Struct SparseHistogram

pub struct SparseHistogram {
    pub config: Config,
    pub index: Vec<usize>,
    pub count: Vec<u64>,
}

This histogram is a sparse, columnar representation of the regular Histogram. It is significantly smaller than a regular Histogram when a large number of buckets are zero, which is a frequent occurence. It stores an individual vector for each field of non-zero buckets. Assuming index[0] = n, (index[0], count[0]) corresponds to the nth bucket.

Fields

config: Config

parameters representing the resolution and the range of the histogram tracking request latencies

index: Vec<usize>

indices for the non-zero buckets in the histogram

count: Vec<u64>

histogram bucket counts corresponding to the indices

Implementations

impl SparseHistogram

pub fn new(grouping_power: u8, max_value_power: u8) -> Result<Self, Error>

Construct a new histogram from the provided parameters. See the documentation for crate::Config to understand their meaning.

pub fn with_config(config: &Config) -> Self

Creates a new histogram using a provided crate::Config.

pub fn wrapping_add( &self, h: &SparseHistogram, ) -> Result<SparseHistogram, Error>

Adds the other histogram to this histogram and returns the result as a new histogram.

An error is returned if the two histograms have incompatible parameters. Buckets which have values in both histograms are allowed to wrap.

pub fn checked_sub(&self, h: &SparseHistogram) -> Result<SparseHistogram, Error>

Subtracts the other histogram to this histogram and returns the result as a new histogram. The other histogram is expected to be a subset of the current histogram, i.e., for every bucket in the other histogram should have a count less than or equal to the corresponding bucket in this histogram.

An error is returned if the two histograms have incompatible parameters or if the other histogram is not a subset of this histogram.

pub fn percentiles( &self, percentiles: &[f64], ) -> Result<Option<Vec<(f64, Bucket)>>, Error>

Return a collection of percentiles from this histogram.

Each percentile should be in the inclusive range 0.0..=100.0. For example, the 50th percentile (median) can be found using 50.0.

The results will be sorted by the percentile.

pub fn percentile(&self, percentile: f64) -> Result<Option<Bucket>, Error>

Return a single percentile from this histogram.

The percentile should be in the inclusive range 0.0..=100.0. For example, the 50th percentile (median) can be found using 50.0.

pub fn downsample(&self, grouping_power: u8) -> Result<SparseHistogram, Error>

Returns a new histogram with a reduced grouping power. The reduced grouping power should lie in the range (0..existing grouping power).

This works by iterating over every bucket in the existing histogram and inserting the contained values into the new histogram. While we do not know the exact values of the data points (only that they lie within the bucket’s range), it does not matter since the bucket is not split during downsampling and any value can be used.

Trait Implementations

impl Clone for SparseHistogram

fn clone(&self) -> SparseHistogram

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SparseHistogram

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl From<&Histogram> for SparseHistogram

fn from(histogram: &Histogram) -> Self

Converts to this type from the input type.

impl From<&SparseHistogram> for Histogram

fn from(other: &SparseHistogram) -> Self

Converts to this type from the input type.

impl<'a> IntoIterator for &'a SparseHistogram

type Item = Bucket

The type of the elements being iterated over.

type IntoIter = Iter<'a>

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

impl PartialEq for SparseHistogram

fn eq(&self, other: &SparseHistogram) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for SparseHistogram