package metrics
import (
"math/rand"
"runtime"
"testing"
"time"
)
// Benchmark{Compute,Copy}{1000,1000000} demonstrate that, even for relatively
// expensive computations like Variance, the cost of copying the Sample, as
// approximated by a make and copy, is much greater than the cost of the
// computation for small samples and only slightly less for large samples.
func BenchmarkCompute1000(b *testing.B) {
s := make([]int64, 1000)
for i := 0; i < len(s); i++ {
s[i] = int64(i)
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
SampleVariance(s)
}
}
func BenchmarkCompute1000000(b *testing.B) {
s := make([]int64, 1000000)
for i := 0; i < len(s); i++ {
s[i] = int64(i)
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
SampleVariance(s)
}
}
func BenchmarkCopy1000(b *testing.B) {
s := make([]int64, 1000)
for i := 0; i < len(s); i++ {
s[i] = int64(i)
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
sCopy := make([]int64, len(s))
copy(sCopy, s)
}
}
func BenchmarkCopy1000000(b *testing.B) {
s := make([]int64, 1000000)
for i := 0; i < len(s); i++ {
s[i] = int64(i)
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
sCopy := make([]int64, len(s))
copy(sCopy, s)
}
}
func BenchmarkExpDecaySample257(b *testing.B) {
benchmarkSample(b, NewExpDecaySample(257, 0.015))
}
func BenchmarkExpDecaySample514(b *testing.B) {
benchmarkSample(b, NewExpDecaySample(514, 0.015))
}
func BenchmarkExpDecaySample1028(b *testing.B) {
benchmarkSample(b, NewExpDecaySample(1028, 0.015))
}
func BenchmarkUniformSample257(b *testing.B) {
benchmarkSample(b, NewUniformSample(257))
}
func BenchmarkUniformSample514(b *testing.B) {
benchmarkSample(b, NewUniformSample(514))
}
func BenchmarkUniformSample1028(b *testing.B) {
benchmarkSample(b, NewUniformSample(1028))
}
func TestExpDecaySample10(t *testing.T) {
rand.Seed(1)
s := NewExpDecaySample(100, 0.99)
for i := 0; i < 10; i++ {
s.Update(int64(i))
}
if size := s.Count(); 10 != size {
t.Errorf("s.Count(): 10 != %v\n", size)
}
if size := s.Size(); 10 != size {
t.Errorf("s.Size(): 10 != %v\n", size)
}
if l := len(s.Values()); 10 != l {
t.Errorf("len(s.Values()): 10 != %v\n", l)
}
for _, v := range s.Values() {
if v > 10 || v < 0 {
t.Errorf("out of range [0, 10): %v\n", v)
}
}
}
func TestExpDecaySample100(t *testing.T) {
rand.Seed(1)
s := NewExpDecaySample(1000, 0.01)
for i := 0; i < 100; i++ {
s.Update(int64(i))
}
if size := s.Count(); 100 != size {
t.Errorf("s.Count(): 100 != %v\n", size)
}
if size := s.Size(); 100 != size {
t.Errorf("s.Size(): 100 != %v\n", size)
}
if l := len(s.Values()); 100 != l {
t.Errorf("len(s.Values()): 100 != %v\n", l)
}
for _, v := range s.Values() {
if v > 100 || v < 0 {
t.Errorf("out of range [0, 100): %v\n", v)
}
}
}
func TestExpDecaySample1000(t *testing.T) {
rand.Seed(1)
s := NewExpDecaySample(100, 0.99)
for i := 0; i < 1000; i++ {
s.Update(int64(i))
}
if size := s.Count(); 1000 != size {
t.Errorf("s.Count(): 1000 != %v\n", size)
}
if size := s.Size(); 100 != size {
t.Errorf("s.Size(): 100 != %v\n", size)
}
if l := len(s.Values()); 100 != l {
t.Errorf("len(s.Values()): 100 != %v\n", l)
}
for _, v := range s.Values() {
if v > 1000 || v < 0 {
t.Errorf("out of range [0, 1000): %v\n", v)
}
}
}
// This test makes sure that the sample's priority is not amplified by using
// nanosecond duration since start rather than second duration since start.
// The priority becomes +Inf quickly after starting if this is done,
// effectively freezing the set of samples until a rescale step happens.
func TestExpDecaySampleNanosecondRegression(t *testing.T) {
rand.Seed(1)
s := NewExpDecaySample(100, 0.99)
for i := 0; i < 100; i++ {
s.Update(10)
}
time.Sleep(1 * time.Millisecond)
for i := 0; i < 100; i++ {
s.Update(20)
}
v := s.Values()
avg := float64(0)
for i := 0; i < len(v); i++ {
avg += float64(v[i])
}
avg /= float64(len(v))
if avg > 16 || avg < 14 {
t.Errorf("out of range [14, 16]: %v\n", avg)
}
}
func TestExpDecaySampleRescale(t *testing.T) {
s := NewExpDecaySample(2, 0.001).(*ExpDecaySample)
s.update(time.Now(), 1)
s.update(time.Now().Add(time.Hour+time.Microsecond), 1)
for _, v := range s.values.Values() {
if v.k == 0.0 {
t.Fatal("v.k == 0.0")
}
}
}
func TestExpDecaySampleSnapshot(t *testing.T) {
now := time.Now()
rand.Seed(1)
s := NewExpDecaySample(100, 0.99)
for i := 1; i <= 10000; i++ {
s.(*ExpDecaySample).update(now.Add(time.Duration(i)), int64(i))
}
snapshot := s.Snapshot()
s.Update(1)
testExpDecaySampleStatistics(t, snapshot)
}
func TestExpDecaySampleStatistics(t *testing.T) {
now := time.Now()
rand.Seed(1)
s := NewExpDecaySample(100, 0.99)
for i := 1; i <= 10000; i++ {
s.(*ExpDecaySample).update(now.Add(time.Duration(i)), int64(i))
}
testExpDecaySampleStatistics(t, s)
}
func TestUniformSample(t *testing.T) {
rand.Seed(1)
s := NewUniformSample(100)
for i := 0; i < 1000; i++ {
s.Update(int64(i))
}
if size := s.Count(); 1000 != size {
t.Errorf("s.Count(): 1000 != %v\n", size)
}
if size := s.Size(); 100 != size {
t.Errorf("s.Size(): 100 != %v\n", size)
}
if l := len(s.Values()); 100 != l {
t.Errorf("len(s.Values()): 100 != %v\n", l)
}
for _, v := range s.Values() {
if v > 1000 || v < 0 {
t.Errorf("out of range [0, 100): %v\n", v)
}
}
}
func TestUniformSampleIncludesTail(t *testing.T) {
rand.Seed(1)
s := NewUniformSample(100)
max := 100
for i := 0; i < max; i++ {
s.Update(int64(i))
}
v := s.Values()
sum := 0
exp := (max - 1) * max / 2
for i := 0; i < len(v); i++ {
sum += int(v[i])
}
if exp != sum {
t.Errorf("sum: %v != %v\n", exp, sum)
}
}
func TestUniformSampleSnapshot(t *testing.T) {
s := NewUniformSample(100)
for i := 1; i <= 10000; i++ {
s.Update(int64(i))
}
snapshot := s.Snapshot()
s.Update(1)
testUniformSampleStatistics(t, snapshot)
}
func TestUniformSampleStatistics(t *testing.T) {
rand.Seed(1)
s := NewUniformSample(100)
for i := 1; i <= 10000; i++ {
s.Update(int64(i))
}
testUniformSampleStatistics(t, s)
}
func benchmarkSample(b *testing.B, s Sample) {
var memStats runtime.MemStats
runtime.ReadMemStats(&memStats)
pauseTotalNs := memStats.PauseTotalNs
b.ResetTimer()
for i := 0; i < b.N; i++ {
s.Update(1)
}
b.StopTimer()
runtime.GC()
runtime.ReadMemStats(&memStats)
b.Logf("GC cost: %d ns/op", int(memStats.PauseTotalNs-pauseTotalNs)/b.N)
}
func testExpDecaySampleStatistics(t *testing.T, s Sample) {
if count := s.Count(); 10000 != count {
t.Errorf("s.Count(): 10000 != %v\n", count)
}
if min := s.Min(); 107 != min {
t.Errorf("s.Min(): 107 != %v\n", min)
}
if max := s.Max(); 10000 != max {
t.Errorf("s.Max(): 10000 != %v\n", max)
}
if mean := s.Mean(); 4965.98 != mean {
t.Errorf("s.Mean(): 4965.98 != %v\n", mean)
}
if stdDev := s.StdDev(); 2959.825156930727 != stdDev {
t.Errorf("s.StdDev(): 2959.825156930727 != %v\n", stdDev)
}
ps := s.Percentiles([]float64{0.5, 0.75, 0.99})
if 4615 != ps[0] {
t.Errorf("median: 4615 != %v\n", ps[0])
}
if 7672 != ps[1] {
t.Errorf("75th percentile: 7672 != %v\n", ps[1])
}
if 9998.99 != ps[2] {
t.Errorf("99th percentile: 9998.99 != %v\n", ps[2])
}
}
func testUniformSampleStatistics(t *testing.T, s Sample) {
if count := s.Count(); 10000 != count {
t.Errorf("s.Count(): 10000 != %v\n", count)
}
if min := s.Min(); 37 != min {
t.Errorf("s.Min(): 37 != %v\n", min)
}
if max := s.Max(); 9989 != max {
t.Errorf("s.Max(): 9989 != %v\n", max)
}
if mean := s.Mean(); 4748.14 != mean {
t.Errorf("s.Mean(): 4748.14 != %v\n", mean)
}
if stdDev := s.StdDev(); 2826.684117548333 != stdDev {
t.Errorf("s.StdDev(): 2826.684117548333 != %v\n", stdDev)
}
ps := s.Percentiles([]float64{0.5, 0.75, 0.99})
if 4599 != ps[0] {
t.Errorf("median: 4599 != %v\n", ps[0])
}
if 7380.5 != ps[1] {
t.Errorf("75th percentile: 7380.5 != %v\n", ps[1])
}
if 9986.429999999998 != ps[2] {
t.Errorf("99th percentile: 9986.429999999998 != %v\n", ps[2])
}
}
// TestUniformSampleConcurrentUpdateCount would expose data race problems with
// concurrent Update and Count calls on Sample when test is called with -race
// argument
func TestUniformSampleConcurrentUpdateCount(t *testing.T) {
if testing.Short() {
t.Skip("skipping in short mode")
}
s := NewUniformSample(100)
for i := 0; i < 100; i++ {
s.Update(int64(i))
}
quit := make(chan struct{})
go func() {
t := time.NewTicker(10 * time.Millisecond)
for {
select {
case <-t.C:
s.Update(rand.Int63())
case <-quit:
t.Stop()
return
}
}
}()
for i := 0; i < 1000; i++ {
s.Count()
time.Sleep(5 * time.Millisecond)
}
quit <- struct{}{}
}
