352 lines
8.2 KiB
Go
352 lines
8.2 KiB
Go
package metrics
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import (
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"math/rand"
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"runtime"
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"testing"
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"time"
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)
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// Benchmark{Compute,Copy}{1000,1000000} demonstrate that, even for relatively
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// expensive computations like Variance, the cost of copying the Sample, as
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// approximated by a make and copy, is much greater than the cost of the
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// computation for small samples and only slightly less for large samples.
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func BenchmarkCompute1000(b *testing.B) {
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s := make([]int64, 1000)
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for i := 0; i < len(s); i++ {
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s[i] = int64(i)
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}
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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SampleVariance(s)
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}
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}
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func BenchmarkCompute1000000(b *testing.B) {
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s := make([]int64, 1000000)
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for i := 0; i < len(s); i++ {
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s[i] = int64(i)
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}
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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SampleVariance(s)
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}
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}
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func BenchmarkCopy1000(b *testing.B) {
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s := make([]int64, 1000)
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for i := 0; i < len(s); i++ {
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s[i] = int64(i)
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}
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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sCopy := make([]int64, len(s))
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copy(sCopy, s)
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}
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}
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func BenchmarkCopy1000000(b *testing.B) {
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s := make([]int64, 1000000)
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for i := 0; i < len(s); i++ {
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s[i] = int64(i)
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}
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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sCopy := make([]int64, len(s))
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copy(sCopy, s)
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}
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}
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func BenchmarkExpDecaySample257(b *testing.B) {
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benchmarkSample(b, NewExpDecaySample(257, 0.015))
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}
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func BenchmarkExpDecaySample514(b *testing.B) {
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benchmarkSample(b, NewExpDecaySample(514, 0.015))
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}
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func BenchmarkExpDecaySample1028(b *testing.B) {
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benchmarkSample(b, NewExpDecaySample(1028, 0.015))
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}
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func BenchmarkUniformSample257(b *testing.B) {
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benchmarkSample(b, NewUniformSample(257))
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}
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func BenchmarkUniformSample514(b *testing.B) {
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benchmarkSample(b, NewUniformSample(514))
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}
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func BenchmarkUniformSample1028(b *testing.B) {
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benchmarkSample(b, NewUniformSample(1028))
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}
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func TestExpDecaySample10(t *testing.T) {
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rand.Seed(1)
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s := NewExpDecaySample(100, 0.99)
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for i := 0; i < 10; i++ {
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s.Update(int64(i))
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}
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if size := s.Count(); 10 != size {
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t.Errorf("s.Count(): 10 != %v\n", size)
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}
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if size := s.Size(); 10 != size {
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t.Errorf("s.Size(): 10 != %v\n", size)
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}
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if l := len(s.Values()); 10 != l {
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t.Errorf("len(s.Values()): 10 != %v\n", l)
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}
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for _, v := range s.Values() {
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if v > 10 || v < 0 {
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t.Errorf("out of range [0, 10): %v\n", v)
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}
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}
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}
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func TestExpDecaySample100(t *testing.T) {
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rand.Seed(1)
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s := NewExpDecaySample(1000, 0.01)
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for i := 0; i < 100; i++ {
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s.Update(int64(i))
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}
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if size := s.Count(); 100 != size {
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t.Errorf("s.Count(): 100 != %v\n", size)
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}
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if size := s.Size(); 100 != size {
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t.Errorf("s.Size(): 100 != %v\n", size)
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}
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if l := len(s.Values()); 100 != l {
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t.Errorf("len(s.Values()): 100 != %v\n", l)
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}
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for _, v := range s.Values() {
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if v > 100 || v < 0 {
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t.Errorf("out of range [0, 100): %v\n", v)
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}
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}
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}
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func TestExpDecaySample1000(t *testing.T) {
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rand.Seed(1)
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s := NewExpDecaySample(100, 0.99)
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for i := 0; i < 1000; i++ {
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s.Update(int64(i))
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}
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if size := s.Count(); 1000 != size {
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t.Errorf("s.Count(): 1000 != %v\n", size)
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}
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if size := s.Size(); 100 != size {
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t.Errorf("s.Size(): 100 != %v\n", size)
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}
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if l := len(s.Values()); 100 != l {
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t.Errorf("len(s.Values()): 100 != %v\n", l)
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}
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for _, v := range s.Values() {
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if v > 1000 || v < 0 {
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t.Errorf("out of range [0, 1000): %v\n", v)
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}
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}
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}
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// This test makes sure that the sample's priority is not amplified by using
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// nanosecond duration since start rather than second duration since start.
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// The priority becomes +Inf quickly after starting if this is done,
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// effectively freezing the set of samples until a rescale step happens.
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func TestExpDecaySampleNanosecondRegression(t *testing.T) {
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rand.Seed(1)
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s := NewExpDecaySample(100, 0.99)
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for i := 0; i < 100; i++ {
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s.Update(10)
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}
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time.Sleep(1 * time.Millisecond)
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for i := 0; i < 100; i++ {
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s.Update(20)
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}
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v := s.Values()
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avg := float64(0)
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for i := 0; i < len(v); i++ {
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avg += float64(v[i])
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}
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avg /= float64(len(v))
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if avg > 16 || avg < 14 {
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t.Errorf("out of range [14, 16]: %v\n", avg)
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}
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}
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func TestExpDecaySampleSnapshot(t *testing.T) {
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now := time.Now()
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rand.Seed(1)
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s := NewExpDecaySample(100, 0.99)
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for i := 1; i <= 10000; i++ {
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s.(*ExpDecaySample).update(now.Add(time.Duration(i)), int64(i))
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}
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snapshot := s.Snapshot()
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s.Update(1)
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testExpDecaySampleStatistics(t, snapshot)
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}
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func TestExpDecaySampleStatistics(t *testing.T) {
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now := time.Now()
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rand.Seed(1)
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s := NewExpDecaySample(100, 0.99)
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for i := 1; i <= 10000; i++ {
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s.(*ExpDecaySample).update(now.Add(time.Duration(i)), int64(i))
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}
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testExpDecaySampleStatistics(t, s)
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}
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func TestUniformSample(t *testing.T) {
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rand.Seed(1)
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s := NewUniformSample(100)
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for i := 0; i < 1000; i++ {
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s.Update(int64(i))
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}
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if size := s.Count(); 1000 != size {
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t.Errorf("s.Count(): 1000 != %v\n", size)
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}
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if size := s.Size(); 100 != size {
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t.Errorf("s.Size(): 100 != %v\n", size)
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}
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if l := len(s.Values()); 100 != l {
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t.Errorf("len(s.Values()): 100 != %v\n", l)
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}
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for _, v := range s.Values() {
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if v > 1000 || v < 0 {
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t.Errorf("out of range [0, 100): %v\n", v)
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}
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}
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}
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func TestUniformSampleIncludesTail(t *testing.T) {
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rand.Seed(1)
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s := NewUniformSample(100)
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max := 100
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for i := 0; i < max; i++ {
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s.Update(int64(i))
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}
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v := s.Values()
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sum := 0
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exp := (max - 1) * max / 2
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for i := 0; i < len(v); i++ {
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sum += int(v[i])
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}
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if exp != sum {
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t.Errorf("sum: %v != %v\n", exp, sum)
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}
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}
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func TestUniformSampleSnapshot(t *testing.T) {
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s := NewUniformSample(100)
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for i := 1; i <= 10000; i++ {
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s.Update(int64(i))
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}
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snapshot := s.Snapshot()
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s.Update(1)
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testUniformSampleStatistics(t, snapshot)
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}
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func TestUniformSampleStatistics(t *testing.T) {
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rand.Seed(1)
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s := NewUniformSample(100)
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for i := 1; i <= 10000; i++ {
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s.Update(int64(i))
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}
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testUniformSampleStatistics(t, s)
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}
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func benchmarkSample(b *testing.B, s Sample) {
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var memStats runtime.MemStats
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runtime.ReadMemStats(&memStats)
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pauseTotalNs := memStats.PauseTotalNs
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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s.Update(1)
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}
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b.StopTimer()
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runtime.GC()
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runtime.ReadMemStats(&memStats)
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b.Logf("GC cost: %d ns/op", int(memStats.PauseTotalNs-pauseTotalNs)/b.N)
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}
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func testExpDecaySampleStatistics(t *testing.T, s Sample) {
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if count := s.Count(); 10000 != count {
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t.Errorf("s.Count(): 10000 != %v\n", count)
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}
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if min := s.Min(); 107 != min {
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t.Errorf("s.Min(): 107 != %v\n", min)
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}
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if max := s.Max(); 10000 != max {
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t.Errorf("s.Max(): 10000 != %v\n", max)
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}
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if mean := s.Mean(); 4965.98 != mean {
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t.Errorf("s.Mean(): 4965.98 != %v\n", mean)
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}
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if stdDev := s.StdDev(); 2959.825156930727 != stdDev {
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t.Errorf("s.StdDev(): 2959.825156930727 != %v\n", stdDev)
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}
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ps := s.Percentiles([]float64{0.5, 0.75, 0.99})
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if 4615 != ps[0] {
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t.Errorf("median: 4615 != %v\n", ps[0])
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}
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if 7672 != ps[1] {
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t.Errorf("75th percentile: 7672 != %v\n", ps[1])
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}
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if 9998.99 != ps[2] {
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t.Errorf("99th percentile: 9998.99 != %v\n", ps[2])
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}
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}
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func testUniformSampleStatistics(t *testing.T, s Sample) {
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if count := s.Count(); 10000 != count {
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t.Errorf("s.Count(): 10000 != %v\n", count)
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}
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if min := s.Min(); 9412 != min {
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t.Errorf("s.Min(): 9412 != %v\n", min)
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}
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if max := s.Max(); 10000 != max {
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t.Errorf("s.Max(): 10000 != %v\n", max)
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}
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if mean := s.Mean(); 9902.26 != mean {
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t.Errorf("s.Mean(): 9902.26 != %v\n", mean)
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}
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if stdDev := s.StdDev(); 101.8667384380201 != stdDev {
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t.Errorf("s.StdDev(): 101.8667384380201 != %v\n", stdDev)
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}
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ps := s.Percentiles([]float64{0.5, 0.75, 0.99})
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if 9930.5 != ps[0] {
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t.Errorf("median: 9930.5 != %v\n", ps[0])
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}
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if 9973.75 != ps[1] {
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t.Errorf("75th percentile: 9973.75 != %v\n", ps[1])
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}
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if 9999.99 != ps[2] {
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t.Errorf("99th percentile: 9999.99 != %v\n", ps[2])
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}
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}
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// TestUniformSampleConcurrentUpdateCount would expose data race problems with
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// concurrent Update and Count calls on Sample when test is called with -race
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// argument
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func TestUniformSampleConcurrentUpdateCount(t *testing.T) {
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if testing.Short() {
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t.Skip("skipping in short mode")
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}
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s := NewUniformSample(100)
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for i := 0; i < 100; i++ {
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s.Update(int64(i))
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}
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quit := make(chan struct{})
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go func() {
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t := time.NewTicker(10 * time.Millisecond)
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for {
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select {
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case <-t.C:
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s.Update(rand.Int63())
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case <-quit:
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t.Stop()
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return
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}
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}
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}()
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for i := 0; i < 1000; i++ {
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s.Count()
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time.Sleep(5 * time.Millisecond)
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}
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quit <- struct{}{}
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}
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