Viewing: histogram.go
// 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"
)
// 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 reguler 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
// SparseBucketsZeroThreshold 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) -8 and
// 4. 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,
// DefSparseBucketsZeroThreshold 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 remaining 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).
NativeHistogramMaxBucketNumber uint32
NativeHistogramMinResetDuration time.Duration
NativeHistogramMaxZeroThreshold float64
}
// 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 Contraint
// 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) != len(labelValues) {
panic(makeInconsistentCardinalityError(desc.fqName, desc.variableLabels.labelNames(), labelValues))
}
for _, n := range desc.variableLabels {
if n.Name == bucketLabel {
panic(errBucketLabelNotAllowed)
}
}
for _, lp := range desc.constLabelPairs {
if lp.GetName() == bucketLabel {
panic(errBucketLabelNotAllowed)
}
}
h := &histogram{
desc: desc,
upperBounds: opts.Buckets,
labelPairs: MakeLabelPairs(desc, labelValues),
nativeHistogramMaxBuckets: opts.NativeHistogramMaxBucketNumber,
nativeHistogramMaxZeroThreshold: opts.NativeHistogramMaxZeroThreshold,
nativeHistogramMinResetDuration: opts.NativeHistogramMinResetDuration,
lastResetTime: time.Now(),
now: time.Now,
}
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)
}
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--
}
div := 1 << -schema
key = (key + div - 1) / div
}
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 hot–cold 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 time.Time // Protected by mtx.
now func() time.Time // To mock out time.Now() for testing.
}
func (h *histogram) Desc() *Desc {
return h.desc
}
func (h *histogram) Observe(v float64) {
h.observe(v, h.findBucket(v))
}
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))),
}
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)
}
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)
}
}
// limitSparsebuckets 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
}
if h.maybeWidenZeroBucket(hotCounts, coldCounts) {
return
}
h.doubleBucketWidth(hotCounts, coldCounts)
}
// maybeReset resests 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 || 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 ressetting 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
}
// 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 bucket. With empty
// labels, it's a no-op. It panics if any of the labels is invalid.
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)
}
// 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
}
func (h *constHistogram) Desc() *Desc {
return h.desc
}
func (h *constHistogram) Write(out *dto.Metric) error {
his := &dto.Histogram{}
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)); 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 ore 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)
}
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