
use super::{Bucketing, Histogram};
use crate::util::floating_point_context::FloatingPointContext;
/// A functional bucketing algorithm.
///
/// Bucketing is performed by a function, rather than pre-computed buckets.
/// The bucket index of a given sample is determined with the following function:
///
/// i = ⌊n log<sub>base</sub>(𝑥)⌋
///
/// In other words, there are n buckets for each power of `base` magnitude.
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub struct Functional {
exponent: f64,
}
impl Functional {
/// Instantiate a new functional bucketing.
fn new(log_base: f64, buckets_per_magnitude: f64) -> Functional {
// Set the FPU control flag to the required state within this function
let _fpc = FloatingPointContext::new();
let exponent = log_base.powf(1.0 / buckets_per_magnitude);
Functional { exponent }
}
/// Maps a sample to a "bucket index" that it belongs in.
/// A "bucket index" is the consecutive integer index of each bucket, useful as a
/// mathematical concept, even though the internal representation is stored and
/// sent using the minimum value in each bucket.
fn sample_to_bucket_index(&self, sample: u64) -> u64 {
// Set the FPU control flag to the required state within this function
let _fpc = FloatingPointContext::new();
((sample.saturating_add(1)) as f64).log(self.exponent) as u64
}
/// Determines the minimum value of a bucket, given a bucket index.
fn bucket_index_to_bucket_minimum(&self, index: u64) -> u64 {
// Set the FPU control flag to the required state within this function
let _fpc = FloatingPointContext::new();
self.exponent.powf(index as f64) as u64
}
}
impl Bucketing for Functional {
fn sample_to_bucket_minimum(&self, sample: u64) -> u64 {
if sample == 0 {
return 0;
}
let index = self.sample_to_bucket_index(sample);
self.bucket_index_to_bucket_minimum(index)
}
fn ranges(&self) -> &[u64] {
unimplemented!("Bucket ranges for functional bucketing are not precomputed")
}
}
impl Histogram<Functional> {
/// Creates a histogram with functional buckets.
pub fn functional(log_base: f64, buckets_per_magnitude: f64) -> Histogram<Functional> {
Histogram {
values: HashMap::new(),
count: 0,
sum: 0,
bucketing: Functional::new(log_base, buckets_per_magnitude),
}
}
/// Gets a snapshot of all contiguous values.
///
/// **Caution** This is a more specific implementation of `snapshot_values` on functional
/// histograms. `snapshot_values` cannot be used with those, due to buckets not being
/// precomputed.
pub fn snapshot(&self) -> HashMap<u64, u64> {
if self.values.is_empty() {
return HashMap::new();
}
let mut min_key = None;
let mut max_key = None;
// `Iterator#min` and `Iterator#max` would do the same job independently,
// but we want to avoid iterating the keys twice, so we loop ourselves.
for key in self.values.keys() {
let key = *key;
// safe unwrap, we checked it's not none
if min_key.is_none() || key < min_key.unwrap() {
min_key = Some(key);
}
// safe unwrap, we checked it's not none
if max_key.is_none() || key > max_key.unwrap() {
max_key = Some(key);
}
}
// Non-empty values, therefore minimum/maximum exists.
// safe unwraps, we set it at least once.
let min_bucket = self.bucketing.sample_to_bucket_index(min_key.unwrap());
let max_bucket = self.bucketing.sample_to_bucket_index(max_key.unwrap()) + 1;
let mut values = self.values.clone();
for idx in min_bucket..=max_bucket {
// Fill in missing entries.
let min_bucket = self.bucketing.bucket_index_to_bucket_minimum(idx);
let _ = values.entry(min_bucket).or_insert(0);
}
values
}
}
#[cfg(test)]
mod test {
use super::*;
#[test]
fn can_count() {
let mut hist = Histogram::functional(2.0, 8.0);
assert!(hist.is_empty());
for i in 1..=10 {
hist.accumulate(i);
}
assert_eq!(10, hist.count());
assert_eq!(55, hist.sum());
}
#[test]
fn sample_to_bucket_minimum_correctly_rounds_down() {
let hist = Histogram::functional(2.0, 8.0);
// Check each of the first 100 integers, where numerical accuracy of the round-tripping
// is most potentially problematic
for value in 0..100 {
let bucket_minimum = hist.bucketing.sample_to_bucket_minimum(value);
assert!(bucket_minimum <= value);
assert_eq!(
bucket_minimum,
hist.bucketing.sample_to_bucket_minimum(bucket_minimum)
);
}
// Do an exponential sampling of higher numbers
for i in 11..500 {
let value = 1.5f64.powi(i);
let value = value as u64;
let bucket_minimum = hist.bucketing.sample_to_bucket_minimum(value);
assert!(bucket_minimum <= value);
assert_eq!(
bucket_minimum,
hist.bucketing.sample_to_bucket_minimum(bucket_minimum)
);
}
}
#[test]
fn histogram_merge() {
let mut hist = Histogram::functional(2.0, 8.0);
// Check each of the first 100 integers, where numerical accuracy of the round-tripping
// is most potentially problematic
for sample in 0..100 {
hist.accumulate(sample);
}
let mut filled_hist = hist.clone();
let mut hist2 = Histogram::functional(2.0, 8.0);
for sample in 100..200 {
hist.accumulate(sample);
hist2.accumulate(sample);
}
filled_hist.merge(&hist2);
assert_eq!(filled_hist, hist);
}
#[test]
#[should_panic]
fn histogram_merge_different_buckets() {
let mut hist1 = Histogram::functional(2.0, 8.0);
let hist2 = Histogram::functional(1.0, 4.0);
hist1.merge(&hist2);
}
#[test]
fn histogram_clears() {
let mut hist = Histogram::functional(2.0, 8.0);
let empty_hist = hist.clone();
assert_eq!(empty_hist, hist);
hist.accumulate(13);
assert_ne!(empty_hist, hist);
hist.clear();
assert_eq!(empty_hist, hist);
}
}