func TestEquals(t *testing.T) {
h1 := hdrhistogram.New(1, 10000000, 3)
for i := 0; i < 1000000; i++ {
if err := h1.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
h2 := hdrhistogram.New(1, 10000000, 3)
for i := 0; i < 10000; i++ {
if err := h1.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
if h1.Equals(h2) {
t.Error("Expected Histograms to not be equivalent")
}
h1.Reset()
h2.Reset()
if !h1.Equals(h2) {
t.Error("Expected Histograms to be equivalent")
}
}
// Runf runs the given job at the given concurrency level, at the given rate,
// returning a set of results with aggregated latency and throughput
// measurements.
func (b Bench) Runf(concurrency int, rate float64, job Job) Result {
var started, finished sync.WaitGroup
started.Add(1)
finished.Add(concurrency)
result := Result{
Concurrency: concurrency,
Latency: hdrhistogram.New(us(b.MinLatency), us(b.MaxLatency), 5),
}
timings := make(chan *hdrhistogram.Histogram, concurrency)
errors := make(chan error, concurrency)
workerRate := float64(concurrency) / rate
period := time.Duration((workerRate)*1000000) * time.Microsecond
for i := 0; i < concurrency; i++ {
go func(id int) {
defer finished.Done()
gen := &Generator{
hist: hdrhistogram.New(us(b.MinLatency), us(b.MaxLatency), 5),
success: &result.Success,
failure: &result.Failure,
period: period,
duration: b.Duration,
warmup: b.Warmup,
}
started.Wait()
errors <- job(id, gen)
timings <- gen.hist
}(i)
}
started.Done()
finished.Wait()
result.Elapsed = b.Duration
close(timings)
for v := range timings {
result.Latency.Merge(v)
}
close(errors)
for e := range errors {
if e != nil {
result.Errors = append(result.Errors, e)
}
}
return result
}
func TestHighestTrackableValue(t *testing.T) {
const maxVal = 11
h := hdrhistogram.New(1, maxVal, 3)
if h.HighestTrackableValue() != maxVal {
t.Errorf("HighestTrackableValue figures was %v, expected %d", h.HighestTrackableValue(), maxVal)
}
}
func TestLowestTrackableValue(t *testing.T) {
const minVal = 2
h := hdrhistogram.New(minVal, 10, 3)
if h.LowestTrackableValue() != minVal {
t.Errorf("LowestTrackableValue figures was %v, expected %d", h.LowestTrackableValue(), minVal)
}
}
func TestByteSize(t *testing.T) {
h := hdrhistogram.New(1, 100000, 3)
if v, want := h.ByteSize(), 65604; v != want {
t.Errorf("ByteSize was %v, but expected %d", v, want)
}
}
func TestCumulativeDistribution(t *testing.T) {
h := hdrhistogram.New(1, 100000000, 3)
for i := 0; i < 1000000; i++ {
if err := h.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
actual := h.CumulativeDistribution()
expected := []hdrhistogram.Bracket{
hdrhistogram.Bracket{Quantile: 0, Count: 1, ValueAt: 0},
hdrhistogram.Bracket{Quantile: 50, Count: 500224, ValueAt: 500223},
hdrhistogram.Bracket{Quantile: 75, Count: 750080, ValueAt: 750079},
hdrhistogram.Bracket{Quantile: 87.5, Count: 875008, ValueAt: 875007},
hdrhistogram.Bracket{Quantile: 93.75, Count: 937984, ValueAt: 937983},
hdrhistogram.Bracket{Quantile: 96.875, Count: 969216, ValueAt: 969215},
hdrhistogram.Bracket{Quantile: 98.4375, Count: 984576, ValueAt: 984575},
hdrhistogram.Bracket{Quantile: 99.21875, Count: 992256, ValueAt: 992255},
hdrhistogram.Bracket{Quantile: 99.609375, Count: 996352, ValueAt: 996351},
hdrhistogram.Bracket{Quantile: 99.8046875, Count: 998400, ValueAt: 998399},
hdrhistogram.Bracket{Quantile: 99.90234375, Count: 999424, ValueAt: 999423},
hdrhistogram.Bracket{Quantile: 99.951171875, Count: 999936, ValueAt: 999935},
hdrhistogram.Bracket{Quantile: 99.9755859375, Count: 999936, ValueAt: 999935},
hdrhistogram.Bracket{Quantile: 99.98779296875, Count: 999936, ValueAt: 999935},
hdrhistogram.Bracket{Quantile: 99.993896484375, Count: 1000000, ValueAt: 1000447},
hdrhistogram.Bracket{Quantile: 100, Count: 1000000, ValueAt: 1000447},
}
if !reflect.DeepEqual(actual, expected) {
t.Errorf("CF was %#v, but expected %#v", actual, expected)
}
}
func BenchmarkNew(b *testing.B) {
b.ReportAllocs()
for i := 0; i < b.N; i++ {
hdrhistogram.New(1, 120000, 3) // this could track 1ms-2min
}
}
func TestSignificantFigures(t *testing.T) {
const sigFigs = 4
h := hdrhistogram.New(1, 10, sigFigs)
if h.SignificantFigures() != sigFigs {
t.Errorf("Significant figures was %v, expected %d", h.SignificantFigures(), sigFigs)
}
}
// aggregate run contexts
func (b *Bench) aggregate() {
// aggregate timer metrics
for _, n := range b.allContextsKeys(timerKeys) {
t := hdrhistogram.New(min, max, precision)
b.timers[n] = t
for i := 0; i < b.concurrentRuns; i++ {
otherTimer, ok := b.runContexts[i].timers[n]
if ok {
t.Merge(otherTimer)
}
}
}
// aggregate counters
for _, n := range b.allContextsKeys(counterKeys) {
for i := 0; i < b.concurrentRuns; i++ {
b.counters[n] += b.runContexts[i].counters[n]
}
}
// aggregate call counts
for i := 0; i < b.concurrentRuns; i++ {
b.calls += b.runContexts[i].Iteration
}
}
func TestValueAtQuantile(t *testing.T) {
h := hdrhistogram.New(1, 10000000, 3)
for i := 0; i < 1000000; i++ {
if err := h.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
data := []struct {
q float64
v int64
}{
{q: 50, v: 500223},
{q: 75, v: 750079},
{q: 90, v: 900095},
{q: 95, v: 950271},
{q: 99, v: 990207},
{q: 99.9, v: 999423},
{q: 99.99, v: 999935},
}
for _, d := range data {
if v := h.ValueAtQuantile(d.q); v != d.v {
t.Errorf("P%v was %v, but expected %v", d.q, v, d.v)
}
}
}
func TestExportImport(t *testing.T) {
min := int64(1)
max := int64(10000000)
sigfigs := 3
h := hdrhistogram.New(min, max, sigfigs)
for i := 0; i < 1000000; i++ {
if err := h.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
s := h.Export()
if v := s.LowestTrackableValue; v != min {
t.Errorf("LowestTrackableValue was %v, but expected %v", v, min)
}
if v := s.HighestTrackableValue; v != max {
t.Errorf("HighestTrackableValue was %v, but expected %v", v, max)
}
if v := int(s.SignificantFigures); v != sigfigs {
t.Errorf("SignificantFigures was %v, but expected %v", v, sigfigs)
}
if imported := hdrhistogram.Import(s); !imported.Equals(h) {
t.Error("Expected Histograms to be equivalent")
}
}
// newConnectionBenchmark creates a connectionBenchmark which runs a system
// benchmark using the given Requester. The requestRate argument specifies the
// number of requests per second to issue. A zero value disables rate limiting
// entirely. The duration argument specifies how long to run the benchmark.
func newConnectionBenchmark(requester Requester, requestRate uint64, duration time.Duration) *connectionBenchmark {
var interval time.Duration
if requestRate > 0 {
interval = time.Duration(1000000000 / requestRate)
}
return &connectionBenchmark{
requester: requester,
requestRate: requestRate,
duration: duration,
expectedInterval: interval,
successHistogram: hdrhistogram.New(1, maxRecordableLatencyNS, sigFigs),
uncorrectedSuccessHistogram: hdrhistogram.New(1, maxRecordableLatencyNS, sigFigs),
errorHistogram: hdrhistogram.New(1, maxRecordableLatencyNS, sigFigs),
uncorrectedErrorHistogram: hdrhistogram.New(1, maxRecordableLatencyNS, sigFigs),
}
}
func TestNaN(t *testing.T) {
h := hdrhistogram.New(1, 100000, 3)
if math.IsNaN(h.Mean()) {
t.Error("mean is NaN")
}
if math.IsNaN(h.StdDev()) {
t.Error("stddev is NaN")
}
}
// NewHistogram initializes a given Histogram. The contained windowed histogram
// rotates every 'duration'; both the windowed and the cumulative histogram
// track nonnegative values up to 'maxVal' with 'sigFigs' decimal points of
// precision.
func NewHistogram(metadata Metadata, duration time.Duration, maxVal int64, sigFigs int) *Histogram {
dHist := newSlidingHistogram(duration, maxVal, sigFigs)
h := &Histogram{
Metadata: metadata,
maxVal: maxVal,
}
h.mu.cumulative = hdrhistogram.New(0, maxVal, sigFigs)
h.mu.sliding = dHist
return h
}
func TestRecordCorrectedValueStall(t *testing.T) {
h := hdrhistogram.New(1, 100000, 3)
if err := h.RecordCorrectedValue(1000, 100); err != nil {
t.Fatal(err)
}
if v, want := h.ValueAtQuantile(75), int64(800); v != want {
t.Errorf("Corrected value was %v, but expected %v", v, want)
}
}
func TestTotalCount(t *testing.T) {
h := hdrhistogram.New(1, 10000000, 3)
for i := 0; i < 1000000; i++ {
if err := h.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
if v, want := h.TotalCount(), int64(i+1); v != want {
t.Errorf("TotalCount was %v, but expected %v", v, want)
}
}
}
func TestMerge(t *testing.T) {
h1 := hdrhistogram.New(1, 1000, 3)
h2 := hdrhistogram.New(1, 1000, 3)
for i := 0; i < 100; i++ {
if err := h1.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
for i := 100; i < 200; i++ {
if err := h2.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
h1.Merge(h2)
if v, want := h1.ValueAtQuantile(50), int64(99); v != want {
t.Errorf("Median was %v, but expected %v", v, want)
}
}
func NewHDRHistogram(low, high int64, sigfigs int) (h *HDRHistogram, err error) {
defer func() {
if msg := recover(); msg != nil {
err = fmt.Errorf("%s", msg)
}
}()
return &HDRHistogram{
low: low,
high: high,
sigfigs: sigfigs,
h: hdrhistogram.New(low, high, sigfigs),
}, nil
}
func TestMax(t *testing.T) {
h := hdrhistogram.New(1, 10000000, 3)
for i := 0; i < 1000000; i++ {
if err := h.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
if v, want := h.Max(), int64(999936); v != want {
t.Errorf("Max was %v, but expected %v", v, want)
}
}
func TestStdDev(t *testing.T) {
h := hdrhistogram.New(1, 10000000, 3)
for i := 0; i < 1000000; i++ {
if err := h.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
if v, want := h.StdDev(), 288675.1403682715; v != want {
t.Errorf("StdDev was %v, but expected %v", v, want)
}
}
func BenchmarkHistogramRecordValue(b *testing.B) {
h := hdrhistogram.New(1, 10000000, 3)
for i := 0; i < 1000000; i++ {
if err := h.RecordValue(int64(i)); err != nil {
b.Fatal(err)
}
}
b.ResetTimer()
b.ReportAllocs()
for i := 0; i < b.N; i++ {
h.RecordValue(100)
}
}
func TestHighSigFig(t *testing.T) {
input := []int64{
459876, 669187, 711612, 816326, 931423, 1033197, 1131895, 2477317,
3964974, 12718782,
}
hist := hdrhistogram.New(459876, 12718782, 5)
for _, sample := range input {
hist.RecordValue(sample)
}
if v, want := hist.ValueAtQuantile(50), int64(1048575); v != want {
t.Errorf("Median was %v, but expected %v", v, want)
}
}
func TestSubBucketMaskOverflow(t *testing.T) {
hist := hdrhistogram.New(2e7, 1e8, 5)
for _, sample := range [...]int64{1e8, 2e7, 3e7} {
hist.RecordValue(sample)
}
for q, want := range map[float64]int64{
50: 33554431,
83.33: 33554431,
83.34: 100663295,
99: 100663295,
} {
if got := hist.ValueAtQuantile(q); got != want {
t.Errorf("got %d for %fth percentile. want: %d", got, q, want)
}
}
}
func (s *subscriber) start() {
latencies := hdrhistogram.New(0, maxRecordableLatencyMS, sigFigs)
for {
message, err := s.Recv()
now := time.Now().UnixNano()
if err != nil {
log.Printf("Subscriber error: %s", err.Error())
s.mu.Lock()
s.results = &result{Err: err.Error()}
s.mu.Unlock()
return
}
then, _ := binary.Varint(message)
latencies.RecordValue((now - then) / 1000)
if !s.hasStarted {
s.hasStarted = true
s.started = time.Now().UnixNano()
}
s.counter++
if s.counter == s.numMessages {
s.stopped = time.Now().UnixNano()
durationMS := float32(s.stopped-s.started) / 1000000.0
s.mu.Lock()
s.results = &result{
Duration: durationMS,
Throughput: 1000 * float32(s.numMessages) / durationMS,
Latency: &latencyResults{
Min: latencies.Min(),
Q1: latencies.ValueAtQuantile(25),
Q2: latencies.ValueAtQuantile(50),
Q3: latencies.ValueAtQuantile(75),
Max: latencies.Max(),
Mean: latencies.Mean(),
StdDev: latencies.StdDev(),
},
}
s.mu.Unlock()
log.Println("Subscriber completed")
return
}
}
}
func init() {
stats.hist = hdrhistogram.New(0, 0x7fffffff, 1)
stats.start = time.Now()
stats.opCounts = map[int]int{}
// Compute the total of all op relative frequencies.
var relFreqTotal float64
for _, op := range ops {
relFreqTotal += op.relFreq
}
// Normalize frequencies.
var normFreqTotal float64
for _, op := range ops {
normFreq := op.relFreq / relFreqTotal
op.normFreq = normFreqTotal + normFreq
normFreqTotal += normFreq
}
}
func TestDistribution(t *testing.T) {
h := hdrhistogram.New(8, 1024, 3)
for i := 0; i < 1024; i++ {
if err := h.RecordValue(int64(i)); err != nil {
t.Fatal(err)
}
}
actual := h.Distribution()
if len(actual) != 128 {
t.Errorf("Number of bars seen was %v, expected was 128", len(actual))
}
for _, b := range actual {
if b.Count != 8 {
t.Errorf("Count per bar seen was %v, expected was 8", b.Count)
}
}
}
func NewOneMeasurementHdrHistogram(name string, props Properties) (*OneMeasurementHdrHistogram, error) {
prop := props.GetDefault(PropertyPercentiles, PropertyPercentilesDefault)
percentiles := parsePercentileValues(prop, PropertyPercentilesDefault)
prop = props.GetDefault(PropertyHdrHistogramOutput, PropertyHdrHistogramOutputDefault)
shouldLog, err := strconv.ParseBool(prop)
if err != nil {
return nil, err
}
prop = props.GetDefault(PropertyHdrHistogramMax, PropertyHdrHistogramMaxDefault)
max, err := strconv.ParseInt(prop, 0, 64)
if err != nil {
return nil, err
}
prop = props.GetDefault(PropertyHdrHistogramSig, PropertyHdrHistogramSigDefault)
sig, err := strconv.ParseInt(prop, 0, 64)
if err != nil {
return nil, err
}
var filePath string
var f *os.File
var writer *HdrHistogramLogWriter
if !shouldLog {
filePath = ""
f = nil
writer = nil
} else {
filePath = props.GetDefault(PropertyHdrHistogramOutputPath, PropertyHdrHistogramOutputPathDefault)
f, err = os.OpenFile(filePath, os.O_RDWR|os.O_CREATE|os.O_APPEND, 0666)
if err != nil {
return nil, err
}
writer = NewHdrHistogramLogWriter(f)
}
object := &OneMeasurementHdrHistogram{
OneMeasurementBase: NewOneMeasurementBase(name),
histogram: hdrhistogram.New(0, max, int(sig)),
filePath: filePath,
file: f,
writer: writer,
percentiles: percentiles,
}
return object, nil
}
func (s *Sampler) Init() {
s.outOfRangeErrors = 0
s.histogram = hdrhistogram.New(s.Min, s.Max, s.Sigint)
// we need to know the bucket Quantiles, for this we just add every possible value to the histogram..
for i := s.Min; i < s.Max; i++ {
s.histogram.RecordValue(i)
}
//We then extract hdrhistograms own bucket range
distribution := s.histogram.CumulativeDistribution()
s.bucketQuantiles = make([]float64, 0, len(distribution))
//and save it for later use
for _, d := range distribution {
s.bucketQuantiles = append(s.bucketQuantiles, d.Quantile)
}
s.histogram.Reset()
// Pipe stuff
// Check if pipe already exists
pipeExists := false
fileInfo, err := os.Stat(s.Path)
if err == nil {
if (fileInfo.Mode() & os.ModeNamedPipe) > 0 {
pipeExists = true
} else {
fmt.Printf("%d != %d\n", os.ModeNamedPipe, fileInfo.Mode())
panic(s.Path + " exists, but it's not a named pipe (FIFO)")
}
}
// Try to create pipe if needed
if !pipeExists {
err := syscall.Mkfifo(s.Path, 0666)
if err != nil {
panic(err.Error())
}
}
}
// AssertCirconusNormalHistogram ensures the Circonus Histogram data captured in
// the result slice abides a normal distribution.
func AssertCirconusNormalHistogram(t *testing.T, mean, stdev, min, max int64, result []string) {
if len(result) <= 0 {
t.Fatal("no results")
}
// Circonus just dumps the raw counts. We need to do our own statistical analysis.
h := hdrhistogram.New(min, max, 3)
for _, s := range result {
// "H[1.23e04]=123"
toks := strings.Split(s, "=")
if len(toks) != 2 {
t.Fatalf("bad H value: %q", s)
}
var bucket string
bucket = toks[0]
bucket = bucket[2 : len(bucket)-1] // "H[1.23e04]" -> "1.23e04"
f, err := strconv.ParseFloat(bucket, 64)
if err != nil {
t.Fatalf("error parsing H value: %q: %v", s, err)
}
count, err := strconv.ParseFloat(toks[1], 64)
if err != nil {
t.Fatalf("error parsing H count: %q: %v", s, err)
}
h.RecordValues(int64(f), int64(count))
}
// Apparently Circonus buckets observations by dropping a sigfig, so we have
// very coarse tolerance.
var tolerance int64 = 30
for _, quantile := range []int{50, 90, 99} {
want := normalValueAtQuantile(mean, stdev, quantile)
have := h.ValueAtQuantile(float64(quantile))
if int64(math.Abs(float64(want)-float64(have))) > tolerance {
t.Errorf("quantile %d: want %d, have %d", quantile, want, have)
}
}
}
// aggregate run contexts
func (b *Bench) aggregate() {
// aggregate timer metrics
for n := range b.runContexts[0].timers {
t := hdrhistogram.New(min, max, precision)
b.timers[n] = t
for i := 0; i < b.concurrentRuns; i++ {
t.Merge(b.runContexts[i].timers[n])
}
}
// aggregate counters
for n := range b.runContexts[0].counters {
for i := 0; i < b.concurrentRuns; i++ {
b.counters[n] += b.runContexts[i].counters[n]
}
}
// aggregate call counts
for i := 0; i < b.concurrentRuns; i++ {
b.calls += b.runContexts[i].Iteration
}
}