Permalink
Find file Copy path
615 lines (554 sloc) 21 KB
// 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"
"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"
// 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.
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{&histogramCounts{}, &histogramCounts{}},
}
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:
h.counts[0].buckets = make([]uint64, len(h.upperBounds))
h.counts[1].buckets = make([]uint64, len(h.upperBounds))
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 is a complicated one. For lock-free yet atomic
// observations, we need to save the total count of observations again,
// combined with the index of the currently-hot counts struct, so that
// we can perform the operation on both values atomically. The least
// significant bit defines the hot counts struct. The remaining 63 bits
// represent the total count of observations. This happens under the
// assumption that the 63bit count will never overflow. Rationale: An
// observations takes about 30ns. Let's assume it could happen in
// 10ns. Overflowing the counter will then take at least (2^63)*10ns,
// which is about 3000 years.
//
// This has to be first in the struct for 64bit alignment. See
// http://golang.org/pkg/sync/atomic/#pkg-note-BUG
countAndHotIdx uint64
selfCollector
desc *Desc
writeMtx sync.Mutex // Only used in the Write method.
upperBounds []float64
// 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
hotIdx int // Index of currently-hot counts. Only used within Write.
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)
// We increment h.countAndHotIdx by 2 so that the counter in the upper
// 63 bits gets incremented by 1. 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, 2)
hotCounts := h.counts[n%2]
if i < len(h.upperBounds) {
atomic.AddUint64(&hotCounts.buckets[i], 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)
}
func (h *histogram) Write(out *dto.Metric) error {
var (
his = &dto.Histogram{}
buckets = make([]*dto.Bucket, len(h.upperBounds))
hotCounts, coldCounts *histogramCounts
count uint64
)
// For simplicity, we mutex the rest of this method. 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.
h.writeMtx.Lock()
defer h.writeMtx.Unlock()
// This is a bit arcane, which is why the following spells out this if
// clause in English:
//
// If the currently-hot counts struct is #0, we atomically increment
// h.countAndHotIdx by 1 so that from now on Observe will use the counts
// struct #1. Furthermore, the atomic increment gives us the new value,
// which, in its most significant 63 bits, tells us the count of
// observations done so far up to and including currently ongoing
// observations still using the counts struct just changed from hot to
// cold. To have a normal uint64 for the count, we bitshift by 1 and
// save the result in count. We also set h.hotIdx to 1 for the next
// Write call, and we will refer to counts #1 as hotCounts and to counts
// #0 as coldCounts.
//
// If the currently-hot counts struct is #1, we do the corresponding
// things the other way round. We have to _decrement_ h.countAndHotIdx
// (which is a bit arcane in itself, as we have to express -1 with an
// unsigned int...).
if h.hotIdx == 0 {
count = atomic.AddUint64(&h.countAndHotIdx, 1) >> 1
h.hotIdx = 1
hotCounts = h.counts[1]
coldCounts = h.counts[0]
} else {
count = atomic.AddUint64(&h.countAndHotIdx, ^uint64(0)) >> 1 // Decrement.
h.hotIdx = 0
hotCounts = h.counts[0]
coldCounts = h.counts[1]
}
// Now we have to wait for the now-declared-cold counts to actually cool
// down, i.e. wait for all observations still using it to finish. That's
// the case once the count in the cold counts struct is the same as the
// one atomically retrieved from the upper 63bits of h.countAndHotIdx.
for {
if count == atomic.LoadUint64(&coldCounts.count) {
break
}
runtime.Gosched() // Let observations get work done.
}
his.SampleCount = proto.Uint64(count)
his.SampleSum = proto.Float64(math.Float64frombits(atomic.LoadUint64(&coldCounts.sumBits)))
var cumCount uint64
for i, upperBound := range h.upperBounds {
cumCount += atomic.LoadUint64(&coldCounts.buckets[i])
buckets[i] = &dto.Bucket{
CumulativeCount: proto.Uint64(cumCount),
UpperBound: proto.Float64(upperBound),
}
}
his.Bucket = buckets
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
}
// 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 VariableLabels 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 VariableLabels 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 VariableLabels 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 VariableLabels 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.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
// 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()
}