mirror of https://github.com/prometheus/prometheus
beorn7
10 years ago
parent
136cf661b7
commit
a4f6edf496
344
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/histogram.go
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vendored
344
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/histogram.go
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// Copyright 2015 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package prometheus
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import (
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"fmt"
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"hash/fnv"
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"math"
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"sort"
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"sync/atomic"
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"github.com/golang/protobuf/proto"
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"github.com/prometheus/client_golang/model"
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dto "github.com/prometheus/client_model/go"
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)
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// A Histogram counts individual observations from an event or sample stream in
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// configurable buckets. Similar to a summary, it also provides a sum of
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// observations and an observation count.
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//
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// On the Prometheus server, quantiles can be calculated from a Histogram using
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// the histogram_quantile function in the query language.
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//
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// Note that Histograms, in contrast to Summaries, can be aggregated with the
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// Prometheus query language (see the documentation for detailed
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// procedures). However, Histograms require the user to pre-define suitable
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// buckets, and they are in general less accurate. The Observe method of a
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// Histogram has a very low performance overhead in comparison with the Observe
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// method of a Summary.
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//
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// To create Histogram instances, use NewHistogram.
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type Histogram interface {
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Metric
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Collector
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// Observe adds a single observation to the histogram.
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Observe(float64)
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}
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var (
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// DefBuckets are the default Histogram buckets. The default buckets are
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// tailored to broadly measure the response time (in seconds) of a
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// network service. Most likely, however, you will be required to define
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// buckets customized to your use case.
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DefBuckets = []float64{.005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5, 10}
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errBucketLabelNotAllowed = fmt.Errorf(
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"%q is not allowed as label name in histograms", model.BucketLabel,
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)
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)
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// LinearBuckets creates 'count' buckets, each 'width' wide, where the lowest
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// bucket has an upper bound of 'start'. The final +Inf bucket is not counted
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// and not included in the returned slice. The returned slice is meant to be
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// used for the Buckets field of HistogramOpts.
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//
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// The function panics if 'count' is zero or negative.
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func LinearBuckets(start, width float64, count int) []float64 {
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if count < 1 {
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panic("LinearBuckets needs a positive count")
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}
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buckets := make([]float64, count)
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for i := range buckets {
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buckets[i] = start
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start += width
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}
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return buckets
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}
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// ExponentialBuckets creates 'count' buckets, where the lowest bucket has an
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// upper bound of 'start' and each following bucket's upper bound is 'factor'
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// times the previous bucket's upper bound. The final +Inf bucket is not counted
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// and not included in the returned slice. The returned slice is meant to be
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// used for the Buckets field of HistogramOpts.
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//
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// The function panics if 'count' is 0 or negative, if 'start' is 0 or negative,
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// or if 'factor' is less than or equal 1.
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func ExponentialBuckets(start, factor float64, count int) []float64 {
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if count < 1 {
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panic("ExponentialBuckets needs a positive count")
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}
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if start <= 0 {
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panic("ExponentialBuckets needs a positive start value")
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}
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if factor <= 1 {
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panic("ExponentialBuckets needs a factor greater than 1")
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}
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buckets := make([]float64, count)
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for i := range buckets {
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buckets[i] = start
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start *= factor
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}
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return buckets
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}
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// HistogramOpts bundles the options for creating a Histogram metric. It is
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// mandatory to set Name and Help to a non-empty string. All other fields are
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// optional and can safely be left at their zero value.
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type HistogramOpts struct {
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// Namespace, Subsystem, and Name are components of the fully-qualified
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// name of the Histogram (created by joining these components with
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// "_"). Only Name is mandatory, the others merely help structuring the
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// name. Note that the fully-qualified name of the Histogram must be a
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// valid Prometheus metric name.
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Namespace string
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Subsystem string
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Name string
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// Help provides information about this Histogram. Mandatory!
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//
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// Metrics with the same fully-qualified name must have the same Help
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// string.
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Help string
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// ConstLabels are used to attach fixed labels to this
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// Histogram. Histograms with the same fully-qualified name must have the
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// same label names in their ConstLabels.
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//
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// Note that in most cases, labels have a value that varies during the
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// lifetime of a process. Those labels are usually managed with a
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// HistogramVec. ConstLabels serve only special purposes. One is for the
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// special case where the value of a label does not change during the
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// lifetime of a process, e.g. if the revision of the running binary is
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// put into a label. Another, more advanced purpose is if more than one
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// Collector needs to collect Histograms with the same fully-qualified
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// name. In that case, those Summaries must differ in the values of
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// their ConstLabels. See the Collector examples.
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//
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// If the value of a label never changes (not even between binaries),
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// that label most likely should not be a label at all (but part of the
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// metric name).
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ConstLabels Labels
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// Buckets defines the buckets into which observations are counted. Each
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// element in the slice is the upper inclusive bound of a bucket. The
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// values must be sorted in strictly increasing order. There is no need
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// to add a highest bucket with +Inf bound, it will be added
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// implicitly. The default value is DefObjectives.
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Buckets []float64
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}
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// NewHistogram creates a new Histogram based on the provided HistogramOpts. It
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// panics if the buckets in HistogramOpts are not in strictly increasing order.
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func NewHistogram(opts HistogramOpts) Histogram {
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return newHistogram(
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NewDesc(
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BuildFQName(opts.Namespace, opts.Subsystem, opts.Name),
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opts.Help,
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nil,
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opts.ConstLabels,
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),
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opts,
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)
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}
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func newHistogram(desc *Desc, opts HistogramOpts, labelValues ...string) Histogram {
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if len(desc.variableLabels) != len(labelValues) {
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panic(errInconsistentCardinality)
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}
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for _, n := range desc.variableLabels {
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if n == model.BucketLabel {
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panic(errBucketLabelNotAllowed)
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}
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}
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for _, lp := range desc.constLabelPairs {
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if lp.GetName() == model.BucketLabel {
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panic(errBucketLabelNotAllowed)
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}
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}
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if len(opts.Buckets) == 0 {
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opts.Buckets = DefBuckets
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}
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h := &histogram{
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desc: desc,
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upperBounds: opts.Buckets,
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labelPairs: makeLabelPairs(desc, labelValues),
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}
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for i, upperBound := range h.upperBounds {
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if i < len(h.upperBounds)-1 {
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if upperBound >= h.upperBounds[i+1] {
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panic(fmt.Errorf(
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"histogram buckets must be in increasing order: %f >= %f",
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upperBound, h.upperBounds[i+1],
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))
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}
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} else {
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if math.IsInf(upperBound, +1) {
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// The +Inf bucket is implicit. Remove it here.
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h.upperBounds = h.upperBounds[:i]
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}
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}
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}
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// Finally we know the final length of h.upperBounds and can make counts.
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h.counts = make([]uint64, len(h.upperBounds))
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h.Init(h) // Init self-collection.
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return h
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}
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type histogram struct {
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SelfCollector
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// Note that there is no mutex required.
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desc *Desc
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upperBounds []float64
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counts []uint64
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labelPairs []*dto.LabelPair
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sumBits uint64 // The bits of the float64 representing the sum of all observations.
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count uint64
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}
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func (h *histogram) Desc() *Desc {
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return h.desc
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}
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func (h *histogram) Observe(v float64) {
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// TODO(beorn7): For small numbers of buckets (<30), a linear search is
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// slightly faster than the binary search. If we really care, we could
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// switch from one search strategy to the other depending on the number
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// of buckets.
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//
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// Microbenchmarks (BenchmarkHistogramNoLabels):
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// 11 buckets: 38.3 ns/op linear - binary 48.7 ns/op
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// 100 buckets: 78.1 ns/op linear - binary 54.9 ns/op
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// 300 buckets: 154 ns/op linear - binary 61.6 ns/op
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i := sort.SearchFloat64s(h.upperBounds, v)
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if i < len(h.counts) {
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atomic.AddUint64(&h.counts[i], 1)
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}
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atomic.AddUint64(&h.count, 1)
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for {
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oldBits := atomic.LoadUint64(&h.sumBits)
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newBits := math.Float64bits(math.Float64frombits(oldBits) + v)
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if atomic.CompareAndSwapUint64(&h.sumBits, oldBits, newBits) {
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break
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}
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}
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}
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func (h *histogram) Write(out *dto.Metric) error {
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his := &dto.Histogram{}
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buckets := make([]*dto.Bucket, len(h.upperBounds))
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his.SampleSum = proto.Float64(math.Float64frombits(atomic.LoadUint64(&h.sumBits)))
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his.SampleCount = proto.Uint64(atomic.LoadUint64(&h.count))
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var count uint64
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for i, upperBound := range h.upperBounds {
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count += atomic.LoadUint64(&h.counts[i])
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buckets[i] = &dto.Bucket{
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CumulativeCount: proto.Uint64(count),
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UpperBound: proto.Float64(upperBound),
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}
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}
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his.Bucket = buckets
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out.Histogram = his
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out.Label = h.labelPairs
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return nil
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}
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// HistogramVec is a Collector that bundles a set of Histograms that all share the
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// same Desc, but have different values for their variable labels. This is used
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// if you want to count the same thing partitioned by various dimensions
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// (e.g. HTTP request latencies, partitioned by status code and method). Create
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// instances with NewHistogramVec.
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type HistogramVec struct {
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MetricVec
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}
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// NewHistogramVec creates a new HistogramVec based on the provided HistogramOpts and
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// partitioned by the given label names. At least one label name must be
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// provided.
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func NewHistogramVec(opts HistogramOpts, labelNames []string) *HistogramVec {
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desc := NewDesc(
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BuildFQName(opts.Namespace, opts.Subsystem, opts.Name),
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opts.Help,
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labelNames,
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opts.ConstLabels,
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)
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return &HistogramVec{
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MetricVec: MetricVec{
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children: map[uint64]Metric{},
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desc: desc,
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hash: fnv.New64a(),
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newMetric: func(lvs ...string) Metric {
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return newHistogram(desc, opts, lvs...)
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},
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},
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}
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}
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// GetMetricWithLabelValues replaces the method of the same name in
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// MetricVec. The difference is that this method returns a Histogram and not a
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// Metric so that no type conversion is required.
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func (m *HistogramVec) GetMetricWithLabelValues(lvs ...string) (Histogram, error) {
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metric, err := m.MetricVec.GetMetricWithLabelValues(lvs...)
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if metric != nil {
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return metric.(Histogram), err
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|
}
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return nil, err
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}
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// GetMetricWith replaces the method of the same name in MetricVec. The
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// difference is that this method returns a Histogram and not a Metric so that no
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// type conversion is required.
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func (m *HistogramVec) GetMetricWith(labels Labels) (Histogram, error) {
|
||||||
|
metric, err := m.MetricVec.GetMetricWith(labels)
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|
if metric != nil {
|
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|
return metric.(Histogram), err
|
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|
}
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||||||
|
return nil, err
|
||||||
|
}
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// 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)
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func (m *HistogramVec) WithLabelValues(lvs ...string) Histogram {
|
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|
return m.MetricVec.WithLabelValues(lvs...).(Histogram)
|
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|
}
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||||||
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// With works as GetMetricWith, but panics where GetMetricWithLabels would have
|
||||||
|
// returned an error. By not returning an error, With allows shortcuts like
|
||||||
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// myVec.With(Labels{"code": "404", "method": "GET"}).Observe(42.21)
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func (m *HistogramVec) With(labels Labels) Histogram {
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|
return m.MetricVec.With(labels).(Histogram)
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}
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318
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/histogram_test.go
generated
vendored
318
Godeps/_workspace/src/github.com/prometheus/client_golang/prometheus/histogram_test.go
generated
vendored
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// Copyright 2015 The Prometheus Authors
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|
// Licensed under the Apache License, Version 2.0 (the "License");
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||||||
|
// 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
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||||||
|
|
||||||
|
import (
|
||||||
|
"math"
|
||||||
|
"math/rand"
|
||||||
|
"reflect"
|
||||||
|
"sort"
|
||||||
|
"sync"
|
||||||
|
"testing"
|
||||||
|
"testing/quick"
|
||||||
|
|
||||||
|
dto "github.com/prometheus/client_model/go"
|
||||||
|
)
|
||||||
|
|
||||||
|
func benchmarkHistogramObserve(w int, b *testing.B) {
|
||||||
|
b.StopTimer()
|
||||||
|
|
||||||
|
wg := new(sync.WaitGroup)
|
||||||
|
wg.Add(w)
|
||||||
|
|
||||||
|
g := new(sync.WaitGroup)
|
||||||
|
g.Add(1)
|
||||||
|
|
||||||
|
s := NewHistogram(HistogramOpts{})
|
||||||
|
|
||||||
|
for i := 0; i < w; i++ {
|
||||||
|
go func() {
|
||||||
|
g.Wait()
|
||||||
|
|
||||||
|
for i := 0; i < b.N; i++ {
|
||||||
|
s.Observe(float64(i))
|
||||||
|
}
|
||||||
|
|
||||||
|
wg.Done()
|
||||||
|
}()
|
||||||
|
}
|
||||||
|
|
||||||
|
b.StartTimer()
|
||||||
|
g.Done()
|
||||||
|
wg.Wait()
|
||||||
|
}
|
||||||
|
|
||||||
|
func BenchmarkHistogramObserve1(b *testing.B) {
|
||||||
|
benchmarkHistogramObserve(1, b)
|
||||||
|
}
|
||||||
|
|
||||||
|
func BenchmarkHistogramObserve2(b *testing.B) {
|
||||||
|
benchmarkHistogramObserve(2, b)
|
||||||
|
}
|
||||||
|
|
||||||
|
func BenchmarkHistogramObserve4(b *testing.B) {
|
||||||
|
benchmarkHistogramObserve(4, b)
|
||||||
|
}
|
||||||
|
|
||||||
|
func BenchmarkHistogramObserve8(b *testing.B) {
|
||||||
|
benchmarkHistogramObserve(8, b)
|
||||||
|
}
|
||||||
|
|
||||||
|
func benchmarkHistogramWrite(w int, b *testing.B) {
|
||||||
|
b.StopTimer()
|
||||||
|
|
||||||
|
wg := new(sync.WaitGroup)
|
||||||
|
wg.Add(w)
|
||||||
|
|
||||||
|
g := new(sync.WaitGroup)
|
||||||
|
g.Add(1)
|
||||||
|
|
||||||
|
s := NewHistogram(HistogramOpts{})
|
||||||
|
|
||||||
|
for i := 0; i < 1000000; i++ {
|
||||||
|
s.Observe(float64(i))
|
||||||
|
}
|
||||||
|
|
||||||
|
for j := 0; j < w; j++ {
|
||||||
|
outs := make([]dto.Metric, b.N)
|
||||||
|
|
||||||
|
go func(o []dto.Metric) {
|
||||||
|
g.Wait()
|
||||||
|
|
||||||
|
for i := 0; i < b.N; i++ {
|
||||||
|
s.Write(&o[i])
|
||||||
|
}
|
||||||
|
|
||||||
|
wg.Done()
|
||||||
|
}(outs)
|
||||||
|
}
|
||||||
|
|
||||||
|
b.StartTimer()
|
||||||
|
g.Done()
|
||||||
|
wg.Wait()
|
||||||
|
}
|
||||||
|
|
||||||
|
func BenchmarkHistogramWrite1(b *testing.B) {
|
||||||
|
benchmarkHistogramWrite(1, b)
|
||||||
|
}
|
||||||
|
|
||||||
|
func BenchmarkHistogramWrite2(b *testing.B) {
|
||||||
|
benchmarkHistogramWrite(2, b)
|
||||||
|
}
|
||||||
|
|
||||||
|
func BenchmarkHistogramWrite4(b *testing.B) {
|
||||||
|
benchmarkHistogramWrite(4, b)
|
||||||
|
}
|
||||||
|
|
||||||
|
func BenchmarkHistogramWrite8(b *testing.B) {
|
||||||
|
benchmarkHistogramWrite(8, b)
|
||||||
|
}
|
||||||
|
|
||||||
|
// Intentionally adding +Inf here to test if that case is handled correctly.
|
||||||
|
// Also, getCumulativeCounts depends on it.
|
||||||
|
var testBuckets = []float64{-2, -1, -0.5, 0, 0.5, 1, 2, math.Inf(+1)}
|
||||||
|
|
||||||
|
func TestHistogramConcurrency(t *testing.T) {
|
||||||
|
rand.Seed(42)
|
||||||
|
|
||||||
|
it := func(n uint32) bool {
|
||||||
|
mutations := int(n%1e4 + 1e4)
|
||||||
|
concLevel := int(n%5 + 1)
|
||||||
|
total := mutations * concLevel
|
||||||
|
|
||||||
|
var start, end sync.WaitGroup
|
||||||
|
start.Add(1)
|
||||||
|
end.Add(concLevel)
|
||||||
|
|
||||||
|
sum := NewHistogram(HistogramOpts{
|
||||||
|
Name: "test_histogram",
|
||||||
|
Help: "helpless",
|
||||||
|
Buckets: testBuckets,
|
||||||
|
})
|
||||||
|
|
||||||
|
allVars := make([]float64, total)
|
||||||
|
var sampleSum float64
|
||||||
|
for i := 0; i < concLevel; i++ {
|
||||||
|
vals := make([]float64, mutations)
|
||||||
|
for j := 0; j < mutations; j++ {
|
||||||
|
v := rand.NormFloat64()
|
||||||
|
vals[j] = v
|
||||||
|
allVars[i*mutations+j] = v
|
||||||
|
sampleSum += v
|
||||||
|
}
|
||||||
|
|
||||||
|
go func(vals []float64) {
|
||||||
|
start.Wait()
|
||||||
|
for _, v := range vals {
|
||||||
|
sum.Observe(v)
|
||||||
|
}
|
||||||
|
end.Done()
|
||||||
|
}(vals)
|
||||||
|
}
|
||||||
|
sort.Float64s(allVars)
|
||||||
|
start.Done()
|
||||||
|
end.Wait()
|
||||||
|
|
||||||
|
m := &dto.Metric{}
|
||||||
|
sum.Write(m)
|
||||||
|
if got, want := int(*m.Histogram.SampleCount), total; got != want {
|
||||||
|
t.Errorf("got sample count %d, want %d", got, want)
|
||||||
|
}
|
||||||
|
if got, want := *m.Histogram.SampleSum, sampleSum; math.Abs((got-want)/want) > 0.001 {
|
||||||
|
t.Errorf("got sample sum %f, want %f", got, want)
|
||||||
|
}
|
||||||
|
|
||||||
|
wantCounts := getCumulativeCounts(allVars)
|
||||||
|
|
||||||
|
if got, want := len(m.Histogram.Bucket), len(testBuckets)-1; got != want {
|
||||||
|
t.Errorf("got %d buckets in protobuf, want %d", got, want)
|
||||||
|
}
|
||||||
|
for i, wantBound := range testBuckets {
|
||||||
|
if i == len(testBuckets)-1 {
|
||||||
|
break // No +Inf bucket in protobuf.
|
||||||
|
}
|
||||||
|
if gotBound := *m.Histogram.Bucket[i].UpperBound; gotBound != wantBound {
|
||||||
|
t.Errorf("got bound %f, want %f", gotBound, wantBound)
|
||||||
|
}
|
||||||
|
if gotCount, wantCount := *m.Histogram.Bucket[i].CumulativeCount, wantCounts[i]; gotCount != wantCount {
|
||||||
|
t.Errorf("got count %d, want %d", gotCount, wantCount)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return true
|
||||||
|
}
|
||||||
|
|
||||||
|
if err := quick.Check(it, nil); err != nil {
|
||||||
|
t.Error(err)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
func TestHistogramVecConcurrency(t *testing.T) {
|
||||||
|
rand.Seed(42)
|
||||||
|
|
||||||
|
objectives := make([]float64, 0, len(DefObjectives))
|
||||||
|
for qu := range DefObjectives {
|
||||||
|
|
||||||
|
objectives = append(objectives, qu)
|
||||||
|
}
|
||||||
|
sort.Float64s(objectives)
|
||||||
|
|
||||||
|
it := func(n uint32) bool {
|
||||||
|
mutations := int(n%1e4 + 1e4)
|
||||||
|
concLevel := int(n%7 + 1)
|
||||||
|
vecLength := int(n%3 + 1)
|
||||||
|
|
||||||
|
var start, end sync.WaitGroup
|
||||||
|
start.Add(1)
|
||||||
|
end.Add(concLevel)
|
||||||
|
|
||||||
|
his := NewHistogramVec(
|
||||||
|
HistogramOpts{
|
||||||
|
Name: "test_histogram",
|
||||||
|
Help: "helpless",
|
||||||
|
Buckets: []float64{-2, -1, -0.5, 0, 0.5, 1, 2, math.Inf(+1)},
|
||||||
|
},
|
||||||
|
[]string{"label"},
|
||||||
|
)
|
||||||
|
|
||||||
|
allVars := make([][]float64, vecLength)
|
||||||
|
sampleSums := make([]float64, vecLength)
|
||||||
|
for i := 0; i < concLevel; i++ {
|
||||||
|
vals := make([]float64, mutations)
|
||||||
|
picks := make([]int, mutations)
|
||||||
|
for j := 0; j < mutations; j++ {
|
||||||
|
v := rand.NormFloat64()
|
||||||
|
vals[j] = v
|
||||||
|
pick := rand.Intn(vecLength)
|
||||||
|
picks[j] = pick
|
||||||
|
allVars[pick] = append(allVars[pick], v)
|
||||||
|
sampleSums[pick] += v
|
||||||
|
}
|
||||||
|
|
||||||
|
go func(vals []float64) {
|
||||||
|
start.Wait()
|
||||||
|
for i, v := range vals {
|
||||||
|
his.WithLabelValues(string('A' + picks[i])).Observe(v)
|
||||||
|
}
|
||||||
|
end.Done()
|
||||||
|
}(vals)
|
||||||
|
}
|
||||||
|
for _, vars := range allVars {
|
||||||
|
sort.Float64s(vars)
|
||||||
|
}
|
||||||
|
start.Done()
|
||||||
|
end.Wait()
|
||||||
|
|
||||||
|
for i := 0; i < vecLength; i++ {
|
||||||
|
m := &dto.Metric{}
|
||||||
|
s := his.WithLabelValues(string('A' + i))
|
||||||
|
s.Write(m)
|
||||||
|
|
||||||
|
if got, want := len(m.Histogram.Bucket), len(testBuckets)-1; got != want {
|
||||||
|
t.Errorf("got %d buckets in protobuf, want %d", got, want)
|
||||||
|
}
|
||||||
|
if got, want := int(*m.Histogram.SampleCount), len(allVars[i]); got != want {
|
||||||
|
t.Errorf("got sample count %d, want %d", got, want)
|
||||||
|
}
|
||||||
|
if got, want := *m.Histogram.SampleSum, sampleSums[i]; math.Abs((got-want)/want) > 0.001 {
|
||||||
|
t.Errorf("got sample sum %f, want %f", got, want)
|
||||||
|
}
|
||||||
|
|
||||||
|
wantCounts := getCumulativeCounts(allVars[i])
|
||||||
|
|
||||||
|
for j, wantBound := range testBuckets {
|
||||||
|
if j == len(testBuckets)-1 {
|
||||||
|
break // No +Inf bucket in protobuf.
|
||||||
|
}
|
||||||
|
if gotBound := *m.Histogram.Bucket[j].UpperBound; gotBound != wantBound {
|
||||||
|
t.Errorf("got bound %f, want %f", gotBound, wantBound)
|
||||||
|
}
|
||||||
|
if gotCount, wantCount := *m.Histogram.Bucket[j].CumulativeCount, wantCounts[j]; gotCount != wantCount {
|
||||||
|
t.Errorf("got count %d, want %d", gotCount, wantCount)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return true
|
||||||
|
}
|
||||||
|
|
||||||
|
if err := quick.Check(it, nil); err != nil {
|
||||||
|
t.Error(err)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
func getCumulativeCounts(vars []float64) []uint64 {
|
||||||
|
counts := make([]uint64, len(testBuckets))
|
||||||
|
for _, v := range vars {
|
||||||
|
for i := len(testBuckets) - 1; i >= 0; i-- {
|
||||||
|
if v > testBuckets[i] {
|
||||||
|
break
|
||||||
|
}
|
||||||
|
counts[i]++
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return counts
|
||||||
|
}
|
||||||
|
|
||||||
|
func TestBuckets(t *testing.T) {
|
||||||
|
got := LinearBuckets(-15, 5, 6)
|
||||||
|
want := []float64{-15, -10, -5, 0, 5, 10}
|
||||||
|
if !reflect.DeepEqual(got, want) {
|
||||||
|
t.Errorf("linear buckets: got %v, want %v", got, want)
|
||||||
|
}
|
||||||
|
|
||||||
|
got = ExponentialBuckets(100, 1.2, 3)
|
||||||
|
want = []float64{100, 120, 144}
|
||||||
|
if !reflect.DeepEqual(got, want) {
|
||||||
|
t.Errorf("linear buckets: got %v, want %v", got, want)
|
||||||
|
}
|
||||||
|
}
|
||||||
Loading…
Reference in new issue