forked from mirror/client_golang
877 lines
26 KiB
Go
877 lines
26 KiB
Go
// 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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"math"
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"math/rand"
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"reflect"
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"runtime"
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"sort"
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"sync"
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"sync/atomic"
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"testing"
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"testing/quick"
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"time"
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//nolint:staticcheck // Ignore SA1019. Need to keep deprecated package for compatibility.
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"github.com/golang/protobuf/proto"
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"google.golang.org/protobuf/types/known/timestamppb"
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"github.com/prometheus/client_golang/prometheus/internal"
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dto "github.com/prometheus/client_model/go"
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)
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func benchmarkHistogramObserve(w int, b *testing.B) {
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b.StopTimer()
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wg := new(sync.WaitGroup)
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wg.Add(w)
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g := new(sync.WaitGroup)
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g.Add(1)
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s := NewHistogram(HistogramOpts{})
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for i := 0; i < w; i++ {
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go func() {
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g.Wait()
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for i := 0; i < b.N; i++ {
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s.Observe(float64(i))
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}
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wg.Done()
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}()
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}
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b.StartTimer()
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g.Done()
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wg.Wait()
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}
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func BenchmarkHistogramObserve1(b *testing.B) {
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benchmarkHistogramObserve(1, b)
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}
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func BenchmarkHistogramObserve2(b *testing.B) {
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benchmarkHistogramObserve(2, b)
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}
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func BenchmarkHistogramObserve4(b *testing.B) {
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benchmarkHistogramObserve(4, b)
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}
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func BenchmarkHistogramObserve8(b *testing.B) {
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benchmarkHistogramObserve(8, b)
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}
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func benchmarkHistogramWrite(w int, b *testing.B) {
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b.StopTimer()
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wg := new(sync.WaitGroup)
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wg.Add(w)
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g := new(sync.WaitGroup)
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g.Add(1)
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s := NewHistogram(HistogramOpts{})
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for i := 0; i < 1000000; i++ {
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s.Observe(float64(i))
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}
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for j := 0; j < w; j++ {
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outs := make([]dto.Metric, b.N)
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go func(o []dto.Metric) {
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g.Wait()
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for i := 0; i < b.N; i++ {
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s.Write(&o[i])
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}
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wg.Done()
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}(outs)
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}
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b.StartTimer()
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g.Done()
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wg.Wait()
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}
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func BenchmarkHistogramWrite1(b *testing.B) {
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benchmarkHistogramWrite(1, b)
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}
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func BenchmarkHistogramWrite2(b *testing.B) {
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benchmarkHistogramWrite(2, b)
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}
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func BenchmarkHistogramWrite4(b *testing.B) {
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benchmarkHistogramWrite(4, b)
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}
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func BenchmarkHistogramWrite8(b *testing.B) {
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benchmarkHistogramWrite(8, b)
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}
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func TestHistogramNonMonotonicBuckets(t *testing.T) {
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testCases := map[string][]float64{
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"not strictly monotonic": {1, 2, 2, 3},
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"not monotonic at all": {1, 2, 4, 3, 5},
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"have +Inf in the middle": {1, 2, math.Inf(+1), 3},
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}
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for name, buckets := range testCases {
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func() {
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defer func() {
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if r := recover(); r == nil {
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t.Errorf("Buckets %v are %s but NewHistogram did not panic.", buckets, name)
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}
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}()
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_ = NewHistogram(HistogramOpts{
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Name: "test_histogram",
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Help: "helpless",
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Buckets: buckets,
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})
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}()
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}
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}
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// Intentionally adding +Inf here to test if that case is handled correctly.
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// Also, getCumulativeCounts depends on it.
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var testBuckets = []float64{-2, -1, -0.5, 0, 0.5, 1, 2, math.Inf(+1)}
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func TestHistogramConcurrency(t *testing.T) {
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if testing.Short() {
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t.Skip("Skipping test in short mode.")
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}
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rand.Seed(42)
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it := func(n uint32) bool {
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mutations := int(n%1e4 + 1e4)
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concLevel := int(n%5 + 1)
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total := mutations * concLevel
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var start, end sync.WaitGroup
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start.Add(1)
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end.Add(concLevel)
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his := NewHistogram(HistogramOpts{
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Name: "test_histogram",
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Help: "helpless",
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Buckets: testBuckets,
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})
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allVars := make([]float64, total)
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var sampleSum float64
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for i := 0; i < concLevel; i++ {
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vals := make([]float64, mutations)
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for j := 0; j < mutations; j++ {
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v := rand.NormFloat64()
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vals[j] = v
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allVars[i*mutations+j] = v
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sampleSum += v
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}
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go func(vals []float64) {
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start.Wait()
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for _, v := range vals {
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if n%2 == 0 {
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his.Observe(v)
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} else {
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his.(ExemplarObserver).ObserveWithExemplar(v, Labels{"foo": "bar"})
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}
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}
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end.Done()
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}(vals)
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}
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sort.Float64s(allVars)
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start.Done()
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end.Wait()
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m := &dto.Metric{}
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his.Write(m)
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if got, want := int(*m.Histogram.SampleCount), total; got != want {
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t.Errorf("got sample count %d, want %d", got, want)
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}
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if got, want := *m.Histogram.SampleSum, sampleSum; math.Abs((got-want)/want) > 0.001 {
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t.Errorf("got sample sum %f, want %f", got, want)
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}
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wantCounts := getCumulativeCounts(allVars)
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wantBuckets := len(testBuckets)
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if !math.IsInf(m.Histogram.Bucket[len(m.Histogram.Bucket)-1].GetUpperBound(), +1) {
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wantBuckets--
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}
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if got := len(m.Histogram.Bucket); got != wantBuckets {
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t.Errorf("got %d buckets in protobuf, want %d", got, wantBuckets)
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}
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for i, wantBound := range testBuckets {
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if i == len(testBuckets)-1 {
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break // No +Inf bucket in protobuf.
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}
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if gotBound := *m.Histogram.Bucket[i].UpperBound; gotBound != wantBound {
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t.Errorf("got bound %f, want %f", gotBound, wantBound)
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}
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if gotCount, wantCount := *m.Histogram.Bucket[i].CumulativeCount, wantCounts[i]; gotCount != wantCount {
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t.Errorf("got count %d, want %d", gotCount, wantCount)
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}
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}
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return true
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}
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if err := quick.Check(it, nil); err != nil {
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t.Error(err)
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}
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}
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func TestHistogramVecConcurrency(t *testing.T) {
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if testing.Short() {
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t.Skip("Skipping test in short mode.")
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}
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rand.Seed(42)
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it := func(n uint32) bool {
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mutations := int(n%1e4 + 1e4)
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concLevel := int(n%7 + 1)
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vecLength := int(n%3 + 1)
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var start, end sync.WaitGroup
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start.Add(1)
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end.Add(concLevel)
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his := NewHistogramVec(
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HistogramOpts{
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Name: "test_histogram",
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Help: "helpless",
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Buckets: []float64{-2, -1, -0.5, 0, 0.5, 1, 2, math.Inf(+1)},
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},
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[]string{"label"},
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)
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allVars := make([][]float64, vecLength)
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sampleSums := make([]float64, vecLength)
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for i := 0; i < concLevel; i++ {
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vals := make([]float64, mutations)
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picks := make([]int, mutations)
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for j := 0; j < mutations; j++ {
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v := rand.NormFloat64()
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vals[j] = v
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pick := rand.Intn(vecLength)
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picks[j] = pick
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allVars[pick] = append(allVars[pick], v)
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sampleSums[pick] += v
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}
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go func(vals []float64) {
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start.Wait()
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for i, v := range vals {
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his.WithLabelValues(string('A' + rune(picks[i]))).Observe(v)
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}
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end.Done()
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}(vals)
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}
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for _, vars := range allVars {
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sort.Float64s(vars)
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}
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start.Done()
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end.Wait()
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for i := 0; i < vecLength; i++ {
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m := &dto.Metric{}
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s := his.WithLabelValues(string('A' + rune(i)))
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s.(Histogram).Write(m)
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if got, want := len(m.Histogram.Bucket), len(testBuckets)-1; got != want {
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t.Errorf("got %d buckets in protobuf, want %d", got, want)
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}
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if got, want := int(*m.Histogram.SampleCount), len(allVars[i]); got != want {
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t.Errorf("got sample count %d, want %d", got, want)
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}
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if got, want := *m.Histogram.SampleSum, sampleSums[i]; math.Abs((got-want)/want) > 0.001 {
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t.Errorf("got sample sum %f, want %f", got, want)
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}
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wantCounts := getCumulativeCounts(allVars[i])
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for j, wantBound := range testBuckets {
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if j == len(testBuckets)-1 {
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break // No +Inf bucket in protobuf.
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}
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if gotBound := *m.Histogram.Bucket[j].UpperBound; gotBound != wantBound {
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t.Errorf("got bound %f, want %f", gotBound, wantBound)
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}
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if gotCount, wantCount := *m.Histogram.Bucket[j].CumulativeCount, wantCounts[j]; gotCount != wantCount {
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t.Errorf("got count %d, want %d", gotCount, wantCount)
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}
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}
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}
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return true
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}
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if err := quick.Check(it, nil); err != nil {
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t.Error(err)
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}
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}
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func getCumulativeCounts(vars []float64) []uint64 {
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counts := make([]uint64, len(testBuckets))
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for _, v := range vars {
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for i := len(testBuckets) - 1; i >= 0; i-- {
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if v > testBuckets[i] {
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break
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}
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counts[i]++
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}
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}
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return counts
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}
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func TestBuckets(t *testing.T) {
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got := LinearBuckets(-15, 5, 6)
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want := []float64{-15, -10, -5, 0, 5, 10}
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if !reflect.DeepEqual(got, want) {
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t.Errorf("linear buckets: got %v, want %v", got, want)
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}
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got = ExponentialBuckets(100, 1.2, 3)
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want = []float64{100, 120, 144}
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if !reflect.DeepEqual(got, want) {
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t.Errorf("exponential buckets: got %v, want %v", got, want)
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}
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got = ExponentialBucketsRange(1, 100, 10)
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want = []float64{
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1.0, 1.6681, 2.7825, 4.6415, 7.7426, 12.9154, 21.5443,
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35.9381, 59.9484, 100.0000,
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}
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const epsilon = 0.0001
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if !internal.AlmostEqualFloat64s(got, want, epsilon) {
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t.Errorf("exponential buckets range: got %v, want %v (epsilon %f)", got, want, epsilon)
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}
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}
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func TestHistogramAtomicObserve(t *testing.T) {
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var (
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quit = make(chan struct{})
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his = NewHistogram(HistogramOpts{
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Buckets: []float64{0.5, 10, 20},
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})
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)
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defer func() { close(quit) }()
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observe := func() {
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for {
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select {
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case <-quit:
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return
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default:
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his.Observe(1)
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}
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}
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}
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go observe()
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go observe()
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go observe()
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for i := 0; i < 100; i++ {
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m := &dto.Metric{}
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if err := his.Write(m); err != nil {
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t.Fatal("unexpected error writing histogram:", err)
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}
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h := m.GetHistogram()
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if h.GetSampleCount() != uint64(h.GetSampleSum()) ||
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h.GetSampleCount() != h.GetBucket()[1].GetCumulativeCount() ||
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h.GetSampleCount() != h.GetBucket()[2].GetCumulativeCount() {
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t.Fatalf(
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"inconsistent counts in histogram: count=%d sum=%f buckets=[%d, %d]",
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h.GetSampleCount(), h.GetSampleSum(),
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h.GetBucket()[1].GetCumulativeCount(), h.GetBucket()[2].GetCumulativeCount(),
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)
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}
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runtime.Gosched()
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}
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}
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func TestHistogramExemplar(t *testing.T) {
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now := time.Now()
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histogram := NewHistogram(HistogramOpts{
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Name: "test",
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Help: "test help",
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Buckets: []float64{1, 2, 3, 4},
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}).(*histogram)
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histogram.now = func() time.Time { return now }
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ts := timestamppb.New(now)
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if err := ts.CheckValid(); err != nil {
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t.Fatal(err)
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}
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expectedExemplars := []*dto.Exemplar{
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nil,
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{
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Label: []*dto.LabelPair{
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{Name: proto.String("id"), Value: proto.String("2")},
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},
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Value: proto.Float64(1.6),
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Timestamp: ts,
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},
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nil,
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{
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Label: []*dto.LabelPair{
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{Name: proto.String("id"), Value: proto.String("3")},
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},
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Value: proto.Float64(4),
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Timestamp: ts,
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},
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{
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Label: []*dto.LabelPair{
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{Name: proto.String("id"), Value: proto.String("4")},
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},
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Value: proto.Float64(4.5),
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Timestamp: ts,
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},
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}
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histogram.ObserveWithExemplar(1.5, Labels{"id": "1"})
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histogram.ObserveWithExemplar(1.6, Labels{"id": "2"}) // To replace exemplar in bucket 0.
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histogram.ObserveWithExemplar(4, Labels{"id": "3"})
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histogram.ObserveWithExemplar(4.5, Labels{"id": "4"}) // Should go to +Inf bucket.
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for i, ex := range histogram.exemplars {
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var got, expected string
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if val := ex.Load(); val != nil {
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got = val.(*dto.Exemplar).String()
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}
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if expectedExemplars[i] != nil {
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expected = expectedExemplars[i].String()
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}
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if got != expected {
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t.Errorf("expected exemplar %s, got %s.", expected, got)
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}
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}
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}
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func TestSparseHistogram(t *testing.T) {
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scenarios := []struct {
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name string
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observations []float64 // With simulated interval of 1m.
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factor float64
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zeroThreshold float64
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maxBuckets uint32
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minResetDuration time.Duration
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maxZeroThreshold float64
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want string // String representation of protobuf.
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}{
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{
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name: "no sparse buckets",
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observations: []float64{1, 2, 3},
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factor: 1,
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want: `sample_count:3 sample_sum:6 bucket:<cumulative_count:0 upper_bound:0.005 > bucket:<cumulative_count:0 upper_bound:0.01 > bucket:<cumulative_count:0 upper_bound:0.025 > bucket:<cumulative_count:0 upper_bound:0.05 > bucket:<cumulative_count:0 upper_bound:0.1 > bucket:<cumulative_count:0 upper_bound:0.25 > bucket:<cumulative_count:0 upper_bound:0.5 > bucket:<cumulative_count:1 upper_bound:1 > bucket:<cumulative_count:2 upper_bound:2.5 > bucket:<cumulative_count:3 upper_bound:5 > bucket:<cumulative_count:3 upper_bound:10 > `, // Has conventional buckets because there are no sparse buckets.
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},
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{
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name: "factor 1.1 results in schema 3",
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observations: []float64{0, 1, 2, 3},
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factor: 1.1,
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want: `sample_count:4 sample_sum:6 schema:3 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:1 > positive_span:<offset:7 length:1 > positive_span:<offset:4 length:1 > positive_delta:1 positive_delta:0 positive_delta:0 `,
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},
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{
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name: "factor 1.2 results in schema 2",
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observations: []float64{0, 1, 1.2, 1.4, 1.8, 2},
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factor: 1.2,
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want: `sample_count:6 sample_sum:7.4 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
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},
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{
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name: "factor 4 results in schema -1",
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observations: []float64{
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0.5, 1, // Bucket 0: (0.25, 1]
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1.5, 2, 3, 3.5, // Bucket 1: (1, 4]
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5, 6, 7, // Bucket 2: (4, 16]
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33.33, // Bucket 3: (16, 64]
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},
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factor: 4,
|
|
want: `sample_count:10 sample_sum:62.83 schema:-1 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:0 length:4 > positive_delta:2 positive_delta:2 positive_delta:-1 positive_delta:-2 `,
|
|
},
|
|
{
|
|
name: "factor 17 results in schema -2",
|
|
observations: []float64{
|
|
0.5, 1, // Bucket 0: (0.0625, 1]
|
|
1.5, 2, 3, 3.5, 5, 6, 7, // Bucket 1: (1, 16]
|
|
33.33, // Bucket 2: (16, 256]
|
|
},
|
|
factor: 17,
|
|
want: `sample_count:10 sample_sum:62.83 schema:-2 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:0 length:3 > positive_delta:2 positive_delta:5 positive_delta:-6 `,
|
|
},
|
|
{
|
|
name: "negative buckets",
|
|
observations: []float64{0, -1, -1.2, -1.4, -1.8, -2},
|
|
factor: 1.2,
|
|
want: `sample_count:6 sample_sum:-7.4 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:0 length:5 > negative_delta:1 negative_delta:-1 negative_delta:2 negative_delta:-2 negative_delta:2 `,
|
|
},
|
|
{
|
|
name: "negative and positive buckets",
|
|
observations: []float64{0, -1, -1.2, -1.4, -1.8, -2, 1, 1.2, 1.4, 1.8, 2},
|
|
factor: 1.2,
|
|
want: `sample_count:11 sample_sum:0 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:0 length:5 > negative_delta:1 negative_delta:-1 negative_delta:2 negative_delta:-2 negative_delta:2 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
|
|
},
|
|
{
|
|
name: "wide zero bucket",
|
|
observations: []float64{0, -1, -1.2, -1.4, -1.8, -2, 1, 1.2, 1.4, 1.8, 2},
|
|
factor: 1.2,
|
|
zeroThreshold: 1.4,
|
|
want: `sample_count:11 sample_sum:0 schema:2 zero_threshold:1.4 zero_count:7 negative_span:<offset:4 length:1 > negative_delta:2 positive_span:<offset:4 length:1 > positive_delta:2 `,
|
|
},
|
|
{
|
|
name: "NaN observation",
|
|
observations: []float64{0, 1, 1.2, 1.4, 1.8, 2, math.NaN()},
|
|
factor: 1.2,
|
|
want: `sample_count:7 sample_sum:nan schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
|
|
},
|
|
{
|
|
name: "+Inf observation",
|
|
observations: []float64{0, 1, 1.2, 1.4, 1.8, 2, math.Inf(+1)},
|
|
factor: 1.2,
|
|
want: `sample_count:7 sample_sum:inf schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_span:<offset:4092 length:1 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 positive_delta:-1 `,
|
|
},
|
|
{
|
|
name: "-Inf observation",
|
|
observations: []float64{0, 1, 1.2, 1.4, 1.8, 2, math.Inf(-1)},
|
|
factor: 1.2,
|
|
want: `sample_count:7 sample_sum:-inf schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:4097 length:1 > negative_delta:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
|
|
},
|
|
{
|
|
name: "limited buckets but nothing triggered",
|
|
observations: []float64{0, 1, 1.2, 1.4, 1.8, 2},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
want: `sample_count:6 sample_sum:7.4 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
|
|
},
|
|
{
|
|
name: "buckets limited by halving resolution",
|
|
observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 3},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
want: `sample_count:8 sample_sum:11.5 schema:1 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:2 positive_delta:-1 positive_delta:-2 positive_delta:1 `,
|
|
},
|
|
{
|
|
name: "buckets limited by widening the zero bucket",
|
|
observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 3},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
maxZeroThreshold: 1.2,
|
|
want: `sample_count:8 sample_sum:11.5 schema:2 zero_threshold:1 zero_count:2 positive_span:<offset:1 length:7 > positive_delta:1 positive_delta:1 positive_delta:-2 positive_delta:2 positive_delta:-2 positive_delta:0 positive_delta:1 `,
|
|
},
|
|
{
|
|
name: "buckets limited by widening the zero bucket twice",
|
|
observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 3, 4},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
maxZeroThreshold: 1.2,
|
|
want: `sample_count:9 sample_sum:15.5 schema:2 zero_threshold:1.189207115002721 zero_count:3 positive_span:<offset:2 length:7 > positive_delta:2 positive_delta:-2 positive_delta:2 positive_delta:-2 positive_delta:0 positive_delta:1 positive_delta:0 `,
|
|
},
|
|
{
|
|
name: "buckets limited by reset",
|
|
observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 3, 4},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
maxZeroThreshold: 1.2,
|
|
minResetDuration: 5 * time.Minute,
|
|
want: `sample_count:2 sample_sum:7 schema:2 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:7 length:2 > positive_delta:1 positive_delta:0 `,
|
|
},
|
|
{
|
|
name: "limited buckets but nothing triggered, negative observations",
|
|
observations: []float64{0, -1, -1.2, -1.4, -1.8, -2},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
want: `sample_count:6 sample_sum:-7.4 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:0 length:5 > negative_delta:1 negative_delta:-1 negative_delta:2 negative_delta:-2 negative_delta:2 `,
|
|
},
|
|
{
|
|
name: "buckets limited by halving resolution, negative observations",
|
|
observations: []float64{0, -1, -1.1, -1.2, -1.4, -1.8, -2, -3},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
want: `sample_count:8 sample_sum:-11.5 schema:1 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:0 length:5 > negative_delta:1 negative_delta:2 negative_delta:-1 negative_delta:-2 negative_delta:1 `,
|
|
},
|
|
{
|
|
name: "buckets limited by widening the zero bucket, negative observations",
|
|
observations: []float64{0, -1, -1.1, -1.2, -1.4, -1.8, -2, -3},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
maxZeroThreshold: 1.2,
|
|
want: `sample_count:8 sample_sum:-11.5 schema:2 zero_threshold:1 zero_count:2 negative_span:<offset:1 length:7 > negative_delta:1 negative_delta:1 negative_delta:-2 negative_delta:2 negative_delta:-2 negative_delta:0 negative_delta:1 `,
|
|
},
|
|
{
|
|
name: "buckets limited by widening the zero bucket twice, negative observations",
|
|
observations: []float64{0, -1, -1.1, -1.2, -1.4, -1.8, -2, -3, -4},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
maxZeroThreshold: 1.2,
|
|
want: `sample_count:9 sample_sum:-15.5 schema:2 zero_threshold:1.189207115002721 zero_count:3 negative_span:<offset:2 length:7 > negative_delta:2 negative_delta:-2 negative_delta:2 negative_delta:-2 negative_delta:0 negative_delta:1 negative_delta:0 `,
|
|
},
|
|
{
|
|
name: "buckets limited by reset, negative observations",
|
|
observations: []float64{0, -1, -1.1, -1.2, -1.4, -1.8, -2, -3, -4},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
maxZeroThreshold: 1.2,
|
|
minResetDuration: 5 * time.Minute,
|
|
want: `sample_count:2 sample_sum:-7 schema:2 zero_threshold:2.938735877055719e-39 zero_count:0 negative_span:<offset:7 length:2 > negative_delta:1 negative_delta:0 `,
|
|
},
|
|
{
|
|
name: "buckets limited by halving resolution, then reset",
|
|
observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 5, 5.1, 3, 4},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
minResetDuration: 9 * time.Minute,
|
|
want: `sample_count:2 sample_sum:7 schema:2 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:7 length:2 > positive_delta:1 positive_delta:0 `,
|
|
},
|
|
{
|
|
name: "buckets limited by widening the zero bucket, then reset",
|
|
observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 5, 5.1, 3, 4},
|
|
factor: 1.2,
|
|
maxBuckets: 4,
|
|
maxZeroThreshold: 1.2,
|
|
minResetDuration: 9 * time.Minute,
|
|
want: `sample_count:2 sample_sum:7 schema:2 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:7 length:2 > positive_delta:1 positive_delta:0 `,
|
|
},
|
|
}
|
|
|
|
for _, s := range scenarios {
|
|
t.Run(s.name, func(t *testing.T) {
|
|
his := NewHistogram(HistogramOpts{
|
|
Name: "name",
|
|
Help: "help",
|
|
NativeHistogramBucketFactor: s.factor,
|
|
NativeHistogramZeroThreshold: s.zeroThreshold,
|
|
NativeHistogramMaxBucketNumber: s.maxBuckets,
|
|
NativeHistogramMinResetDuration: s.minResetDuration,
|
|
NativeHistogramMaxZeroThreshold: s.maxZeroThreshold,
|
|
})
|
|
ts := time.Now().Add(30 * time.Second)
|
|
now := func() time.Time {
|
|
return ts
|
|
}
|
|
his.(*histogram).now = now
|
|
for _, o := range s.observations {
|
|
his.Observe(o)
|
|
ts = ts.Add(time.Minute)
|
|
}
|
|
m := &dto.Metric{}
|
|
if err := his.Write(m); err != nil {
|
|
t.Fatal("unexpected error writing metric", err)
|
|
}
|
|
got := m.Histogram.String()
|
|
if s.want != got {
|
|
t.Errorf("want histogram %q, got %q", s.want, got)
|
|
}
|
|
})
|
|
}
|
|
}
|
|
|
|
func TestSparseHistogramConcurrency(t *testing.T) {
|
|
if testing.Short() {
|
|
t.Skip("Skipping test in short mode.")
|
|
}
|
|
|
|
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)
|
|
|
|
his := NewHistogram(HistogramOpts{
|
|
Name: "test_sparse_histogram",
|
|
Help: "This help is sparse.",
|
|
NativeHistogramBucketFactor: 1.05,
|
|
NativeHistogramZeroThreshold: 0.0000001,
|
|
NativeHistogramMaxBucketNumber: 50,
|
|
NativeHistogramMinResetDuration: time.Hour, // Comment out to test for totals below.
|
|
NativeHistogramMaxZeroThreshold: 0.001,
|
|
})
|
|
|
|
ts := time.Now().Add(30 * time.Second).Unix()
|
|
now := func() time.Time {
|
|
return time.Unix(atomic.LoadInt64(&ts), 0)
|
|
}
|
|
his.(*histogram).now = now
|
|
|
|
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 {
|
|
// An observation every 1 to 10 seconds.
|
|
atomic.AddInt64(&ts, rand.Int63n(10)+1)
|
|
his.Observe(v)
|
|
}
|
|
end.Done()
|
|
}(vals)
|
|
}
|
|
sort.Float64s(allVars)
|
|
start.Done()
|
|
end.Wait()
|
|
|
|
m := &dto.Metric{}
|
|
his.Write(m)
|
|
|
|
// Uncomment these tests for totals only if you have disabled histogram resets above.
|
|
//
|
|
// 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)
|
|
// }
|
|
|
|
sumBuckets := int(m.Histogram.GetZeroCount())
|
|
current := 0
|
|
for _, delta := range m.Histogram.GetNegativeDelta() {
|
|
current += int(delta)
|
|
if current < 0 {
|
|
t.Fatalf("negative bucket population negative: %d", current)
|
|
}
|
|
sumBuckets += current
|
|
}
|
|
current = 0
|
|
for _, delta := range m.Histogram.GetPositiveDelta() {
|
|
current += int(delta)
|
|
if current < 0 {
|
|
t.Fatalf("positive bucket population negative: %d", current)
|
|
}
|
|
sumBuckets += current
|
|
}
|
|
if got, want := sumBuckets, int(*m.Histogram.SampleCount); got != want {
|
|
t.Errorf("got bucket population sum %d, want %d", got, want)
|
|
}
|
|
|
|
return true
|
|
}
|
|
|
|
if err := quick.Check(it, nil); err != nil {
|
|
t.Error(err)
|
|
}
|
|
}
|
|
|
|
func TestGetLe(t *testing.T) {
|
|
scenarios := []struct {
|
|
key int
|
|
schema int32
|
|
want float64
|
|
}{
|
|
{
|
|
key: -1,
|
|
schema: -1,
|
|
want: 0.25,
|
|
},
|
|
{
|
|
key: 0,
|
|
schema: -1,
|
|
want: 1,
|
|
},
|
|
{
|
|
key: 1,
|
|
schema: -1,
|
|
want: 4,
|
|
},
|
|
{
|
|
key: 512,
|
|
schema: -1,
|
|
want: math.MaxFloat64,
|
|
},
|
|
{
|
|
key: 513,
|
|
schema: -1,
|
|
want: math.Inf(+1),
|
|
},
|
|
{
|
|
key: -1,
|
|
schema: 0,
|
|
want: 0.5,
|
|
},
|
|
{
|
|
key: 0,
|
|
schema: 0,
|
|
want: 1,
|
|
},
|
|
{
|
|
key: 1,
|
|
schema: 0,
|
|
want: 2,
|
|
},
|
|
{
|
|
key: 1024,
|
|
schema: 0,
|
|
want: math.MaxFloat64,
|
|
},
|
|
{
|
|
key: 1025,
|
|
schema: 0,
|
|
want: math.Inf(+1),
|
|
},
|
|
{
|
|
key: -1,
|
|
schema: 2,
|
|
want: 0.8408964152537144,
|
|
},
|
|
{
|
|
key: 0,
|
|
schema: 2,
|
|
want: 1,
|
|
},
|
|
{
|
|
key: 1,
|
|
schema: 2,
|
|
want: 1.189207115002721,
|
|
},
|
|
{
|
|
key: 4096,
|
|
schema: 2,
|
|
want: math.MaxFloat64,
|
|
},
|
|
{
|
|
key: 4097,
|
|
schema: 2,
|
|
want: math.Inf(+1),
|
|
},
|
|
}
|
|
|
|
for i, s := range scenarios {
|
|
got := getLe(s.key, s.schema)
|
|
if s.want != got {
|
|
t.Errorf("%d. key %d, schema %d, want upper bound of %g, got %g", i, s.key, s.schema, s.want, got)
|
|
}
|
|
}
|
|
}
|