client_golang/prometheus/histogram_test.go

877 lines
26 KiB
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 (
"math"
"math/rand"
"reflect"
"runtime"
"sort"
"sync"
"sync/atomic"
"testing"
"testing/quick"
"time"
//nolint:staticcheck // Ignore SA1019. Need to keep deprecated package for compatibility.
"github.com/golang/protobuf/proto"
"google.golang.org/protobuf/types/known/timestamppb"
"github.com/prometheus/client_golang/prometheus/internal"
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)
}
func TestHistogramNonMonotonicBuckets(t *testing.T) {
testCases := map[string][]float64{
"not strictly monotonic": {1, 2, 2, 3},
"not monotonic at all": {1, 2, 4, 3, 5},
"have +Inf in the middle": {1, 2, math.Inf(+1), 3},
}
for name, buckets := range testCases {
func() {
defer func() {
if r := recover(); r == nil {
t.Errorf("Buckets %v are %s but NewHistogram did not panic.", buckets, name)
}
}()
_ = NewHistogram(HistogramOpts{
Name: "test_histogram",
Help: "helpless",
Buckets: buckets,
})
}()
}
}
// 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) {
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_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 {
if n%2 == 0 {
his.Observe(v)
} else {
his.(ExemplarObserver).ObserveWithExemplar(v, Labels{"foo": "bar"})
}
}
end.Done()
}(vals)
}
sort.Float64s(allVars)
start.Done()
end.Wait()
m := &dto.Metric{}
his.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)
wantBuckets := len(testBuckets)
if !math.IsInf(m.Histogram.Bucket[len(m.Histogram.Bucket)-1].GetUpperBound(), +1) {
wantBuckets--
}
if got := len(m.Histogram.Bucket); got != wantBuckets {
t.Errorf("got %d buckets in protobuf, want %d", got, wantBuckets)
}
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) {
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%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' + rune(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' + rune(i)))
s.(Histogram).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("exponential buckets: got %v, want %v", got, want)
}
got = ExponentialBucketsRange(1, 100, 10)
want = []float64{
1.0, 1.6681, 2.7825, 4.6415, 7.7426, 12.9154, 21.5443,
35.9381, 59.9484, 100.0000,
}
const epsilon = 0.0001
if !internal.AlmostEqualFloat64s(got, want, epsilon) {
t.Errorf("exponential buckets range: got %v, want %v (epsilon %f)", got, want, epsilon)
}
}
func TestHistogramAtomicObserve(t *testing.T) {
var (
quit = make(chan struct{})
his = NewHistogram(HistogramOpts{
Buckets: []float64{0.5, 10, 20},
})
)
defer func() { close(quit) }()
observe := func() {
for {
select {
case <-quit:
return
default:
his.Observe(1)
}
}
}
go observe()
go observe()
go observe()
for i := 0; i < 100; i++ {
m := &dto.Metric{}
if err := his.Write(m); err != nil {
t.Fatal("unexpected error writing histogram:", err)
}
h := m.GetHistogram()
if h.GetSampleCount() != uint64(h.GetSampleSum()) ||
h.GetSampleCount() != h.GetBucket()[1].GetCumulativeCount() ||
h.GetSampleCount() != h.GetBucket()[2].GetCumulativeCount() {
t.Fatalf(
"inconsistent counts in histogram: count=%d sum=%f buckets=[%d, %d]",
h.GetSampleCount(), h.GetSampleSum(),
h.GetBucket()[1].GetCumulativeCount(), h.GetBucket()[2].GetCumulativeCount(),
)
}
runtime.Gosched()
}
}
func TestHistogramExemplar(t *testing.T) {
now := time.Now()
histogram := NewHistogram(HistogramOpts{
Name: "test",
Help: "test help",
Buckets: []float64{1, 2, 3, 4},
}).(*histogram)
histogram.now = func() time.Time { return now }
ts := timestamppb.New(now)
if err := ts.CheckValid(); err != nil {
t.Fatal(err)
}
expectedExemplars := []*dto.Exemplar{
nil,
{
Label: []*dto.LabelPair{
{Name: proto.String("id"), Value: proto.String("2")},
},
Value: proto.Float64(1.6),
Timestamp: ts,
},
nil,
{
Label: []*dto.LabelPair{
{Name: proto.String("id"), Value: proto.String("3")},
},
Value: proto.Float64(4),
Timestamp: ts,
},
{
Label: []*dto.LabelPair{
{Name: proto.String("id"), Value: proto.String("4")},
},
Value: proto.Float64(4.5),
Timestamp: ts,
},
}
histogram.ObserveWithExemplar(1.5, Labels{"id": "1"})
histogram.ObserveWithExemplar(1.6, Labels{"id": "2"}) // To replace exemplar in bucket 0.
histogram.ObserveWithExemplar(4, Labels{"id": "3"})
histogram.ObserveWithExemplar(4.5, Labels{"id": "4"}) // Should go to +Inf bucket.
for i, ex := range histogram.exemplars {
var got, expected string
if val := ex.Load(); val != nil {
got = val.(*dto.Exemplar).String()
}
if expectedExemplars[i] != nil {
expected = expectedExemplars[i].String()
}
if got != expected {
t.Errorf("expected exemplar %s, got %s.", expected, got)
}
}
}
func TestNativeHistogram(t *testing.T) {
scenarios := []struct {
name string
observations []float64 // With simulated interval of 1m.
factor float64
zeroThreshold float64
maxBuckets uint32
minResetDuration time.Duration
maxZeroThreshold float64
want string // String representation of protobuf.
}{
{
name: "no sparse buckets",
observations: []float64{1, 2, 3},
factor: 1,
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.
},
{
name: "factor 1.1 results in schema 3",
observations: []float64{0, 1, 2, 3},
factor: 1.1,
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 `,
},
{
name: "factor 1.2 results in schema 2",
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 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
},
{
name: "factor 4 results in schema -1",
observations: []float64{
0.5, 1, // Bucket 0: (0.25, 1]
1.5, 2, 3, 3.5, // Bucket 1: (1, 4]
5, 6, 7, // Bucket 2: (4, 16]
33.33, // Bucket 3: (16, 64]
},
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 TestNativeHistogramConcurrency(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_native_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)
}
}
}