client_golang/prometheus/histogram_test.go

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// 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"
Fix float64 comparison test failure on archs using FMA (#1133) * Fix float64 comparison test failure on archs using FMA Architectures using FMA optimization yield slightly different results so we cannot assume floating point values will be precisely the same across different architectures. The solution in this change is to check "abs(a-b) < tolerance" instead of comparing the exact values. This will give us confidence that the histogram buckets are near identical. Signed-off-by: Seth Bunce <seth.bunce@getcruise.com> * Apply suggestions from code review Co-authored-by: Daniel Swarbrick <daniel.swarbrick@gmail.com> Signed-off-by: Seth Bunce <seth.bunce@getcruise.com> * copy float compare dependency Per discussion in the pull request, we'd like to avoid having an extra dependency on a float comparison package. Instead, we copy the float compare functions from the float comparison package. The float comparison package we're choosing is this. The author of this package has commented in the pull request and it looks like we have consensus that this is the best option. github.com/beorn7/floats Signed-off-by: Seth Bunce <seth.bunce@gmail.com> * remove float32 variant, relocate into separate file This change removes the float32 variant of the AlmostEqual funcs, that we will likely never use. This change also relocates the function into a separate file to avoid modifying a file that's a fork of another vendored package. Signed-off-by: Seth Bunce <seth.bunce@gmail.com> Signed-off-by: Seth Bunce <seth.bunce@getcruise.com> Signed-off-by: Seth Bunce <seth.bunce@gmail.com> Co-authored-by: Daniel Swarbrick <daniel.swarbrick@gmail.com>
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"github.com/prometheus/client_golang/prometheus/internal"
2015-02-27 18:12:59 +03:00
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.")
}
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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{
Fix float64 comparison test failure on archs using FMA (#1133) * Fix float64 comparison test failure on archs using FMA Architectures using FMA optimization yield slightly different results so we cannot assume floating point values will be precisely the same across different architectures. The solution in this change is to check "abs(a-b) < tolerance" instead of comparing the exact values. This will give us confidence that the histogram buckets are near identical. Signed-off-by: Seth Bunce <seth.bunce@getcruise.com> * Apply suggestions from code review Co-authored-by: Daniel Swarbrick <daniel.swarbrick@gmail.com> Signed-off-by: Seth Bunce <seth.bunce@getcruise.com> * copy float compare dependency Per discussion in the pull request, we'd like to avoid having an extra dependency on a float comparison package. Instead, we copy the float compare functions from the float comparison package. The float comparison package we're choosing is this. The author of this package has commented in the pull request and it looks like we have consensus that this is the best option. github.com/beorn7/floats Signed-off-by: Seth Bunce <seth.bunce@gmail.com> * remove float32 variant, relocate into separate file This change removes the float32 variant of the AlmostEqual funcs, that we will likely never use. This change also relocates the function into a separate file to avoid modifying a file that's a fork of another vendored package. Signed-off-by: Seth Bunce <seth.bunce@gmail.com> Signed-off-by: Seth Bunce <seth.bunce@getcruise.com> Signed-off-by: Seth Bunce <seth.bunce@gmail.com> Co-authored-by: Daniel Swarbrick <daniel.swarbrick@gmail.com>
2022-11-07 21:20:43 +03:00
1.0, 1.6681, 2.7825, 4.6415, 7.7426, 12.9154, 21.5443,
35.9381, 59.9484, 100.0000,
}
Fix float64 comparison test failure on archs using FMA (#1133) * Fix float64 comparison test failure on archs using FMA Architectures using FMA optimization yield slightly different results so we cannot assume floating point values will be precisely the same across different architectures. The solution in this change is to check "abs(a-b) < tolerance" instead of comparing the exact values. This will give us confidence that the histogram buckets are near identical. Signed-off-by: Seth Bunce <seth.bunce@getcruise.com> * Apply suggestions from code review Co-authored-by: Daniel Swarbrick <daniel.swarbrick@gmail.com> Signed-off-by: Seth Bunce <seth.bunce@getcruise.com> * copy float compare dependency Per discussion in the pull request, we'd like to avoid having an extra dependency on a float comparison package. Instead, we copy the float compare functions from the float comparison package. The float comparison package we're choosing is this. The author of this package has commented in the pull request and it looks like we have consensus that this is the best option. github.com/beorn7/floats Signed-off-by: Seth Bunce <seth.bunce@gmail.com> * remove float32 variant, relocate into separate file This change removes the float32 variant of the AlmostEqual funcs, that we will likely never use. This change also relocates the function into a separate file to avoid modifying a file that's a fork of another vendored package. Signed-off-by: Seth Bunce <seth.bunce@gmail.com> Signed-off-by: Seth Bunce <seth.bunce@getcruise.com> Signed-off-by: Seth Bunce <seth.bunce@gmail.com> Co-authored-by: Daniel Swarbrick <daniel.swarbrick@gmail.com>
2022-11-07 21:20:43 +03:00
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) }()
Make Histogram observations atomic while keeping them lock-free Fixes #275 This is rather tricky and required some studying of the Go memory model. I have added copious code comments to explain what's going on. Benchmarks haven't changed significantly, despite the additional atomic operations now required during Observe. Write performance is noticable, but it is also much more involved now and has a mutex. (But note that Write is supposed to be a relatively rare operation and thus not in the hot path compared to Observe.) Allocs haven't changed at all. OLD: BenchmarkHistogramWithLabelValues-4 10000000 151 ns/op 0 B/op 0 allocs/op BenchmarkHistogramNoLabels-4 50000000 36.0 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve1-4 50000000 28.1 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve2-4 10000000 160 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve4-4 5000000 378 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve8-4 2000000 768 ns/op 0 B/op 0 allocs/op BenchmarkHistogramWrite1-4 1000000 1589 ns/op 896 B/op 37 allocs/op BenchmarkHistogramWrite2-4 500000 2973 ns/op 1792 B/op 74 allocs/op BenchmarkHistogramWrite4-4 300000 6979 ns/op 3584 B/op 148 allocs/op BenchmarkHistogramWrite8-4 100000 10701 ns/op 7168 B/op 296 allocs/op NEW: BenchmarkHistogramWithLabelValues-4 10000000 191 ns/op 0 B/op 0 allocs/op BenchmarkHistogramNoLabels-4 30000000 50.1 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve1-4 30000000 40.0 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve2-4 20000000 91.5 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve4-4 5000000 317 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve8-4 2000000 636 ns/op 0 B/op 0 allocs/op BenchmarkHistogramWrite1-4 1000000 2072 ns/op 896 B/op 37 allocs/op BenchmarkHistogramWrite2-4 300000 3729 ns/op 1792 B/op 74 allocs/op BenchmarkHistogramWrite4-4 200000 7847 ns/op 3584 B/op 148 allocs/op BenchmarkHistogramWrite8-4 100000 16975 ns/op 7168 B/op 296 allocs/op Signed-off-by: beorn7 <beorn@soundcloud.com>
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observe := func() {
for {
select {
case <-quit:
return
default:
his.Observe(1)
}
}
Make Histogram observations atomic while keeping them lock-free Fixes #275 This is rather tricky and required some studying of the Go memory model. I have added copious code comments to explain what's going on. Benchmarks haven't changed significantly, despite the additional atomic operations now required during Observe. Write performance is noticable, but it is also much more involved now and has a mutex. (But note that Write is supposed to be a relatively rare operation and thus not in the hot path compared to Observe.) Allocs haven't changed at all. OLD: BenchmarkHistogramWithLabelValues-4 10000000 151 ns/op 0 B/op 0 allocs/op BenchmarkHistogramNoLabels-4 50000000 36.0 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve1-4 50000000 28.1 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve2-4 10000000 160 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve4-4 5000000 378 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve8-4 2000000 768 ns/op 0 B/op 0 allocs/op BenchmarkHistogramWrite1-4 1000000 1589 ns/op 896 B/op 37 allocs/op BenchmarkHistogramWrite2-4 500000 2973 ns/op 1792 B/op 74 allocs/op BenchmarkHistogramWrite4-4 300000 6979 ns/op 3584 B/op 148 allocs/op BenchmarkHistogramWrite8-4 100000 10701 ns/op 7168 B/op 296 allocs/op NEW: BenchmarkHistogramWithLabelValues-4 10000000 191 ns/op 0 B/op 0 allocs/op BenchmarkHistogramNoLabels-4 30000000 50.1 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve1-4 30000000 40.0 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve2-4 20000000 91.5 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve4-4 5000000 317 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve8-4 2000000 636 ns/op 0 B/op 0 allocs/op BenchmarkHistogramWrite1-4 1000000 2072 ns/op 896 B/op 37 allocs/op BenchmarkHistogramWrite2-4 300000 3729 ns/op 1792 B/op 74 allocs/op BenchmarkHistogramWrite4-4 200000 7847 ns/op 3584 B/op 148 allocs/op BenchmarkHistogramWrite8-4 100000 16975 ns/op 7168 B/op 296 allocs/op Signed-off-by: beorn7 <beorn@soundcloud.com>
2018-09-07 17:20:30 +03:00
}
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()) ||
Make Histogram observations atomic while keeping them lock-free Fixes #275 This is rather tricky and required some studying of the Go memory model. I have added copious code comments to explain what's going on. Benchmarks haven't changed significantly, despite the additional atomic operations now required during Observe. Write performance is noticable, but it is also much more involved now and has a mutex. (But note that Write is supposed to be a relatively rare operation and thus not in the hot path compared to Observe.) Allocs haven't changed at all. OLD: BenchmarkHistogramWithLabelValues-4 10000000 151 ns/op 0 B/op 0 allocs/op BenchmarkHistogramNoLabels-4 50000000 36.0 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve1-4 50000000 28.1 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve2-4 10000000 160 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve4-4 5000000 378 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve8-4 2000000 768 ns/op 0 B/op 0 allocs/op BenchmarkHistogramWrite1-4 1000000 1589 ns/op 896 B/op 37 allocs/op BenchmarkHistogramWrite2-4 500000 2973 ns/op 1792 B/op 74 allocs/op BenchmarkHistogramWrite4-4 300000 6979 ns/op 3584 B/op 148 allocs/op BenchmarkHistogramWrite8-4 100000 10701 ns/op 7168 B/op 296 allocs/op NEW: BenchmarkHistogramWithLabelValues-4 10000000 191 ns/op 0 B/op 0 allocs/op BenchmarkHistogramNoLabels-4 30000000 50.1 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve1-4 30000000 40.0 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve2-4 20000000 91.5 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve4-4 5000000 317 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve8-4 2000000 636 ns/op 0 B/op 0 allocs/op BenchmarkHistogramWrite1-4 1000000 2072 ns/op 896 B/op 37 allocs/op BenchmarkHistogramWrite2-4 300000 3729 ns/op 1792 B/op 74 allocs/op BenchmarkHistogramWrite4-4 200000 7847 ns/op 3584 B/op 148 allocs/op BenchmarkHistogramWrite8-4 100000 16975 ns/op 7168 B/op 296 allocs/op Signed-off-by: beorn7 <beorn@soundcloud.com>
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h.GetSampleCount() != h.GetBucket()[1].GetCumulativeCount() ||
h.GetSampleCount() != h.GetBucket()[2].GetCumulativeCount() {
t.Fatalf(
Make Histogram observations atomic while keeping them lock-free Fixes #275 This is rather tricky and required some studying of the Go memory model. I have added copious code comments to explain what's going on. Benchmarks haven't changed significantly, despite the additional atomic operations now required during Observe. Write performance is noticable, but it is also much more involved now and has a mutex. (But note that Write is supposed to be a relatively rare operation and thus not in the hot path compared to Observe.) Allocs haven't changed at all. OLD: BenchmarkHistogramWithLabelValues-4 10000000 151 ns/op 0 B/op 0 allocs/op BenchmarkHistogramNoLabels-4 50000000 36.0 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve1-4 50000000 28.1 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve2-4 10000000 160 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve4-4 5000000 378 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve8-4 2000000 768 ns/op 0 B/op 0 allocs/op BenchmarkHistogramWrite1-4 1000000 1589 ns/op 896 B/op 37 allocs/op BenchmarkHistogramWrite2-4 500000 2973 ns/op 1792 B/op 74 allocs/op BenchmarkHistogramWrite4-4 300000 6979 ns/op 3584 B/op 148 allocs/op BenchmarkHistogramWrite8-4 100000 10701 ns/op 7168 B/op 296 allocs/op NEW: BenchmarkHistogramWithLabelValues-4 10000000 191 ns/op 0 B/op 0 allocs/op BenchmarkHistogramNoLabels-4 30000000 50.1 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve1-4 30000000 40.0 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve2-4 20000000 91.5 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve4-4 5000000 317 ns/op 0 B/op 0 allocs/op BenchmarkHistogramObserve8-4 2000000 636 ns/op 0 B/op 0 allocs/op BenchmarkHistogramWrite1-4 1000000 2072 ns/op 896 B/op 37 allocs/op BenchmarkHistogramWrite2-4 300000 3729 ns/op 1792 B/op 74 allocs/op BenchmarkHistogramWrite4-4 200000 7847 ns/op 3584 B/op 148 allocs/op BenchmarkHistogramWrite8-4 100000 16975 ns/op 7168 B/op 296 allocs/op Signed-off-by: beorn7 <beorn@soundcloud.com>
2018-09-07 17:20:30 +03:00
"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 TestSparseHistogram(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 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)
}
}
}