clone from danieldin95

vendor/github.com/prometheus/client_golang/prometheus/histogram.go

@@ -0,0 +1,642 @@
+// 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"
+
+	//nolint:staticcheck // Ignore SA1019. Need to keep deprecated package for compatibility.
+	"github.com/golang/protobuf/proto"
+
+	dto "github.com/prometheus/client_model/go"
+)
+
+// A Histogram counts individual observations from an event or sample stream in
+// configurable buckets. 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 function in the query language.
+//
+// Note that Histograms, in contrast to Summaries, can be aggregated with the
+// Prometheus query language (see the documentation for detailed
+// procedures). However, Histograms require the user to pre-define suitable
+// buckets, and they are in general less accurate. 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. 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}
+
+	errBucketLabelNotAllowed = fmt.Errorf(
+		"%q is not allowed as label name in histograms", bucketLabel,
+	)
+)
+
+// LinearBuckets creates 'count' 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' 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
+}
+
+// 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. The default value is DefBuckets.
+	Buckets []float64
+}
+
+// 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, labelValues))
+	}
+
+	for _, n := range desc.variableLabels {
+		if n == bucketLabel {
+			panic(errBucketLabelNotAllowed)
+		}
+	}
+	for _, lp := range desc.constLabelPairs {
+		if lp.GetName() == bucketLabel {
+			panic(errBucketLabelNotAllowed)
+		}
+	}
+
+	if len(opts.Buckets) == 0 {
+		opts.Buckets = DefBuckets
+	}
+
+	h := &histogram{
+		desc:        desc,
+		upperBounds: opts.Buckets,
+		labelPairs:  MakeLabelPairs(desc, labelValues),
+		counts:      [2]*histogramCounts{{}, {}},
+		now:         time.Now,
+	}
+	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].buckets = make([]uint64, len(h.upperBounds))
+	h.counts[1].buckets = make([]uint64, len(h.upperBounds))
+	h.exemplars = make([]atomic.Value, len(h.upperBounds)+1)
+
+	h.init(h) // Init self-collection.
+	return h
+}
+
+type histogramCounts struct {
+	// sumBits contains the bits of the float64 representing the sum of all
+	// observations. sumBits and count have to go first in the struct to
+	// guarantee alignment for atomic operations.
+	// http://golang.org/pkg/sync/atomic/#pkg-note-BUG
+	sumBits uint64
+	count   uint64
+	buckets []uint64
+}
+
+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 cool 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
+	writeMtx sync.Mutex // Only used in the Write method.
+
+	// 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.
+
+	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.writeMtx.Lock()
+	defer h.writeMtx.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]
+
+	// Await cooldown.
+	for count != atomic.LoadUint64(&coldCounts.count) {
+		runtime.Gosched() // Let observations get work done.
+	}
+
+	his := &dto.Histogram{
+		Bucket:      make([]*dto.Bucket, len(h.upperBounds)),
+		SampleCount: proto.Uint64(count),
+		SampleSum:   proto.Float64(math.Float64frombits(atomic.LoadUint64(&coldCounts.sumBits))),
+	}
+	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)
+	}
+
+	out.Histogram = his
+	out.Label = h.labelPairs
+
+	// Finally add all the cold counts to the new hot counts and reset the cold counts.
+	atomic.AddUint64(&hotCounts.count, count)
+	atomic.StoreUint64(&coldCounts.count, 0)
+	for {
+		oldBits := atomic.LoadUint64(&hotCounts.sumBits)
+		newBits := math.Float64bits(math.Float64frombits(oldBits) + his.GetSampleSum())
+		if atomic.CompareAndSwapUint64(&hotCounts.sumBits, oldBits, newBits) {
+			atomic.StoreUint64(&coldCounts.sumBits, 0)
+			break
+		}
+	}
+	for i := range h.upperBounds {
+		atomic.AddUint64(&hotCounts.buckets[i], atomic.LoadUint64(&coldCounts.buckets[i]))
+		atomic.StoreUint64(&coldCounts.buckets[i], 0)
+	}
+	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) {
+	// 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]
+
+	if bucket < len(h.upperBounds) {
+		atomic.AddUint64(&hotCounts.buckets[bucket], 1)
+	}
+	for {
+		oldBits := atomic.LoadUint64(&hotCounts.sumBits)
+		newBits := math.Float64bits(math.Float64frombits(oldBits) + v)
+		if atomic.CompareAndSwapUint64(&hotCounts.sumBits, oldBits, newBits) {
+			break
+		}
+	}
+	// Increment count last as we take it as a signal that the observation
+	// is complete.
+	atomic.AddUint64(&hotCounts.count, 1)
+}
+
+// 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 {
+	desc := NewDesc(
+		BuildFQName(opts.Namespace, opts.Subsystem, opts.Name),
+		opts.Help,
+		labelNames,
+		opts.ConstLabels,
+	)
+	return &HistogramVec{
+		MetricVec: NewMetricVec(desc, func(lvs ...string) Metric {
+			return newHistogram(desc, opts, 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.
+//
+// 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()
+}