mirror of https://github.com/status-im/op-geth.git
365 lines
8.5 KiB
Go
365 lines
8.5 KiB
Go
package metrics
|
|
|
|
import (
|
|
"math"
|
|
"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(); size != 10 {
|
|
t.Errorf("s.Count(): 10 != %v\n", size)
|
|
}
|
|
if size := s.Size(); size != 10 {
|
|
t.Errorf("s.Size(): 10 != %v\n", size)
|
|
}
|
|
if l := len(s.Values()); l != 10 {
|
|
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(); size != 100 {
|
|
t.Errorf("s.Count(): 100 != %v\n", size)
|
|
}
|
|
if size := s.Size(); size != 100 {
|
|
t.Errorf("s.Size(): 100 != %v\n", size)
|
|
}
|
|
if l := len(s.Values()); l != 100 {
|
|
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(); size != 1000 {
|
|
t.Errorf("s.Count(): 1000 != %v\n", size)
|
|
}
|
|
if size := s.Size(); size != 100 {
|
|
t.Errorf("s.Size(): 100 != %v\n", size)
|
|
}
|
|
if l := len(s.Values()); l != 100 {
|
|
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(); size != 1000 {
|
|
t.Errorf("s.Count(): 1000 != %v\n", size)
|
|
}
|
|
if size := s.Size(); size != 100 {
|
|
t.Errorf("s.Size(): 100 != %v\n", size)
|
|
}
|
|
if l := len(s.Values()); l != 100 {
|
|
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(); count != 10000 {
|
|
t.Errorf("s.Count(): 10000 != %v\n", count)
|
|
}
|
|
if min := s.Min(); min != 107 {
|
|
t.Errorf("s.Min(): 107 != %v\n", min)
|
|
}
|
|
if max := s.Max(); max != 10000 {
|
|
t.Errorf("s.Max(): 10000 != %v\n", max)
|
|
}
|
|
if mean := s.Mean(); mean != 4965.98 {
|
|
t.Errorf("s.Mean(): 4965.98 != %v\n", mean)
|
|
}
|
|
if stdDev := s.StdDev(); stdDev != 2959.825156930727 {
|
|
t.Errorf("s.StdDev(): 2959.825156930727 != %v\n", stdDev)
|
|
}
|
|
ps := s.Percentiles([]float64{0.5, 0.75, 0.99})
|
|
if ps[0] != 4615 {
|
|
t.Errorf("median: 4615 != %v\n", ps[0])
|
|
}
|
|
if ps[1] != 7672 {
|
|
t.Errorf("75th percentile: 7672 != %v\n", ps[1])
|
|
}
|
|
if ps[2] != 9998.99 {
|
|
t.Errorf("99th percentile: 9998.99 != %v\n", ps[2])
|
|
}
|
|
}
|
|
|
|
func testUniformSampleStatistics(t *testing.T, s Sample) {
|
|
if count := s.Count(); count != 10000 {
|
|
t.Errorf("s.Count(): 10000 != %v\n", count)
|
|
}
|
|
if min := s.Min(); min != 37 {
|
|
t.Errorf("s.Min(): 37 != %v\n", min)
|
|
}
|
|
if max := s.Max(); max != 9989 {
|
|
t.Errorf("s.Max(): 9989 != %v\n", max)
|
|
}
|
|
if mean := s.Mean(); mean != 4748.14 {
|
|
t.Errorf("s.Mean(): 4748.14 != %v\n", mean)
|
|
}
|
|
if stdDev := s.StdDev(); stdDev != 2826.684117548333 {
|
|
t.Errorf("s.StdDev(): 2826.684117548333 != %v\n", stdDev)
|
|
}
|
|
ps := s.Percentiles([]float64{0.5, 0.75, 0.99})
|
|
if ps[0] != 4599 {
|
|
t.Errorf("median: 4599 != %v\n", ps[0])
|
|
}
|
|
if ps[1] != 7380.5 {
|
|
t.Errorf("75th percentile: 7380.5 != %v\n", ps[1])
|
|
}
|
|
if math.Abs(9986.429999999998-ps[2]) > epsilonPercentile {
|
|
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{}{}
|
|
}
|