Tizen_4.0 base

[platform/upstream/docker-engine.git] / vendor / github.com / prometheus / client_golang / prometheus / 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"
        "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()
}