plugeth/metrics/sample_test.go
Martin Holst Swende 4d3525610e
all: remove deprecated uses of math.rand (#26710)
This PR is a (superior) alternative to https://github.com/ethereum/go-ethereum/pull/26708, it handles deprecation, primarily two specific cases. 

`rand.Seed` is typically used in two ways
- `rand.Seed(time.Now().UnixNano())` -- we seed it, just to be sure to get some random, and not always get the same thing on every run. This is not needed, with global seeding, so those are just removed. 
- `rand.Seed(1)` this is typically done to ensure we have a stable test. If we rely on this, we need to fix up the tests to use a deterministic prng-source. A few occurrences like this has been replaced with a proper custom source. 

`rand.Read` has been replaced by `crypto/rand`.`Read` in this PR.
2023-02-16 14:36:58 -05:00

357 lines
8.6 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) {
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) {
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) {
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) {
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()
s := NewExpDecaySample(100, 0.99).(*ExpDecaySample).SetRand(rand.New(rand.NewSource(1)))
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()
s := NewExpDecaySample(100, 0.99).(*ExpDecaySample).SetRand(rand.New(rand.NewSource(1)))
for i := 1; i <= 10000; i++ {
s.(*ExpDecaySample).update(now.Add(time.Duration(i)), int64(i))
}
testExpDecaySampleStatistics(t, s)
}
func TestUniformSample(t *testing.T) {
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) {
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).(*UniformSample).SetRand(rand.New(rand.NewSource(1)))
for i := 1; i <= 10000; i++ {
s.Update(int64(i))
}
snapshot := s.Snapshot()
s.Update(1)
testUniformSampleStatistics(t, snapshot)
}
func TestUniformSampleStatistics(t *testing.T) {
s := NewUniformSample(100).(*UniformSample).SetRand(rand.New(rand.NewSource(1)))
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)
defer t.Stop()
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{}{}
}