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-rw-r--r--metrics/sample_test.go363
1 files changed, 363 insertions, 0 deletions
diff --git a/metrics/sample_test.go b/metrics/sample_test.go
new file mode 100644
index 000000000..d60e99c5b
--- /dev/null
+++ b/metrics/sample_test.go
@@ -0,0 +1,363 @@
+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{}{}
+}
generated by cgit v1.2.3 (git 2.25.1) at 2025-01-01 19:13:53 +0800