1 files changed, 363 insertions, 0 deletions
diff --git a/Godeps/_workspace/src/github.com/rcrowley/go-metrics/sample_test.go b/Godeps/_workspace/src/github.com/rcrowley/go-metrics/sample_test.go
new file mode 100644
index 000000000..d60e99c5b
--- /dev/null
+++ b/Godeps/_workspace/src/github.com/rcrowley/go-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{}{}
+}