// 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"
"sort"
"sync/atomic"
"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.
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"
var (
// 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.
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 and Help to a non-empty string. All other fields are
// optional and can safely be left at their zero value.
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. Mandatory!
//
// Metrics with the same fully-qualified name must have the same Help
// string.
Help string
// ConstLabels are used to attach fixed labels to this
// Histogram. Histograms with the same fully-qualified name must have the
// same label names in their ConstLabels.
//
// Note that in most cases, labels have a value that varies during the
// lifetime of a process. Those labels are usually managed with a
// HistogramVec. ConstLabels serve only special purposes. One is for the
// special case where the value of a label does not change during the
// lifetime of a process, e.g. if the revision of the running binary is
// put into a label. Another, more advanced purpose is if more than one
// Collector needs to collect Histograms with the same fully-qualified
// name. In that case, those Summaries must differ in the values of
// their ConstLabels. See the Collector examples.
//
// If the value of a label never changes (not even between binaries),
// that label most likely should not be a label at all (but part of the
// metric name).
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.
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(errInconsistentCardinality)
}
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),
}
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 counts.
h.counts = make([]uint64, len(h.upperBounds))
h.Init(h) // Init self-collection.
return h
}
type histogram 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
SelfCollector
// Note that there is no mutex required.
desc *Desc
upperBounds []float64
counts []uint64
labelPairs []*dto.LabelPair
}
func (h *histogram) Desc() *Desc {
return h.desc
}
func (h *histogram) Observe(v float64) {
// 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
i := sort.SearchFloat64s(h.upperBounds, v)
if i < len(h.counts) {
atomic.AddUint64(&h.counts[i], 1)
}
atomic.AddUint64(&h.count, 1)
for {
oldBits := atomic.LoadUint64(&h.sumBits)
newBits := math.Float64bits(math.Float64frombits(oldBits) + v)
if atomic.CompareAndSwapUint64(&h.sumBits, oldBits, newBits) {
break
}
}
}
func (h *histogram) Write(out *dto.Metric) error {
his := &dto.Histogram{}
buckets := make([]*dto.Bucket, len(h.upperBounds))
his.SampleSum = proto.Float64(math.Float64frombits(atomic.LoadUint64(&h.sumBits)))
his.SampleCount = proto.Uint64(atomic.LoadUint64(&h.count))
var count uint64
for i, upperBound := range h.upperBounds {
count += atomic.LoadUint64(&h.counts[i])
buckets[i] = &dto.Bucket{
CumulativeCount: proto.Uint64(count),
UpperBound: proto.Float64(upperBound),
}
}
his.Bucket = buckets
out.Histogram = his
out.Label = h.labelPairs
return nil
}
// 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. At least one label name must be
// provided.
func NewHistogramVec(opts HistogramOpts, labelNames []string) *HistogramVec {
desc := NewDesc(
BuildFQName(opts.Namespace, opts.Subsystem, opts.Name),
opts.Help,
labelNames,
opts.ConstLabels,
)
return &HistogramVec{
MetricVec: MetricVec{
children: map[uint64]Metric{},
desc: desc,
newMetric: func(lvs ...string) Metric {
return newHistogram(desc, opts, lvs...)
},
},
}
}
// GetMetricWithLabelValues replaces the method of the same name in
// MetricVec. The difference is that this method returns a Histogram and not a
// Metric so that no type conversion is required.
func (m *HistogramVec) GetMetricWithLabelValues(lvs ...string) (Histogram, error) {
metric, err := m.MetricVec.GetMetricWithLabelValues(lvs...)
if metric != nil {
return metric.(Histogram), err
}
return nil, err
}
// GetMetricWith replaces the method of the same name in MetricVec. The
// difference is that this method returns a Histogram and not a Metric so that no
// type conversion is required.
func (m *HistogramVec) GetMetricWith(labels Labels) (Histogram, error) {
metric, err := m.MetricVec.GetMetricWith(labels)
if metric != nil {
return metric.(Histogram), err
}
return nil, err
}
// WithLabelValues works as GetMetricWithLabelValues, but panics where
// GetMetricWithLabelValues would have returned an error. By not returning an
// error, WithLabelValues allows shortcuts like
// myVec.WithLabelValues("404", "GET").Observe(42.21)
func (m *HistogramVec) WithLabelValues(lvs ...string) Histogram {
return m.MetricVec.WithLabelValues(lvs...).(Histogram)
}
// With works as GetMetricWith, but panics where GetMetricWithLabels would have
// returned an error. By not returning an error, With allows shortcuts like
// myVec.With(Labels{"code": "404", "method": "GET"}).Observe(42.21)
func (m *HistogramVec) With(labels Labels) Histogram {
return m.MetricVec.With(labels).(Histogram)
}
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.
func NewConstHistogram(
desc *Desc,
count uint64,
sum float64,
buckets map[float64]uint64,
labelValues ...string,
) (Metric, error) {
if len(desc.variableLabels) != len(labelValues) {
return nil, errInconsistentCardinality
}
return &constHistogram{
desc: desc,
count: count,
sum: sum,
buckets: buckets,
labelPairs: makeLabelPairs(desc, labelValues),
}, nil
}
// MustNewConstHistogram is a version of NewConstHistogram that panics where
// NewConstMetric 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()
}