client_golang/prometheus/histogram.go

1754 lines
70 KiB
Go
Raw Permalink Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

// Copyright 2015 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package prometheus
import (
"fmt"
"math"
"runtime"
"sort"
"sync"
"sync/atomic"
"time"
dto "github.com/prometheus/client_model/go"
"google.golang.org/protobuf/proto"
"google.golang.org/protobuf/types/known/timestamppb"
)
// nativeHistogramBounds for the frac of observed values. Only relevant for
// schema > 0. The position in the slice is the schema. (0 is never used, just
// here for convenience of using the schema directly as the index.)
//
// TODO(beorn7): Currently, we do a binary search into these slices. There are
// ways to turn it into a small number of simple array lookups. It probably only
// matters for schema 5 and beyond, but should be investigated. See this comment
// as a starting point:
// https://github.com/open-telemetry/opentelemetry-specification/issues/1776#issuecomment-870164310
var nativeHistogramBounds = [][]float64{
// Schema "0":
{0.5},
// Schema 1:
{0.5, 0.7071067811865475},
// Schema 2:
{0.5, 0.5946035575013605, 0.7071067811865475, 0.8408964152537144},
// Schema 3:
{
0.5, 0.5452538663326288, 0.5946035575013605, 0.6484197773255048,
0.7071067811865475, 0.7711054127039704, 0.8408964152537144, 0.9170040432046711,
},
// Schema 4:
{
0.5, 0.5221368912137069, 0.5452538663326288, 0.5693943173783458,
0.5946035575013605, 0.620928906036742, 0.6484197773255048, 0.6771277734684463,
0.7071067811865475, 0.7384130729697496, 0.7711054127039704, 0.805245165974627,
0.8408964152537144, 0.8781260801866495, 0.9170040432046711, 0.9576032806985735,
},
// Schema 5:
{
0.5, 0.5109485743270583, 0.5221368912137069, 0.5335702003384117,
0.5452538663326288, 0.5571933712979462, 0.5693943173783458, 0.5818624293887887,
0.5946035575013605, 0.6076236799902344, 0.620928906036742, 0.6345254785958666,
0.6484197773255048, 0.6626183215798706, 0.6771277734684463, 0.6919549409819159,
0.7071067811865475, 0.7225904034885232, 0.7384130729697496, 0.7545822137967112,
0.7711054127039704, 0.7879904225539431, 0.805245165974627, 0.8228777390769823,
0.8408964152537144, 0.8593096490612387, 0.8781260801866495, 0.8973545375015533,
0.9170040432046711, 0.9370838170551498, 0.9576032806985735, 0.9785720620876999,
},
// Schema 6:
{
0.5, 0.5054446430258502, 0.5109485743270583, 0.5165124395106142,
0.5221368912137069, 0.5278225891802786, 0.5335702003384117, 0.5393803988785598,
0.5452538663326288, 0.5511912916539204, 0.5571933712979462, 0.5632608093041209,
0.5693943173783458, 0.5755946149764913, 0.5818624293887887, 0.5881984958251406,
0.5946035575013605, 0.6010783657263515, 0.6076236799902344, 0.6142402680534349,
0.620928906036742, 0.6276903785123455, 0.6345254785958666, 0.6414350080393891,
0.6484197773255048, 0.6554806057623822, 0.6626183215798706, 0.6698337620266515,
0.6771277734684463, 0.6845012114872953, 0.6919549409819159, 0.6994898362691555,
0.7071067811865475, 0.7148066691959849, 0.7225904034885232, 0.7304588970903234,
0.7384130729697496, 0.7464538641456323, 0.7545822137967112, 0.762799075372269,
0.7711054127039704, 0.7795022001189185, 0.7879904225539431, 0.7965710756711334,
0.805245165974627, 0.8140137109286738, 0.8228777390769823, 0.8318382901633681,
0.8408964152537144, 0.8500531768592616, 0.8593096490612387, 0.8686669176368529,
0.8781260801866495, 0.8876882462632604, 0.8973545375015533, 0.9071260877501991,
0.9170040432046711, 0.9269895625416926, 0.9370838170551498, 0.9472879907934827,
0.9576032806985735, 0.9680308967461471, 0.9785720620876999, 0.9892280131939752,
},
// Schema 7:
{
0.5, 0.5027149505564014, 0.5054446430258502, 0.5081891574554764,
0.5109485743270583, 0.5137229745593818, 0.5165124395106142, 0.5193170509806894,
0.5221368912137069, 0.5249720429003435, 0.5278225891802786, 0.5306886136446309,
0.5335702003384117, 0.5364674337629877, 0.5393803988785598, 0.5423091811066545,
0.5452538663326288, 0.5482145409081883, 0.5511912916539204, 0.5541842058618393,
0.5571933712979462, 0.5602188762048033, 0.5632608093041209, 0.5663192597993595,
0.5693943173783458, 0.572486072215902, 0.5755946149764913, 0.5787200368168754,
0.5818624293887887, 0.585021884841625, 0.5881984958251406, 0.5913923554921704,
0.5946035575013605, 0.5978321960199137, 0.6010783657263515, 0.6043421618132907,
0.6076236799902344, 0.6109230164863786, 0.6142402680534349, 0.6175755319684665,
0.620928906036742, 0.6243004885946023, 0.6276903785123455, 0.6310986751971253,
0.6345254785958666, 0.637970889198196, 0.6414350080393891, 0.6449179367033329,
0.6484197773255048, 0.6519406325959679, 0.6554806057623822, 0.659039800633032,
0.6626183215798706, 0.6662162735415805, 0.6698337620266515, 0.6734708931164728,
0.6771277734684463, 0.6808045103191123, 0.6845012114872953, 0.688217985377265,
0.6919549409819159, 0.6957121878859629, 0.6994898362691555, 0.7032879969095076,
0.7071067811865475, 0.7109463010845827, 0.7148066691959849, 0.718687998724491,
0.7225904034885232, 0.7265139979245261, 0.7304588970903234, 0.7344252166684908,
0.7384130729697496, 0.7424225829363761, 0.7464538641456323, 0.7505070348132126,
0.7545822137967112, 0.7586795205991071, 0.762799075372269, 0.7669409989204777,
0.7711054127039704, 0.7752924388424999, 0.7795022001189185, 0.7837348199827764,
0.7879904225539431, 0.7922691326262467, 0.7965710756711334, 0.8008963778413465,
0.805245165974627, 0.8096175675974316, 0.8140137109286738, 0.8184337248834821,
0.8228777390769823, 0.8273458838280969, 0.8318382901633681, 0.8363550898207981,
0.8408964152537144, 0.8454623996346523, 0.8500531768592616, 0.8546688815502312,
0.8593096490612387, 0.8639756154809185, 0.8686669176368529, 0.8733836930995842,
0.8781260801866495, 0.8828942179666361, 0.8876882462632604, 0.8925083056594671,
0.8973545375015533, 0.9022270839033115, 0.9071260877501991, 0.9120516927035263,
0.9170040432046711, 0.9219832844793128, 0.9269895625416926, 0.9320230241988943,
0.9370838170551498, 0.9421720895161669, 0.9472879907934827, 0.9524316709088368,
0.9576032806985735, 0.9628029718180622, 0.9680308967461471, 0.9732872087896164,
0.9785720620876999, 0.9838856116165875, 0.9892280131939752, 0.9945994234836328,
},
// Schema 8:
{
0.5, 0.5013556375251013, 0.5027149505564014, 0.5040779490592088,
0.5054446430258502, 0.5068150424757447, 0.5081891574554764, 0.509566998038869,
0.5109485743270583, 0.5123338964485679, 0.5137229745593818, 0.5151158188430205,
0.5165124395106142, 0.5179128468009786, 0.5193170509806894, 0.520725062344158,
0.5221368912137069, 0.5235525479396449, 0.5249720429003435, 0.526395386502313,
0.5278225891802786, 0.5292536613972564, 0.5306886136446309, 0.5321274564422321,
0.5335702003384117, 0.5350168559101208, 0.5364674337629877, 0.5379219445313954,
0.5393803988785598, 0.5408428074966075, 0.5423091811066545, 0.5437795304588847,
0.5452538663326288, 0.5467321995364429, 0.5482145409081883, 0.549700901315111,
0.5511912916539204, 0.5526857228508706, 0.5541842058618393, 0.5556867516724088,
0.5571933712979462, 0.5587040757836845, 0.5602188762048033, 0.5617377836665098,
0.5632608093041209, 0.564787964283144, 0.5663192597993595, 0.5678547070789026,
0.5693943173783458, 0.5709381019847808, 0.572486072215902, 0.5740382394200894,
0.5755946149764913, 0.5771552102951081, 0.5787200368168754, 0.5802891060137493,
0.5818624293887887, 0.5834400184762408, 0.585021884841625, 0.5866080400818185,
0.5881984958251406, 0.5897932637314379, 0.5913923554921704, 0.5929957828304968,
0.5946035575013605, 0.5962156912915756, 0.5978321960199137, 0.5994530835371903,
0.6010783657263515, 0.6027080545025619, 0.6043421618132907, 0.6059806996384005,
0.6076236799902344, 0.6092711149137041, 0.6109230164863786, 0.6125793968185725,
0.6142402680534349, 0.6159056423670379, 0.6175755319684665, 0.6192499490999082,
0.620928906036742, 0.622612415087629, 0.6243004885946023, 0.6259931389331581,
0.6276903785123455, 0.6293922197748583, 0.6310986751971253, 0.6328097572894031,
0.6345254785958666, 0.6362458516947014, 0.637970889198196, 0.6397006037528346,
0.6414350080393891, 0.6431741147730128, 0.6449179367033329, 0.6466664866145447,
0.6484197773255048, 0.6501778216898253, 0.6519406325959679, 0.6537082229673385,
0.6554806057623822, 0.6572577939746774, 0.659039800633032, 0.6608266388015788,
0.6626183215798706, 0.6644148621029772, 0.6662162735415805, 0.6680225691020727,
0.6698337620266515, 0.6716498655934177, 0.6734708931164728, 0.6752968579460171,
0.6771277734684463, 0.6789636531064505, 0.6808045103191123, 0.6826503586020058,
0.6845012114872953, 0.6863570825438342, 0.688217985377265, 0.690083933630119,
0.6919549409819159, 0.6938310211492645, 0.6957121878859629, 0.6975984549830999,
0.6994898362691555, 0.7013863456101023, 0.7032879969095076, 0.7051948041086352,
0.7071067811865475, 0.7090239421602076, 0.7109463010845827, 0.7128738720527471,
0.7148066691959849, 0.7167447066838943, 0.718687998724491, 0.7206365595643126,
0.7225904034885232, 0.7245495448210174, 0.7265139979245261, 0.7284837772007218,
0.7304588970903234, 0.7324393720732029, 0.7344252166684908, 0.7364164454346837,
0.7384130729697496, 0.7404151139112358, 0.7424225829363761, 0.7444354947621984,
0.7464538641456323, 0.7484777058836176, 0.7505070348132126, 0.7525418658117031,
0.7545822137967112, 0.7566280937263048, 0.7586795205991071, 0.7607365094544071,
0.762799075372269, 0.7648672334736434, 0.7669409989204777, 0.7690203869158282,
0.7711054127039704, 0.7731960915705107, 0.7752924388424999, 0.7773944698885442,
0.7795022001189185, 0.7816156449856788, 0.7837348199827764, 0.7858597406461707,
0.7879904225539431, 0.7901268813264122, 0.7922691326262467, 0.7944171921585818,
0.7965710756711334, 0.7987307989543135, 0.8008963778413465, 0.8030678282083853,
0.805245165974627, 0.8074284071024302, 0.8096175675974316, 0.8118126635086642,
0.8140137109286738, 0.8162207259936375, 0.8184337248834821, 0.820652723822003,
0.8228777390769823, 0.8251087869603088, 0.8273458838280969, 0.8295890460808079,
0.8318382901633681, 0.8340936325652911, 0.8363550898207981, 0.8386226785089391,
0.8408964152537144, 0.8431763167241966, 0.8454623996346523, 0.8477546807446661,
0.8500531768592616, 0.8523579048290255, 0.8546688815502312, 0.8569861239649629,
0.8593096490612387, 0.8616394738731368, 0.8639756154809185, 0.8663180910111553,
0.8686669176368529, 0.871022112577578, 0.8733836930995842, 0.8757516765159389,
0.8781260801866495, 0.8805069215187917, 0.8828942179666361, 0.8852879870317771,
0.8876882462632604, 0.890095013257712, 0.8925083056594671, 0.8949281411607002,
0.8973545375015533, 0.8997875124702672, 0.9022270839033115, 0.9046732696855155,
0.9071260877501991, 0.909585556079304, 0.9120516927035263, 0.9145245157024483,
0.9170040432046711, 0.9194902933879467, 0.9219832844793128, 0.9244830347552253,
0.9269895625416926, 0.92950288621441, 0.9320230241988943, 0.9345499949706191,
0.9370838170551498, 0.93962450902828, 0.9421720895161669, 0.9447265771954693,
0.9472879907934827, 0.9498563490882775, 0.9524316709088368, 0.9550139751351947,
0.9576032806985735, 0.9601996065815236, 0.9628029718180622, 0.9654133954938133,
0.9680308967461471, 0.9706554947643201, 0.9732872087896164, 0.9759260581154889,
0.9785720620876999, 0.9812252401044634, 0.9838856116165875, 0.9865531961276168,
0.9892280131939752, 0.9919100824251095, 0.9945994234836328, 0.9972960560854698,
},
}
// The nativeHistogramBounds above can be generated with the code below.
//
// TODO(beorn7): It's tempting to actually use `go generate` to generate the
// code above. However, this could lead to slightly different numbers on
// different architectures. We still need to come to terms if we are fine with
// that, or if we might prefer to specify precise numbers in the standard.
//
// var nativeHistogramBounds [][]float64 = make([][]float64, 9)
//
// func init() {
// // Populate nativeHistogramBounds.
// numBuckets := 1
// for i := range nativeHistogramBounds {
// bounds := []float64{0.5}
// factor := math.Exp2(math.Exp2(float64(-i)))
// for j := 0; j < numBuckets-1; j++ {
// var bound float64
// if (j+1)%2 == 0 {
// // Use previously calculated value for increased precision.
// bound = nativeHistogramBounds[i-1][j/2+1]
// } else {
// bound = bounds[j] * factor
// }
// bounds = append(bounds, bound)
// }
// numBuckets *= 2
// nativeHistogramBounds[i] = bounds
// }
// }
// A Histogram counts individual observations from an event or sample stream in
// configurable static buckets (or in dynamic sparse buckets as part of the
// experimental Native Histograms, see below for more details). Similar to a
// Summary, it also provides a sum of observations and an observation count.
//
// On the Prometheus server, quantiles can be calculated from a Histogram using
// the histogram_quantile PromQL function.
//
// Note that Histograms, in contrast to Summaries, can be aggregated in PromQL
// (see the documentation for detailed procedures). However, Histograms require
// the user to pre-define suitable buckets, and they are in general less
// accurate. (Both problems are addressed by the experimental Native
// Histograms. To use them, configure a NativeHistogramBucketFactor in the
// HistogramOpts. They also require a Prometheus server v2.40+ with the
// corresponding feature flag enabled.)
//
// The Observe method of a Histogram has a very low performance overhead in
// comparison with the Observe method of a Summary.
//
// To create Histogram instances, use NewHistogram.
type Histogram interface {
Metric
Collector
// Observe adds a single observation to the histogram. Observations are
// usually positive or zero. Negative observations are accepted but
// prevent current versions of Prometheus from properly detecting
// counter resets in the sum of observations. (The experimental Native
// Histograms handle negative observations properly.) See
// https://prometheus.io/docs/practices/histograms/#count-and-sum-of-observations
// for details.
Observe(float64)
}
// bucketLabel is used for the label that defines the upper bound of a
// bucket of a histogram ("le" -> "less or equal").
const bucketLabel = "le"
// DefBuckets are the default Histogram buckets. The default buckets are
// tailored to broadly measure the response time (in seconds) of a network
// service. Most likely, however, you will be required to define buckets
// customized to your use case.
var DefBuckets = []float64{.005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5, 10}
// DefNativeHistogramZeroThreshold is the default value for
// NativeHistogramZeroThreshold in the HistogramOpts.
//
// The value is 2^-128 (or 0.5*2^-127 in the actual IEEE 754 representation),
// which is a bucket boundary at all possible resolutions.
const DefNativeHistogramZeroThreshold = 2.938735877055719e-39
// NativeHistogramZeroThresholdZero can be used as NativeHistogramZeroThreshold
// in the HistogramOpts to create a zero bucket of width zero, i.e. a zero
// bucket that only receives observations of precisely zero.
const NativeHistogramZeroThresholdZero = -1
var errBucketLabelNotAllowed = fmt.Errorf(
"%q is not allowed as label name in histograms", bucketLabel,
)
// LinearBuckets creates 'count' regular buckets, each 'width' wide, where the
// lowest bucket has an upper bound of 'start'. The final +Inf bucket is not
// counted and not included in the returned slice. The returned slice is meant
// to be used for the Buckets field of HistogramOpts.
//
// The function panics if 'count' is zero or negative.
func LinearBuckets(start, width float64, count int) []float64 {
if count < 1 {
panic("LinearBuckets needs a positive count")
}
buckets := make([]float64, count)
for i := range buckets {
buckets[i] = start
start += width
}
return buckets
}
// ExponentialBuckets creates 'count' regular buckets, where the lowest bucket
// has an upper bound of 'start' and each following bucket's upper bound is
// 'factor' times the previous bucket's upper bound. The final +Inf bucket is
// not counted and not included in the returned slice. The returned slice is
// meant to be used for the Buckets field of HistogramOpts.
//
// The function panics if 'count' is 0 or negative, if 'start' is 0 or negative,
// or if 'factor' is less than or equal 1.
func ExponentialBuckets(start, factor float64, count int) []float64 {
if count < 1 {
panic("ExponentialBuckets needs a positive count")
}
if start <= 0 {
panic("ExponentialBuckets needs a positive start value")
}
if factor <= 1 {
panic("ExponentialBuckets needs a factor greater than 1")
}
buckets := make([]float64, count)
for i := range buckets {
buckets[i] = start
start *= factor
}
return buckets
}
// ExponentialBucketsRange creates 'count' buckets, where the lowest bucket is
// 'min' and the highest bucket is 'max'. The final +Inf bucket is not counted
// and not included in the returned slice. The returned slice is meant to be
// used for the Buckets field of HistogramOpts.
//
// The function panics if 'count' is 0 or negative, if 'min' is 0 or negative.
func ExponentialBucketsRange(min, max float64, count int) []float64 {
if count < 1 {
panic("ExponentialBucketsRange count needs a positive count")
}
if min <= 0 {
panic("ExponentialBucketsRange min needs to be greater than 0")
}
// Formula for exponential buckets.
// max = min*growthFactor^(bucketCount-1)
// We know max/min and highest bucket. Solve for growthFactor.
growthFactor := math.Pow(max/min, 1.0/float64(count-1))
// Now that we know growthFactor, solve for each bucket.
buckets := make([]float64, count)
for i := 1; i <= count; i++ {
buckets[i-1] = min * math.Pow(growthFactor, float64(i-1))
}
return buckets
}
// HistogramOpts bundles the options for creating a Histogram metric. It is
// mandatory to set Name to a non-empty string. All other fields are optional
// and can safely be left at their zero value, although it is strongly
// encouraged to set a Help string.
type HistogramOpts struct {
// Namespace, Subsystem, and Name are components of the fully-qualified
// name of the Histogram (created by joining these components with
// "_"). Only Name is mandatory, the others merely help structuring the
// name. Note that the fully-qualified name of the Histogram must be a
// valid Prometheus metric name.
Namespace string
Subsystem string
Name string
// Help provides information about this Histogram.
//
// Metrics with the same fully-qualified name must have the same Help
// string.
Help string
// ConstLabels are used to attach fixed labels to this metric. Metrics
// with the same fully-qualified name must have the same label names in
// their ConstLabels.
//
// ConstLabels are only used rarely. In particular, do not use them to
// attach the same labels to all your metrics. Those use cases are
// better covered by target labels set by the scraping Prometheus
// server, or by one specific metric (e.g. a build_info or a
// machine_role metric). See also
// https://prometheus.io/docs/instrumenting/writing_exporters/#target-labels-not-static-scraped-labels
ConstLabels Labels
// Buckets defines the buckets into which observations are counted. Each
// element in the slice is the upper inclusive bound of a bucket. The
// values must be sorted in strictly increasing order. There is no need
// to add a highest bucket with +Inf bound, it will be added
// implicitly. If Buckets is left as nil or set to a slice of length
// zero, it is replaced by default buckets. The default buckets are
// DefBuckets if no buckets for a native histogram (see below) are used,
// otherwise the default is no buckets. (In other words, if you want to
// use both regular buckets and buckets for a native histogram, you have
// to define the regular buckets here explicitly.)
Buckets []float64
// If NativeHistogramBucketFactor is greater than one, so-called sparse
// buckets are used (in addition to the regular buckets, if defined
// above). A Histogram with sparse buckets will be ingested as a Native
// Histogram by a Prometheus server with that feature enabled (requires
// Prometheus v2.40+). Sparse buckets are exponential buckets covering
// the whole float64 range (with the exception of the “zero” bucket, see
// NativeHistogramZeroThreshold below). From any one bucket to the next,
// the width of the bucket grows by a constant
// factor. NativeHistogramBucketFactor provides an upper bound for this
// factor (exception see below). The smaller
// NativeHistogramBucketFactor, the more buckets will be used and thus
// the more costly the histogram will become. A generally good trade-off
// between cost and accuracy is a value of 1.1 (each bucket is at most
// 10% wider than the previous one), which will result in each power of
// two divided into 8 buckets (e.g. there will be 8 buckets between 1
// and 2, same as between 2 and 4, and 4 and 8, etc.).
//
// Details about the actually used factor: The factor is calculated as
// 2^(2^-n), where n is an integer number between (and including) -4 and
// 8. n is chosen so that the resulting factor is the largest that is
// still smaller or equal to NativeHistogramBucketFactor. Note that the
// smallest possible factor is therefore approx. 1.00271 (i.e. 2^(2^-8)
// ). If NativeHistogramBucketFactor is greater than 1 but smaller than
// 2^(2^-8), then the actually used factor is still 2^(2^-8) even though
// it is larger than the provided NativeHistogramBucketFactor.
//
// NOTE: Native Histograms are still an experimental feature. Their
// behavior might still change without a major version
// bump. Subsequently, all NativeHistogram... options here might still
// change their behavior or name (or might completely disappear) without
// a major version bump.
NativeHistogramBucketFactor float64
// All observations with an absolute value of less or equal
// NativeHistogramZeroThreshold are accumulated into a “zero” bucket.
// For best results, this should be close to a bucket boundary. This is
// usually the case if picking a power of two. If
// NativeHistogramZeroThreshold is left at zero,
// DefNativeHistogramZeroThreshold is used as the threshold. To
// configure a zero bucket with an actual threshold of zero (i.e. only
// observations of precisely zero will go into the zero bucket), set
// NativeHistogramZeroThreshold to the NativeHistogramZeroThresholdZero
// constant (or any negative float value).
NativeHistogramZeroThreshold float64
// The next three fields define a strategy to limit the number of
// populated sparse buckets. If NativeHistogramMaxBucketNumber is left
// at zero, the number of buckets is not limited. (Note that this might
// lead to unbounded memory consumption if the values observed by the
// Histogram are sufficiently wide-spread. In particular, this could be
// used as a DoS attack vector. Where the observed values depend on
// external inputs, it is highly recommended to set a
// NativeHistogramMaxBucketNumber.) Once the set
// NativeHistogramMaxBucketNumber is exceeded, the following strategy is
// enacted:
// - First, if the last reset (or the creation) of the histogram is at
// least NativeHistogramMinResetDuration ago, then the whole
// histogram is reset to its initial state (including regular
// buckets).
// - If less time has passed, or if NativeHistogramMinResetDuration is
// zero, no reset is performed. Instead, the zero threshold is
// increased sufficiently to reduce the number of buckets to or below
// NativeHistogramMaxBucketNumber, but not to more than
// NativeHistogramMaxZeroThreshold. Thus, if
// NativeHistogramMaxZeroThreshold is already at or below the current
// zero threshold, nothing happens at this step.
// - After that, if the number of buckets still exceeds
// NativeHistogramMaxBucketNumber, the resolution of the histogram is
// reduced by doubling the width of the sparse buckets (up to a
// growth factor between one bucket to the next of 2^(2^4) = 65536,
// see above).
// - Any increased zero threshold or reduced resolution is reset back
// to their original values once NativeHistogramMinResetDuration has
// passed (since the last reset or the creation of the histogram).
NativeHistogramMaxBucketNumber uint32
NativeHistogramMinResetDuration time.Duration
NativeHistogramMaxZeroThreshold float64
// NativeHistogramMaxExemplars limits the number of exemplars
// that are kept in memory for each native histogram. If you leave it at
// zero, a default value of 10 is used. If no exemplars should be kept specifically
// for native histograms, set it to a negative value. (Scrapers can
// still use the exemplars exposed for classic buckets, which are managed
// independently.)
NativeHistogramMaxExemplars int
// NativeHistogramExemplarTTL is only checked once
// NativeHistogramMaxExemplars is exceeded. In that case, the
// oldest exemplar is removed if it is older than NativeHistogramExemplarTTL.
// Otherwise, the older exemplar in the pair of exemplars that are closest
// together (on an exponential scale) is removed.
// If NativeHistogramExemplarTTL is left at its zero value, a default value of
// 5m is used. To always delete the oldest exemplar, set it to a negative value.
NativeHistogramExemplarTTL time.Duration
// now is for testing purposes, by default it's time.Now.
now func() time.Time
// afterFunc is for testing purposes, by default it's time.AfterFunc.
afterFunc func(time.Duration, func()) *time.Timer
}
// HistogramVecOpts bundles the options to create a HistogramVec metric.
// It is mandatory to set HistogramOpts, see there for mandatory fields. VariableLabels
// is optional and can safely be left to its default value.
type HistogramVecOpts struct {
HistogramOpts
// VariableLabels are used to partition the metric vector by the given set
// of labels. Each label value will be constrained with the optional Constraint
// function, if provided.
VariableLabels ConstrainableLabels
}
// NewHistogram creates a new Histogram based on the provided HistogramOpts. It
// panics if the buckets in HistogramOpts are not in strictly increasing order.
//
// The returned implementation also implements ExemplarObserver. It is safe to
// perform the corresponding type assertion. Exemplars are tracked separately
// for each bucket.
func NewHistogram(opts HistogramOpts) Histogram {
return newHistogram(
NewDesc(
BuildFQName(opts.Namespace, opts.Subsystem, opts.Name),
opts.Help,
nil,
opts.ConstLabels,
),
opts,
)
}
func newHistogram(desc *Desc, opts HistogramOpts, labelValues ...string) Histogram {
if len(desc.variableLabels.names) != len(labelValues) {
panic(makeInconsistentCardinalityError(desc.fqName, desc.variableLabels.names, labelValues))
}
for _, n := range desc.variableLabels.names {
if n == bucketLabel {
panic(errBucketLabelNotAllowed)
}
}
for _, lp := range desc.constLabelPairs {
if lp.GetName() == bucketLabel {
panic(errBucketLabelNotAllowed)
}
}
if opts.now == nil {
opts.now = time.Now
}
if opts.afterFunc == nil {
opts.afterFunc = time.AfterFunc
}
h := &histogram{
desc: desc,
upperBounds: opts.Buckets,
labelPairs: MakeLabelPairs(desc, labelValues),
nativeHistogramMaxBuckets: opts.NativeHistogramMaxBucketNumber,
nativeHistogramMaxZeroThreshold: opts.NativeHistogramMaxZeroThreshold,
nativeHistogramMinResetDuration: opts.NativeHistogramMinResetDuration,
lastResetTime: opts.now(),
now: opts.now,
afterFunc: opts.afterFunc,
}
if len(h.upperBounds) == 0 && opts.NativeHistogramBucketFactor <= 1 {
h.upperBounds = DefBuckets
}
if opts.NativeHistogramBucketFactor <= 1 {
h.nativeHistogramSchema = math.MinInt32 // To mark that there are no sparse buckets.
} else {
switch {
case opts.NativeHistogramZeroThreshold > 0:
h.nativeHistogramZeroThreshold = opts.NativeHistogramZeroThreshold
case opts.NativeHistogramZeroThreshold == 0:
h.nativeHistogramZeroThreshold = DefNativeHistogramZeroThreshold
} // Leave h.nativeHistogramZeroThreshold at 0 otherwise.
h.nativeHistogramSchema = pickSchema(opts.NativeHistogramBucketFactor)
h.nativeExemplars = makeNativeExemplars(opts.NativeHistogramExemplarTTL, opts.NativeHistogramMaxExemplars)
}
for i, upperBound := range h.upperBounds {
if i < len(h.upperBounds)-1 {
if upperBound >= h.upperBounds[i+1] {
panic(fmt.Errorf(
"histogram buckets must be in increasing order: %f >= %f",
upperBound, h.upperBounds[i+1],
))
}
} else {
if math.IsInf(upperBound, +1) {
// The +Inf bucket is implicit. Remove it here.
h.upperBounds = h.upperBounds[:i]
}
}
}
// Finally we know the final length of h.upperBounds and can make buckets
// for both counts as well as exemplars:
h.counts[0] = &histogramCounts{buckets: make([]uint64, len(h.upperBounds))}
atomic.StoreUint64(&h.counts[0].nativeHistogramZeroThresholdBits, math.Float64bits(h.nativeHistogramZeroThreshold))
atomic.StoreInt32(&h.counts[0].nativeHistogramSchema, h.nativeHistogramSchema)
h.counts[1] = &histogramCounts{buckets: make([]uint64, len(h.upperBounds))}
atomic.StoreUint64(&h.counts[1].nativeHistogramZeroThresholdBits, math.Float64bits(h.nativeHistogramZeroThreshold))
atomic.StoreInt32(&h.counts[1].nativeHistogramSchema, h.nativeHistogramSchema)
h.exemplars = make([]atomic.Value, len(h.upperBounds)+1)
h.init(h) // Init self-collection.
return h
}
type histogramCounts struct {
// Order in this struct matters for the alignment required by atomic
// operations, see http://golang.org/pkg/sync/atomic/#pkg-note-BUG
// sumBits contains the bits of the float64 representing the sum of all
// observations.
sumBits uint64
count uint64
// nativeHistogramZeroBucket counts all (positive and negative)
// observations in the zero bucket (with an absolute value less or equal
// the current threshold, see next field.
nativeHistogramZeroBucket uint64
// nativeHistogramZeroThresholdBits is the bit pattern of the current
// threshold for the zero bucket. It's initially equal to
// nativeHistogramZeroThreshold but may change according to the bucket
// count limitation strategy.
nativeHistogramZeroThresholdBits uint64
// nativeHistogramSchema may change over time according to the bucket
// count limitation strategy and therefore has to be saved here.
nativeHistogramSchema int32
// Number of (positive and negative) sparse buckets.
nativeHistogramBucketsNumber uint32
// Regular buckets.
buckets []uint64
// The sparse buckets for native histograms are implemented with a
// sync.Map for now. A dedicated data structure will likely be more
// efficient. There are separate maps for negative and positive
// observations. The map's value is an *int64, counting observations in
// that bucket. (Note that we don't use uint64 as an int64 won't
// overflow in practice, and working with signed numbers from the
// beginning simplifies the handling of deltas.) The map's key is the
// index of the bucket according to the used
// nativeHistogramSchema. Index 0 is for an upper bound of 1.
nativeHistogramBucketsPositive, nativeHistogramBucketsNegative sync.Map
}
// observe manages the parts of observe that only affects
// histogramCounts. doSparse is true if sparse buckets should be done,
// too.
func (hc *histogramCounts) observe(v float64, bucket int, doSparse bool) {
if bucket < len(hc.buckets) {
atomic.AddUint64(&hc.buckets[bucket], 1)
}
atomicAddFloat(&hc.sumBits, v)
if doSparse && !math.IsNaN(v) {
var (
key int
schema = atomic.LoadInt32(&hc.nativeHistogramSchema)
zeroThreshold = math.Float64frombits(atomic.LoadUint64(&hc.nativeHistogramZeroThresholdBits))
bucketCreated, isInf bool
)
if math.IsInf(v, 0) {
// Pretend v is MaxFloat64 but later increment key by one.
if math.IsInf(v, +1) {
v = math.MaxFloat64
} else {
v = -math.MaxFloat64
}
isInf = true
}
frac, exp := math.Frexp(math.Abs(v))
if schema > 0 {
bounds := nativeHistogramBounds[schema]
key = sort.SearchFloat64s(bounds, frac) + (exp-1)*len(bounds)
} else {
key = exp
if frac == 0.5 {
key--
}
offset := (1 << -schema) - 1
key = (key + offset) >> -schema
}
if isInf {
key++
}
switch {
case v > zeroThreshold:
bucketCreated = addToBucket(&hc.nativeHistogramBucketsPositive, key, 1)
case v < -zeroThreshold:
bucketCreated = addToBucket(&hc.nativeHistogramBucketsNegative, key, 1)
default:
atomic.AddUint64(&hc.nativeHistogramZeroBucket, 1)
}
if bucketCreated {
atomic.AddUint32(&hc.nativeHistogramBucketsNumber, 1)
}
}
// Increment count last as we take it as a signal that the observation
// is complete.
atomic.AddUint64(&hc.count, 1)
}
type histogram struct {
// countAndHotIdx enables lock-free writes with use of atomic updates.
// The most significant bit is the hot index [0 or 1] of the count field
// below. Observe calls update the hot one. All remaining bits count the
// number of Observe calls. Observe starts by incrementing this counter,
// and finish by incrementing the count field in the respective
// histogramCounts, as a marker for completion.
//
// Calls of the Write method (which are non-mutating reads from the
// perspective of the histogram) swap the hotcold under the writeMtx
// lock. A cooldown is awaited (while locked) by comparing the number of
// observations with the initiation count. Once they match, then the
// last observation on the now cool one has completed. All cold fields must
// be merged into the new hot before releasing writeMtx.
//
// Fields with atomic access first! See alignment constraint:
// http://golang.org/pkg/sync/atomic/#pkg-note-BUG
countAndHotIdx uint64
selfCollector
desc *Desc
// Only used in the Write method and for sparse bucket management.
mtx sync.Mutex
// Two counts, one is "hot" for lock-free observations, the other is
// "cold" for writing out a dto.Metric. It has to be an array of
// pointers to guarantee 64bit alignment of the histogramCounts, see
// http://golang.org/pkg/sync/atomic/#pkg-note-BUG.
counts [2]*histogramCounts
upperBounds []float64
labelPairs []*dto.LabelPair
exemplars []atomic.Value // One more than buckets (to include +Inf), each a *dto.Exemplar.
nativeHistogramSchema int32 // The initial schema. Set to math.MinInt32 if no sparse buckets are used.
nativeHistogramZeroThreshold float64 // The initial zero threshold.
nativeHistogramMaxZeroThreshold float64
nativeHistogramMaxBuckets uint32
nativeHistogramMinResetDuration time.Duration
// lastResetTime is protected by mtx. It is also used as created timestamp.
lastResetTime time.Time
// resetScheduled is protected by mtx. It is true if a reset is
// scheduled for a later time (when nativeHistogramMinResetDuration has
// passed).
resetScheduled bool
nativeExemplars nativeExemplars
// now is for testing purposes, by default it's time.Now.
now func() time.Time
// afterFunc is for testing purposes, by default it's time.AfterFunc.
afterFunc func(time.Duration, func()) *time.Timer
}
func (h *histogram) Desc() *Desc {
return h.desc
}
func (h *histogram) Observe(v float64) {
h.observe(v, h.findBucket(v))
}
// ObserveWithExemplar should not be called in a high-frequency setting
// for a native histogram with configured exemplars. For this case,
// the implementation isn't lock-free and might suffer from lock contention.
func (h *histogram) ObserveWithExemplar(v float64, e Labels) {
i := h.findBucket(v)
h.observe(v, i)
h.updateExemplar(v, i, e)
}
func (h *histogram) Write(out *dto.Metric) error {
// For simplicity, we protect this whole method by a mutex. It is not in
// the hot path, i.e. Observe is called much more often than Write. The
// complication of making Write lock-free isn't worth it, if possible at
// all.
h.mtx.Lock()
defer h.mtx.Unlock()
// Adding 1<<63 switches the hot index (from 0 to 1 or from 1 to 0)
// without touching the count bits. See the struct comments for a full
// description of the algorithm.
n := atomic.AddUint64(&h.countAndHotIdx, 1<<63)
// count is contained unchanged in the lower 63 bits.
count := n & ((1 << 63) - 1)
// The most significant bit tells us which counts is hot. The complement
// is thus the cold one.
hotCounts := h.counts[n>>63]
coldCounts := h.counts[(^n)>>63]
waitForCooldown(count, coldCounts)
his := &dto.Histogram{
Bucket: make([]*dto.Bucket, len(h.upperBounds)),
SampleCount: proto.Uint64(count),
SampleSum: proto.Float64(math.Float64frombits(atomic.LoadUint64(&coldCounts.sumBits))),
CreatedTimestamp: timestamppb.New(h.lastResetTime),
}
out.Histogram = his
out.Label = h.labelPairs
var cumCount uint64
for i, upperBound := range h.upperBounds {
cumCount += atomic.LoadUint64(&coldCounts.buckets[i])
his.Bucket[i] = &dto.Bucket{
CumulativeCount: proto.Uint64(cumCount),
UpperBound: proto.Float64(upperBound),
}
if e := h.exemplars[i].Load(); e != nil {
his.Bucket[i].Exemplar = e.(*dto.Exemplar)
}
}
// If there is an exemplar for the +Inf bucket, we have to add that bucket explicitly.
if e := h.exemplars[len(h.upperBounds)].Load(); e != nil {
b := &dto.Bucket{
CumulativeCount: proto.Uint64(count),
UpperBound: proto.Float64(math.Inf(1)),
Exemplar: e.(*dto.Exemplar),
}
his.Bucket = append(his.Bucket, b)
}
if h.nativeHistogramSchema > math.MinInt32 {
his.ZeroThreshold = proto.Float64(math.Float64frombits(atomic.LoadUint64(&coldCounts.nativeHistogramZeroThresholdBits)))
his.Schema = proto.Int32(atomic.LoadInt32(&coldCounts.nativeHistogramSchema))
zeroBucket := atomic.LoadUint64(&coldCounts.nativeHistogramZeroBucket)
defer func() {
coldCounts.nativeHistogramBucketsPositive.Range(addAndReset(&hotCounts.nativeHistogramBucketsPositive, &hotCounts.nativeHistogramBucketsNumber))
coldCounts.nativeHistogramBucketsNegative.Range(addAndReset(&hotCounts.nativeHistogramBucketsNegative, &hotCounts.nativeHistogramBucketsNumber))
}()
his.ZeroCount = proto.Uint64(zeroBucket)
his.NegativeSpan, his.NegativeDelta = makeBuckets(&coldCounts.nativeHistogramBucketsNegative)
his.PositiveSpan, his.PositiveDelta = makeBuckets(&coldCounts.nativeHistogramBucketsPositive)
// Add a no-op span to a histogram without observations and with
// a zero threshold of zero. Otherwise, a native histogram would
// look like a classic histogram to scrapers.
if *his.ZeroThreshold == 0 && *his.ZeroCount == 0 && len(his.PositiveSpan) == 0 && len(his.NegativeSpan) == 0 {
his.PositiveSpan = []*dto.BucketSpan{{
Offset: proto.Int32(0),
Length: proto.Uint32(0),
}}
}
// If exemplars are not configured, the cap will be 0.
// So append is not needed in this case.
if cap(h.nativeExemplars.exemplars) > 0 {
h.nativeExemplars.Lock()
his.Exemplars = append(his.Exemplars, h.nativeExemplars.exemplars...)
h.nativeExemplars.Unlock()
}
}
addAndResetCounts(hotCounts, coldCounts)
return nil
}
// findBucket returns the index of the bucket for the provided value, or
// len(h.upperBounds) for the +Inf bucket.
func (h *histogram) findBucket(v float64) int {
// TODO(beorn7): For small numbers of buckets (<30), a linear search is
// slightly faster than the binary search. If we really care, we could
// switch from one search strategy to the other depending on the number
// of buckets.
//
// Microbenchmarks (BenchmarkHistogramNoLabels):
// 11 buckets: 38.3 ns/op linear - binary 48.7 ns/op
// 100 buckets: 78.1 ns/op linear - binary 54.9 ns/op
// 300 buckets: 154 ns/op linear - binary 61.6 ns/op
return sort.SearchFloat64s(h.upperBounds, v)
}
// observe is the implementation for Observe without the findBucket part.
func (h *histogram) observe(v float64, bucket int) {
// Do not add to sparse buckets for NaN observations.
doSparse := h.nativeHistogramSchema > math.MinInt32 && !math.IsNaN(v)
// We increment h.countAndHotIdx so that the counter in the lower
// 63 bits gets incremented. At the same time, we get the new value
// back, which we can use to find the currently-hot counts.
n := atomic.AddUint64(&h.countAndHotIdx, 1)
hotCounts := h.counts[n>>63]
hotCounts.observe(v, bucket, doSparse)
if doSparse {
h.limitBuckets(hotCounts, v, bucket)
}
}
// limitBuckets applies a strategy to limit the number of populated sparse
// buckets. It's generally best effort, and there are situations where the
// number can go higher (if even the lowest resolution isn't enough to reduce
// the number sufficiently, or if the provided counts aren't fully updated yet
// by a concurrently happening Write call).
func (h *histogram) limitBuckets(counts *histogramCounts, value float64, bucket int) {
if h.nativeHistogramMaxBuckets == 0 {
return // No limit configured.
}
if h.nativeHistogramMaxBuckets >= atomic.LoadUint32(&counts.nativeHistogramBucketsNumber) {
return // Bucket limit not exceeded yet.
}
h.mtx.Lock()
defer h.mtx.Unlock()
// The hot counts might have been swapped just before we acquired the
// lock. Re-fetch the hot counts first...
n := atomic.LoadUint64(&h.countAndHotIdx)
hotIdx := n >> 63
coldIdx := (^n) >> 63
hotCounts := h.counts[hotIdx]
coldCounts := h.counts[coldIdx]
// ...and then check again if we really have to reduce the bucket count.
if h.nativeHistogramMaxBuckets >= atomic.LoadUint32(&hotCounts.nativeHistogramBucketsNumber) {
return // Bucket limit not exceeded after all.
}
// Try the various strategies in order.
if h.maybeReset(hotCounts, coldCounts, coldIdx, value, bucket) {
return
}
// One of the other strategies will happen. To undo what they will do as
// soon as enough time has passed to satisfy
// h.nativeHistogramMinResetDuration, schedule a reset at the right time
// if we haven't done so already.
if h.nativeHistogramMinResetDuration > 0 && !h.resetScheduled {
h.resetScheduled = true
h.afterFunc(h.nativeHistogramMinResetDuration-h.now().Sub(h.lastResetTime), h.reset)
}
if h.maybeWidenZeroBucket(hotCounts, coldCounts) {
return
}
h.doubleBucketWidth(hotCounts, coldCounts)
}
// maybeReset resets the whole histogram if at least
// h.nativeHistogramMinResetDuration has been passed. It returns true if the
// histogram has been reset. The caller must have locked h.mtx.
func (h *histogram) maybeReset(
hot, cold *histogramCounts, coldIdx uint64, value float64, bucket int,
) bool {
// We are using the possibly mocked h.now() rather than
// time.Since(h.lastResetTime) to enable testing.
if h.nativeHistogramMinResetDuration == 0 || // No reset configured.
h.resetScheduled || // Do not interefere if a reset is already scheduled.
h.now().Sub(h.lastResetTime) < h.nativeHistogramMinResetDuration {
return false
}
// Completely reset coldCounts.
h.resetCounts(cold)
// Repeat the latest observation to not lose it completely.
cold.observe(value, bucket, true)
// Make coldCounts the new hot counts while resetting countAndHotIdx.
n := atomic.SwapUint64(&h.countAndHotIdx, (coldIdx<<63)+1)
count := n & ((1 << 63) - 1)
waitForCooldown(count, hot)
// Finally, reset the formerly hot counts, too.
h.resetCounts(hot)
h.lastResetTime = h.now()
return true
}
// reset resets the whole histogram. It locks h.mtx itself, i.e. it has to be
// called without having locked h.mtx.
func (h *histogram) reset() {
h.mtx.Lock()
defer h.mtx.Unlock()
n := atomic.LoadUint64(&h.countAndHotIdx)
hotIdx := n >> 63
coldIdx := (^n) >> 63
hot := h.counts[hotIdx]
cold := h.counts[coldIdx]
// Completely reset coldCounts.
h.resetCounts(cold)
// Make coldCounts the new hot counts while resetting countAndHotIdx.
n = atomic.SwapUint64(&h.countAndHotIdx, coldIdx<<63)
count := n & ((1 << 63) - 1)
waitForCooldown(count, hot)
// Finally, reset the formerly hot counts, too.
h.resetCounts(hot)
h.lastResetTime = h.now()
h.resetScheduled = false
}
// maybeWidenZeroBucket widens the zero bucket until it includes the existing
// buckets closest to the zero bucket (which could be two, if an equidistant
// negative and a positive bucket exists, but usually it's only one bucket to be
// merged into the new wider zero bucket). h.nativeHistogramMaxZeroThreshold
// limits how far the zero bucket can be extended, and if that's not enough to
// include an existing bucket, the method returns false. The caller must have
// locked h.mtx.
func (h *histogram) maybeWidenZeroBucket(hot, cold *histogramCounts) bool {
currentZeroThreshold := math.Float64frombits(atomic.LoadUint64(&hot.nativeHistogramZeroThresholdBits))
if currentZeroThreshold >= h.nativeHistogramMaxZeroThreshold {
return false
}
// Find the key of the bucket closest to zero.
smallestKey := findSmallestKey(&hot.nativeHistogramBucketsPositive)
smallestNegativeKey := findSmallestKey(&hot.nativeHistogramBucketsNegative)
if smallestNegativeKey < smallestKey {
smallestKey = smallestNegativeKey
}
if smallestKey == math.MaxInt32 {
return false
}
newZeroThreshold := getLe(smallestKey, atomic.LoadInt32(&hot.nativeHistogramSchema))
if newZeroThreshold > h.nativeHistogramMaxZeroThreshold {
return false // New threshold would exceed the max threshold.
}
atomic.StoreUint64(&cold.nativeHistogramZeroThresholdBits, math.Float64bits(newZeroThreshold))
// Remove applicable buckets.
if _, loaded := cold.nativeHistogramBucketsNegative.LoadAndDelete(smallestKey); loaded {
atomicDecUint32(&cold.nativeHistogramBucketsNumber)
}
if _, loaded := cold.nativeHistogramBucketsPositive.LoadAndDelete(smallestKey); loaded {
atomicDecUint32(&cold.nativeHistogramBucketsNumber)
}
// Make cold counts the new hot counts.
n := atomic.AddUint64(&h.countAndHotIdx, 1<<63)
count := n & ((1 << 63) - 1)
// Swap the pointer names to represent the new roles and make
// the rest less confusing.
hot, cold = cold, hot
waitForCooldown(count, cold)
// Add all the now cold counts to the new hot counts...
addAndResetCounts(hot, cold)
// ...adjust the new zero threshold in the cold counts, too...
atomic.StoreUint64(&cold.nativeHistogramZeroThresholdBits, math.Float64bits(newZeroThreshold))
// ...and then merge the newly deleted buckets into the wider zero
// bucket.
mergeAndDeleteOrAddAndReset := func(hotBuckets, coldBuckets *sync.Map) func(k, v interface{}) bool {
return func(k, v interface{}) bool {
key := k.(int)
bucket := v.(*int64)
if key == smallestKey {
// Merge into hot zero bucket...
atomic.AddUint64(&hot.nativeHistogramZeroBucket, uint64(atomic.LoadInt64(bucket)))
// ...and delete from cold counts.
coldBuckets.Delete(key)
atomicDecUint32(&cold.nativeHistogramBucketsNumber)
} else {
// Add to corresponding hot bucket...
if addToBucket(hotBuckets, key, atomic.LoadInt64(bucket)) {
atomic.AddUint32(&hot.nativeHistogramBucketsNumber, 1)
}
// ...and reset cold bucket.
atomic.StoreInt64(bucket, 0)
}
return true
}
}
cold.nativeHistogramBucketsPositive.Range(mergeAndDeleteOrAddAndReset(&hot.nativeHistogramBucketsPositive, &cold.nativeHistogramBucketsPositive))
cold.nativeHistogramBucketsNegative.Range(mergeAndDeleteOrAddAndReset(&hot.nativeHistogramBucketsNegative, &cold.nativeHistogramBucketsNegative))
return true
}
// doubleBucketWidth doubles the bucket width (by decrementing the schema
// number). Note that very sparse buckets could lead to a low reduction of the
// bucket count (or even no reduction at all). The method does nothing if the
// schema is already -4.
func (h *histogram) doubleBucketWidth(hot, cold *histogramCounts) {
coldSchema := atomic.LoadInt32(&cold.nativeHistogramSchema)
if coldSchema == -4 {
return // Already at lowest resolution.
}
coldSchema--
atomic.StoreInt32(&cold.nativeHistogramSchema, coldSchema)
// Play it simple and just delete all cold buckets.
atomic.StoreUint32(&cold.nativeHistogramBucketsNumber, 0)
deleteSyncMap(&cold.nativeHistogramBucketsNegative)
deleteSyncMap(&cold.nativeHistogramBucketsPositive)
// Make coldCounts the new hot counts.
n := atomic.AddUint64(&h.countAndHotIdx, 1<<63)
count := n & ((1 << 63) - 1)
// Swap the pointer names to represent the new roles and make
// the rest less confusing.
hot, cold = cold, hot
waitForCooldown(count, cold)
// Add all the now cold counts to the new hot counts...
addAndResetCounts(hot, cold)
// ...adjust the schema in the cold counts, too...
atomic.StoreInt32(&cold.nativeHistogramSchema, coldSchema)
// ...and then merge the cold buckets into the wider hot buckets.
merge := func(hotBuckets *sync.Map) func(k, v interface{}) bool {
return func(k, v interface{}) bool {
key := k.(int)
bucket := v.(*int64)
// Adjust key to match the bucket to merge into.
if key > 0 {
key++
}
key /= 2
// Add to corresponding hot bucket.
if addToBucket(hotBuckets, key, atomic.LoadInt64(bucket)) {
atomic.AddUint32(&hot.nativeHistogramBucketsNumber, 1)
}
return true
}
}
cold.nativeHistogramBucketsPositive.Range(merge(&hot.nativeHistogramBucketsPositive))
cold.nativeHistogramBucketsNegative.Range(merge(&hot.nativeHistogramBucketsNegative))
// Play it simple again and just delete all cold buckets.
atomic.StoreUint32(&cold.nativeHistogramBucketsNumber, 0)
deleteSyncMap(&cold.nativeHistogramBucketsNegative)
deleteSyncMap(&cold.nativeHistogramBucketsPositive)
}
func (h *histogram) resetCounts(counts *histogramCounts) {
atomic.StoreUint64(&counts.sumBits, 0)
atomic.StoreUint64(&counts.count, 0)
atomic.StoreUint64(&counts.nativeHistogramZeroBucket, 0)
atomic.StoreUint64(&counts.nativeHistogramZeroThresholdBits, math.Float64bits(h.nativeHistogramZeroThreshold))
atomic.StoreInt32(&counts.nativeHistogramSchema, h.nativeHistogramSchema)
atomic.StoreUint32(&counts.nativeHistogramBucketsNumber, 0)
for i := range h.upperBounds {
atomic.StoreUint64(&counts.buckets[i], 0)
}
deleteSyncMap(&counts.nativeHistogramBucketsNegative)
deleteSyncMap(&counts.nativeHistogramBucketsPositive)
}
// updateExemplar replaces the exemplar for the provided classic bucket.
// With empty labels, it's a no-op. It panics if any of the labels is invalid.
// If histogram is native, the exemplar will be cached into nativeExemplars,
// which has a limit, and will remove one exemplar when limit is reached.
func (h *histogram) updateExemplar(v float64, bucket int, l Labels) {
if l == nil {
return
}
e, err := newExemplar(v, h.now(), l)
if err != nil {
panic(err)
}
h.exemplars[bucket].Store(e)
doSparse := h.nativeHistogramSchema > math.MinInt32 && !math.IsNaN(v)
if doSparse {
h.nativeExemplars.addExemplar(e)
}
}
// HistogramVec is a Collector that bundles a set of Histograms that all share the
// same Desc, but have different values for their variable labels. This is used
// if you want to count the same thing partitioned by various dimensions
// (e.g. HTTP request latencies, partitioned by status code and method). Create
// instances with NewHistogramVec.
type HistogramVec struct {
*MetricVec
}
// NewHistogramVec creates a new HistogramVec based on the provided HistogramOpts and
// partitioned by the given label names.
func NewHistogramVec(opts HistogramOpts, labelNames []string) *HistogramVec {
return V2.NewHistogramVec(HistogramVecOpts{
HistogramOpts: opts,
VariableLabels: UnconstrainedLabels(labelNames),
})
}
// NewHistogramVec creates a new HistogramVec based on the provided HistogramVecOpts.
func (v2) NewHistogramVec(opts HistogramVecOpts) *HistogramVec {
desc := V2.NewDesc(
BuildFQName(opts.Namespace, opts.Subsystem, opts.Name),
opts.Help,
opts.VariableLabels,
opts.ConstLabels,
)
return &HistogramVec{
MetricVec: NewMetricVec(desc, func(lvs ...string) Metric {
return newHistogram(desc, opts.HistogramOpts, lvs...)
}),
}
}
// GetMetricWithLabelValues returns the Histogram for the given slice of label
// values (same order as the variable labels in Desc). If that combination of
// label values is accessed for the first time, a new Histogram is created.
//
// It is possible to call this method without using the returned Histogram to only
// create the new Histogram but leave it at its starting value, a Histogram without
// any observations.
//
// Keeping the Histogram for later use is possible (and should be considered if
// performance is critical), but keep in mind that Reset, DeleteLabelValues and
// Delete can be used to delete the Histogram from the HistogramVec. In that case, the
// Histogram will still exist, but it will not be exported anymore, even if a
// Histogram with the same label values is created later. See also the CounterVec
// example.
//
// An error is returned if the number of label values is not the same as the
// number of variable labels in Desc (minus any curried labels).
//
// Note that for more than one label value, this method is prone to mistakes
// caused by an incorrect order of arguments. Consider GetMetricWith(Labels) as
// an alternative to avoid that type of mistake. For higher label numbers, the
// latter has a much more readable (albeit more verbose) syntax, but it comes
// with a performance overhead (for creating and processing the Labels map).
// See also the GaugeVec example.
func (v *HistogramVec) GetMetricWithLabelValues(lvs ...string) (Observer, error) {
metric, err := v.MetricVec.GetMetricWithLabelValues(lvs...)
if metric != nil {
return metric.(Observer), err
}
return nil, err
}
// GetMetricWith returns the Histogram for the given Labels map (the label names
// must match those of the variable labels in Desc). If that label map is
// accessed for the first time, a new Histogram is created. Implications of
// creating a Histogram without using it and keeping the Histogram for later use
// are the same as for GetMetricWithLabelValues.
//
// An error is returned if the number and names of the Labels are inconsistent
// with those of the variable labels in Desc (minus any curried labels).
//
// This method is used for the same purpose as
// GetMetricWithLabelValues(...string). See there for pros and cons of the two
// methods.
func (v *HistogramVec) GetMetricWith(labels Labels) (Observer, error) {
metric, err := v.MetricVec.GetMetricWith(labels)
if metric != nil {
return metric.(Observer), err
}
return nil, err
}
// WithLabelValues works as GetMetricWithLabelValues, but panics where
// GetMetricWithLabelValues would have returned an error. Not returning an
// error allows shortcuts like
//
// myVec.WithLabelValues("404", "GET").Observe(42.21)
func (v *HistogramVec) WithLabelValues(lvs ...string) Observer {
h, err := v.GetMetricWithLabelValues(lvs...)
if err != nil {
panic(err)
}
return h
}
// With works as GetMetricWith but panics where GetMetricWithLabels would have
// returned an error. Not returning an error allows shortcuts like
//
// myVec.With(prometheus.Labels{"code": "404", "method": "GET"}).Observe(42.21)
func (v *HistogramVec) With(labels Labels) Observer {
h, err := v.GetMetricWith(labels)
if err != nil {
panic(err)
}
return h
}
// CurryWith returns a vector curried with the provided labels, i.e. the
// returned vector has those labels pre-set for all labeled operations performed
// on it. The cardinality of the curried vector is reduced accordingly. The
// order of the remaining labels stays the same (just with the curried labels
// taken out of the sequence which is relevant for the
// (GetMetric)WithLabelValues methods). It is possible to curry a curried
// vector, but only with labels not yet used for currying before.
//
// The metrics contained in the HistogramVec are shared between the curried and
// uncurried vectors. They are just accessed differently. Curried and uncurried
// vectors behave identically in terms of collection. Only one must be
// registered with a given registry (usually the uncurried version). The Reset
// method deletes all metrics, even if called on a curried vector.
func (v *HistogramVec) CurryWith(labels Labels) (ObserverVec, error) {
vec, err := v.MetricVec.CurryWith(labels)
if vec != nil {
return &HistogramVec{vec}, err
}
return nil, err
}
// MustCurryWith works as CurryWith but panics where CurryWith would have
// returned an error.
func (v *HistogramVec) MustCurryWith(labels Labels) ObserverVec {
vec, err := v.CurryWith(labels)
if err != nil {
panic(err)
}
return vec
}
type constHistogram struct {
desc *Desc
count uint64
sum float64
buckets map[float64]uint64
labelPairs []*dto.LabelPair
createdTs *timestamppb.Timestamp
}
func (h *constHistogram) Desc() *Desc {
return h.desc
}
func (h *constHistogram) Write(out *dto.Metric) error {
his := &dto.Histogram{
CreatedTimestamp: h.createdTs,
}
buckets := make([]*dto.Bucket, 0, len(h.buckets))
his.SampleCount = proto.Uint64(h.count)
his.SampleSum = proto.Float64(h.sum)
for upperBound, count := range h.buckets {
buckets = append(buckets, &dto.Bucket{
CumulativeCount: proto.Uint64(count),
UpperBound: proto.Float64(upperBound),
})
}
if len(buckets) > 0 {
sort.Sort(buckSort(buckets))
}
his.Bucket = buckets
out.Histogram = his
out.Label = h.labelPairs
return nil
}
// NewConstHistogram returns a metric representing a Prometheus histogram with
// fixed values for the count, sum, and bucket counts. As those parameters
// cannot be changed, the returned value does not implement the Histogram
// interface (but only the Metric interface). Users of this package will not
// have much use for it in regular operations. However, when implementing custom
// Collectors, it is useful as a throw-away metric that is generated on the fly
// to send it to Prometheus in the Collect method.
//
// buckets is a map of upper bounds to cumulative counts, excluding the +Inf
// bucket. The +Inf bucket is implicit, and its value is equal to the provided count.
//
// NewConstHistogram returns an error if the length of labelValues is not
// consistent with the variable labels in Desc or if Desc is invalid.
func NewConstHistogram(
desc *Desc,
count uint64,
sum float64,
buckets map[float64]uint64,
labelValues ...string,
) (Metric, error) {
if desc.err != nil {
return nil, desc.err
}
if err := validateLabelValues(labelValues, len(desc.variableLabels.names)); err != nil {
return nil, err
}
return &constHistogram{
desc: desc,
count: count,
sum: sum,
buckets: buckets,
labelPairs: MakeLabelPairs(desc, labelValues),
}, nil
}
// MustNewConstHistogram is a version of NewConstHistogram that panics where
// NewConstHistogram would have returned an error.
func MustNewConstHistogram(
desc *Desc,
count uint64,
sum float64,
buckets map[float64]uint64,
labelValues ...string,
) Metric {
m, err := NewConstHistogram(desc, count, sum, buckets, labelValues...)
if err != nil {
panic(err)
}
return m
}
type buckSort []*dto.Bucket
func (s buckSort) Len() int {
return len(s)
}
func (s buckSort) Swap(i, j int) {
s[i], s[j] = s[j], s[i]
}
func (s buckSort) Less(i, j int) bool {
return s[i].GetUpperBound() < s[j].GetUpperBound()
}
// pickSchema returns the largest number n between -4 and 8 such that
// 2^(2^-n) is less or equal the provided bucketFactor.
//
// Special cases:
// - bucketFactor <= 1: panics.
// - bucketFactor < 2^(2^-8) (but > 1): still returns 8.
func pickSchema(bucketFactor float64) int32 {
if bucketFactor <= 1 {
panic(fmt.Errorf("bucketFactor %f is <=1", bucketFactor))
}
floor := math.Floor(math.Log2(math.Log2(bucketFactor)))
switch {
case floor <= -8:
return 8
case floor >= 4:
return -4
default:
return -int32(floor)
}
}
func makeBuckets(buckets *sync.Map) ([]*dto.BucketSpan, []int64) {
var ii []int
buckets.Range(func(k, v interface{}) bool {
ii = append(ii, k.(int))
return true
})
sort.Ints(ii)
if len(ii) == 0 {
return nil, nil
}
var (
spans []*dto.BucketSpan
deltas []int64
prevCount int64
nextI int
)
appendDelta := func(count int64) {
*spans[len(spans)-1].Length++
deltas = append(deltas, count-prevCount)
prevCount = count
}
for n, i := range ii {
v, _ := buckets.Load(i)
count := atomic.LoadInt64(v.(*int64))
// Multiple spans with only small gaps in between are probably
// encoded more efficiently as one larger span with a few empty
// buckets. Needs some research to find the sweet spot. For now,
// we assume that gaps of one or two buckets should not create
// a new span.
iDelta := int32(i - nextI)
if n == 0 || iDelta > 2 {
// We have to create a new span, either because we are
// at the very beginning, or because we have found a gap
// of more than two buckets.
spans = append(spans, &dto.BucketSpan{
Offset: proto.Int32(iDelta),
Length: proto.Uint32(0),
})
} else {
// We have found a small gap (or no gap at all).
// Insert empty buckets as needed.
for j := int32(0); j < iDelta; j++ {
appendDelta(0)
}
}
appendDelta(count)
nextI = i + 1
}
return spans, deltas
}
// addToBucket increments the sparse bucket at key by the provided amount. It
// returns true if a new sparse bucket had to be created for that.
func addToBucket(buckets *sync.Map, key int, increment int64) bool {
if existingBucket, ok := buckets.Load(key); ok {
// Fast path without allocation.
atomic.AddInt64(existingBucket.(*int64), increment)
return false
}
// Bucket doesn't exist yet. Slow path allocating new counter.
newBucket := increment // TODO(beorn7): Check if this is sufficient to not let increment escape.
if actualBucket, loaded := buckets.LoadOrStore(key, &newBucket); loaded {
// The bucket was created concurrently in another goroutine.
// Have to increment after all.
atomic.AddInt64(actualBucket.(*int64), increment)
return false
}
return true
}
// addAndReset returns a function to be used with sync.Map.Range of spare
// buckets in coldCounts. It increments the buckets in the provided hotBuckets
// according to the buckets ranged through. It then resets all buckets ranged
// through to 0 (but leaves them in place so that they don't need to get
// recreated on the next scrape).
func addAndReset(hotBuckets *sync.Map, bucketNumber *uint32) func(k, v interface{}) bool {
return func(k, v interface{}) bool {
bucket := v.(*int64)
if addToBucket(hotBuckets, k.(int), atomic.LoadInt64(bucket)) {
atomic.AddUint32(bucketNumber, 1)
}
atomic.StoreInt64(bucket, 0)
return true
}
}
func deleteSyncMap(m *sync.Map) {
m.Range(func(k, v interface{}) bool {
m.Delete(k)
return true
})
}
func findSmallestKey(m *sync.Map) int {
result := math.MaxInt32
m.Range(func(k, v interface{}) bool {
key := k.(int)
if key < result {
result = key
}
return true
})
return result
}
func getLe(key int, schema int32) float64 {
// Here a bit of context about the behavior for the last bucket counting
// regular numbers (called simply "last bucket" below) and the bucket
// counting observations of ±Inf (called "inf bucket" below, with a key
// one higher than that of the "last bucket"):
//
// If we apply the usual formula to the last bucket, its upper bound
// would be calculated as +Inf. The reason is that the max possible
// regular float64 number (math.MaxFloat64) doesn't coincide with one of
// the calculated bucket boundaries. So the calculated boundary has to
// be larger than math.MaxFloat64, and the only float64 larger than
// math.MaxFloat64 is +Inf. However, we want to count actual
// observations of ±Inf in the inf bucket. Therefore, we have to treat
// the upper bound of the last bucket specially and set it to
// math.MaxFloat64. (The upper bound of the inf bucket, with its key
// being one higher than that of the last bucket, naturally comes out as
// +Inf by the usual formula. So that's fine.)
//
// math.MaxFloat64 has a frac of 0.9999999999999999 and an exp of
// 1024. If there were a float64 number following math.MaxFloat64, it
// would have a frac of 1.0 and an exp of 1024, or equivalently a frac
// of 0.5 and an exp of 1025. However, since frac must be smaller than
// 1, and exp must be smaller than 1025, either representation overflows
// a float64. (Which, in turn, is the reason that math.MaxFloat64 is the
// largest possible float64. Q.E.D.) However, the formula for
// calculating the upper bound from the idx and schema of the last
// bucket results in precisely that. It is either frac=1.0 & exp=1024
// (for schema < 0) or frac=0.5 & exp=1025 (for schema >=0). (This is,
// by the way, a power of two where the exponent itself is a power of
// two, 2¹⁰ in fact, which coinicides with a bucket boundary in all
// schemas.) So these are the special cases we have to catch below.
if schema < 0 {
exp := key << -schema
if exp == 1024 {
// This is the last bucket before the overflow bucket
// (for ±Inf observations). Return math.MaxFloat64 as
// explained above.
return math.MaxFloat64
}
return math.Ldexp(1, exp)
}
fracIdx := key & ((1 << schema) - 1)
frac := nativeHistogramBounds[schema][fracIdx]
exp := (key >> schema) + 1
if frac == 0.5 && exp == 1025 {
// This is the last bucket before the overflow bucket (for ±Inf
// observations). Return math.MaxFloat64 as explained above.
return math.MaxFloat64
}
return math.Ldexp(frac, exp)
}
// waitForCooldown returns after the count field in the provided histogramCounts
// has reached the provided count value.
func waitForCooldown(count uint64, counts *histogramCounts) {
for count != atomic.LoadUint64(&counts.count) {
runtime.Gosched() // Let observations get work done.
}
}
// atomicAddFloat adds the provided float atomically to another float
// represented by the bit pattern the bits pointer is pointing to.
func atomicAddFloat(bits *uint64, v float64) {
for {
loadedBits := atomic.LoadUint64(bits)
newBits := math.Float64bits(math.Float64frombits(loadedBits) + v)
if atomic.CompareAndSwapUint64(bits, loadedBits, newBits) {
break
}
}
}
// atomicDecUint32 atomically decrements the uint32 p points to. See
// https://pkg.go.dev/sync/atomic#AddUint32 to understand how this is done.
func atomicDecUint32(p *uint32) {
atomic.AddUint32(p, ^uint32(0))
}
// addAndResetCounts adds certain fields (count, sum, conventional buckets, zero
// bucket) from the cold counts to the corresponding fields in the hot
// counts. Those fields are then reset to 0 in the cold counts.
func addAndResetCounts(hot, cold *histogramCounts) {
atomic.AddUint64(&hot.count, atomic.LoadUint64(&cold.count))
atomic.StoreUint64(&cold.count, 0)
coldSum := math.Float64frombits(atomic.LoadUint64(&cold.sumBits))
atomicAddFloat(&hot.sumBits, coldSum)
atomic.StoreUint64(&cold.sumBits, 0)
for i := range hot.buckets {
atomic.AddUint64(&hot.buckets[i], atomic.LoadUint64(&cold.buckets[i]))
atomic.StoreUint64(&cold.buckets[i], 0)
}
atomic.AddUint64(&hot.nativeHistogramZeroBucket, atomic.LoadUint64(&cold.nativeHistogramZeroBucket))
atomic.StoreUint64(&cold.nativeHistogramZeroBucket, 0)
}
type nativeExemplars struct {
sync.Mutex
ttl time.Duration
exemplars []*dto.Exemplar
}
func makeNativeExemplars(ttl time.Duration, maxCount int) nativeExemplars {
if ttl == 0 {
ttl = 5 * time.Minute
}
if maxCount == 0 {
maxCount = 10
}
if maxCount < 0 {
maxCount = 0
}
return nativeExemplars{
ttl: ttl,
exemplars: make([]*dto.Exemplar, 0, maxCount),
}
}
func (n *nativeExemplars) addExemplar(e *dto.Exemplar) {
if cap(n.exemplars) == 0 {
return
}
n.Lock()
defer n.Unlock()
// The index where to insert the new exemplar.
var nIdx int = -1
// When the number of exemplars has not yet exceeded or
// is equal to cap(n.exemplars), then
// insert the new exemplar directly.
if len(n.exemplars) < cap(n.exemplars) {
for nIdx = 0; nIdx < len(n.exemplars); nIdx++ {
if *e.Value < *n.exemplars[nIdx].Value {
break
}
}
n.exemplars = append(n.exemplars[:nIdx], append([]*dto.Exemplar{e}, n.exemplars[nIdx:]...)...)
return
}
// When the number of exemplars exceeds the limit, remove one exemplar.
var (
rIdx int // The index where to remove the old exemplar.
ot = time.Now() // Oldest timestamp seen.
otIdx = -1 // Index of the exemplar with the oldest timestamp.
md = -1.0 // Logarithm of the delta of the closest pair of exemplars.
mdIdx = -1 // Index of the older exemplar within the closest pair.
cLog float64 // Logarithm of the current exemplar.
pLog float64 // Logarithm of the previous exemplar.
)
for i, exemplar := range n.exemplars {
// Find the exemplar with the oldest timestamp.
if otIdx == -1 || exemplar.Timestamp.AsTime().Before(ot) {
ot = exemplar.Timestamp.AsTime()
otIdx = i
}
// Find the index at which to insert new the exemplar.
if *e.Value <= *exemplar.Value && nIdx == -1 {
nIdx = i
}
// Find the two closest exemplars and pick the one the with older timestamp.
pLog = cLog
cLog = math.Log(exemplar.GetValue())
if i == 0 {
continue
}
diff := math.Abs(cLog - pLog)
if md == -1 || diff < md {
md = diff
if n.exemplars[i].Timestamp.AsTime().Before(n.exemplars[i-1].Timestamp.AsTime()) {
mdIdx = i
} else {
mdIdx = i - 1
}
}
}
// If all existing exemplar are smaller than new exemplar,
// then the exemplar should be inserted at the end.
if nIdx == -1 {
nIdx = len(n.exemplars)
}
if otIdx != -1 && e.Timestamp.AsTime().Sub(ot) > n.ttl {
rIdx = otIdx
} else {
// In the previous for loop, when calculating the closest pair of exemplars,
// we did not take into account the newly inserted exemplar.
// So we need to calculate with the newly inserted exemplar again.
elog := math.Log(e.GetValue())
if nIdx > 0 {
diff := math.Abs(elog - math.Log(n.exemplars[nIdx-1].GetValue()))
if diff < md {
md = diff
mdIdx = nIdx
if n.exemplars[nIdx-1].Timestamp.AsTime().Before(e.Timestamp.AsTime()) {
mdIdx = nIdx - 1
}
}
}
if nIdx < len(n.exemplars) {
diff := math.Abs(math.Log(n.exemplars[nIdx].GetValue()) - elog)
if diff < md {
mdIdx = nIdx
if n.exemplars[nIdx].Timestamp.AsTime().Before(e.Timestamp.AsTime()) {
mdIdx = nIdx
}
}
}
rIdx = mdIdx
}
// Adjust the slice according to rIdx and nIdx.
switch {
case rIdx == nIdx:
n.exemplars[nIdx] = e
case rIdx < nIdx:
n.exemplars = append(n.exemplars[:rIdx], append(n.exemplars[rIdx+1:nIdx], append([]*dto.Exemplar{e}, n.exemplars[nIdx:]...)...)...)
case rIdx > nIdx:
n.exemplars = append(n.exemplars[:nIdx], append([]*dto.Exemplar{e}, append(n.exemplars[nIdx:rIdx], n.exemplars[rIdx+1:]...)...)...)
}
}