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提交 58d222fb 作者: Jakub Sztandera
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Remove codahale/hdrhistogram as it is not longer used 

License: MIT
Signed-off-by: 's avatarJakub Sztandera <kubuxu@protonmail.ch>
上级 6bd66cc8
正在显示 包含 0 行增加 和 1000 行删除
Godeps/_workspace/src/github.com/codahale/hdrhistogram/.travis.yml deleted 100644 → 0
language: go
go:
- 1.3.3
notifications:
# See http://about.travis-ci.org/docs/user/build-configuration/ to learn more
# about configuring notification recipients and more.
email:
recipients:
- coda.hale@gmail.com
Godeps/_workspace/src/github.com/codahale/hdrhistogram/LICENSE deleted 100644 → 0
The MIT License (MIT)
Copyright (c) 2014 Coda Hale
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Godeps/_workspace/src/github.com/codahale/hdrhistogram/README.md deleted 100644 → 0
hdrhistogram
============
[![Build Status](https://travis-ci.org/codahale/hdrhistogram.png?branch=master)](https://travis-ci.org/codahale/hdrhistogram)
A pure Go implementation of the [HDR Histogram](https://github.com/HdrHistogram/HdrHistogram).
> A Histogram that supports recording and analyzing sampled data value counts
> across a configurable integer value range with configurable value precision
> within the range. Value precision is expressed as the number of significant
> digits in the value recording, and provides control over value quantization
> behavior across the value range and the subsequent value resolution at any
> given level.
For documentation, check [godoc](http://godoc.org/github.com/codahale/hdrhistogram).
Godeps/_workspace/src/github.com/codahale/hdrhistogram/hdr.go deleted 100644 → 0
// Package hdrhistogram provides an implementation of Gil Tene's HDR Histogram
// data structure. The HDR Histogram allows for fast and accurate analysis of
// the extreme ranges of data with non-normal distributions, like latency.
package hdrhistogram
import (
"fmt"
"math"
)
// A Bracket is a part of a cumulative distribution.
type Bracket struct {
Quantile float64
Count, ValueAt int64
}
// A Snapshot is an exported view of a Histogram, useful for serializing them.
// A Histogram can be constructed from it by passing it to Import.
type Snapshot struct {
LowestTrackableValue int64
HighestTrackableValue int64
SignificantFigures int64
Counts []int64
}
// A Histogram is a lossy data structure used to record the distribution of
// non-normally distributed data (like latency) with a high degree of accuracy
// and a bounded degree of precision.
type Histogram struct {
lowestTrackableValue int64
highestTrackableValue int64
unitMagnitude int64
significantFigures int64
subBucketHalfCountMagnitude int32
subBucketHalfCount int32
subBucketMask int64
subBucketCount int32
bucketCount int32
countsLen int32
totalCount int64
counts []int64
}
// New returns a new Histogram instance capable of tracking values in the given
// range and with the given amount of precision.
func New(minValue, maxValue int64, sigfigs int) *Histogram {
if sigfigs < 1 || 5 < sigfigs {
panic(fmt.Errorf("sigfigs must be [1,5] (was %d)", sigfigs))
}
largestValueWithSingleUnitResolution := 2 * math.Pow10(sigfigs)
subBucketCountMagnitude := int32(math.Ceil(math.Log2(float64(largestValueWithSingleUnitResolution))))
subBucketHalfCountMagnitude := subBucketCountMagnitude
if subBucketHalfCountMagnitude < 1 {
subBucketHalfCountMagnitude = 1
}
subBucketHalfCountMagnitude--
unitMagnitude := int32(math.Floor(math.Log2(float64(minValue))))
if unitMagnitude < 0 {
unitMagnitude = 0
}
subBucketCount := int32(math.Pow(2, float64(subBucketHalfCountMagnitude)+1))
subBucketHalfCount := subBucketCount / 2
subBucketMask := int64(subBucketCount-1) << uint(unitMagnitude)
// determine exponent range needed to support the trackable value with no
// overflow:
smallestUntrackableValue := int64(subBucketCount) << uint(unitMagnitude)
bucketsNeeded := int32(1)
for smallestUntrackableValue < maxValue {
smallestUntrackableValue <<= 1
bucketsNeeded++
}
bucketCount := bucketsNeeded
countsLen := (bucketCount + 1) * (subBucketCount / 2)
return &Histogram{
lowestTrackableValue: minValue,
highestTrackableValue: maxValue,
unitMagnitude: int64(unitMagnitude),
significantFigures: int64(sigfigs),
subBucketHalfCountMagnitude: subBucketHalfCountMagnitude,
subBucketHalfCount: subBucketHalfCount,
subBucketMask: subBucketMask,
subBucketCount: subBucketCount,
bucketCount: bucketCount,
countsLen: countsLen,
totalCount: 0,
counts: make([]int64, countsLen),
}
}
// ByteSize returns an estimate of the amount of memory allocated to the
// histogram in bytes.
//
// N.B.: This does not take into account the overhead for slices, which are
// small, constant, and specific to the compiler version.
func (h *Histogram) ByteSize() int {
return 6*8 + 5*4 + len(h.counts)*8
}
// Merge merges the data stored in the given histogram with the receiver,
// returning the number of recorded values which had to be dropped.
func (h *Histogram) Merge(from *Histogram) (dropped int64) {
i := from.rIterator()
for i.next() {
v := i.valueFromIdx
c := i.countAtIdx
if h.RecordValues(v, c) != nil {
dropped += c
}
}
return
}
// TotalCount returns total number of values recorded.
func (h *Histogram) TotalCount() int64 {
return h.totalCount
}
// Max returns the approximate maximum recorded value.
func (h *Histogram) Max() int64 {
var max int64
i := h.iterator()
for i.next() {
if i.countAtIdx != 0 {
max = i.highestEquivalentValue
}
}
return h.lowestEquivalentValue(max)
}
// Min returns the approximate minimum recorded value.
func (h *Histogram) Min() int64 {
var min int64
i := h.iterator()
for i.next() {
if i.countAtIdx != 0 && min == 0 {
min = i.highestEquivalentValue
break
}
}
return h.lowestEquivalentValue(min)
}
// Mean returns the approximate arithmetic mean of the recorded values.
func (h *Histogram) Mean() float64 {
var total int64
i := h.iterator()
for i.next() {
if i.countAtIdx != 0 {
total += i.countAtIdx * h.medianEquivalentValue(i.valueFromIdx)
}
}
return float64(total) / float64(h.totalCount)
}
// StdDev returns the approximate standard deviation of the recorded values.
func (h *Histogram) StdDev() float64 {
mean := h.Mean()
geometricDevTotal := 0.0
i := h.iterator()
for i.next() {
if i.countAtIdx != 0 {
dev := float64(h.medianEquivalentValue(i.valueFromIdx)) - mean
geometricDevTotal += (dev * dev) * float64(i.countAtIdx)
}
}
return math.Sqrt(geometricDevTotal / float64(h.totalCount))
}
// Reset deletes all recorded values and restores the histogram to its original
// state.
func (h *Histogram) Reset() {
h.totalCount = 0
for i := range h.counts {
h.counts[i] = 0
}
}
// RecordValue records the given value, returning an error if the value is out
// of range.
func (h *Histogram) RecordValue(v int64) error {
return h.RecordValues(v, 1)
}
// RecordCorrectedValue records the given value, correcting for stalls in the
// recording process. This only works for processes which are recording values
// at an expected interval (e.g., doing jitter analysis). Processes which are
// recording ad-hoc values (e.g., latency for incoming requests) can't take
// advantage of this.
func (h *Histogram) RecordCorrectedValue(v, expectedInterval int64) error {
if err := h.RecordValue(v); err != nil {
return err
}
if expectedInterval <= 0 || v <= expectedInterval {
return nil
}
missingValue := v - expectedInterval
for missingValue >= expectedInterval {
if err := h.RecordValue(missingValue); err != nil {
return err
}
missingValue -= expectedInterval
}
return nil
}
// RecordValues records n occurrences of the given value, returning an error if
// the value is out of range.
func (h *Histogram) RecordValues(v, n int64) error {
idx := h.countsIndexFor(v)
if idx < 0 || int(h.countsLen) <= idx {
return fmt.Errorf("value %d is too large to be recorded", v)
}
h.counts[idx] += n
h.totalCount += n
return nil
}
// ValueAtQuantile returns the recorded value at the given quantile (0..100).
func (h *Histogram) ValueAtQuantile(q float64) int64 {
if q > 100 {
q = 100
}
total := int64(0)
countAtPercentile := int64(((q / 100) * float64(h.totalCount)) + 0.5)
i := h.iterator()
for i.next() {
total += i.countAtIdx
if total >= countAtPercentile {
return h.highestEquivalentValue(i.valueFromIdx)
}
}
return 0
}
// CumulativeDistribution returns an ordered list of brackets of the
// distribution of recorded values.
func (h *Histogram) CumulativeDistribution() []Bracket {
var result []Bracket
i := h.pIterator(1)
for i.next() {
result = append(result, Bracket{
Quantile: i.percentile,
Count: i.countToIdx,
ValueAt: i.highestEquivalentValue,
})
}
return result
}
// Equals returns true if the two Histograms are equivalent, false if not.
func (h *Histogram) Equals(other *Histogram) bool {
switch {
case
h.lowestTrackableValue != other.lowestTrackableValue,
h.highestTrackableValue != other.highestTrackableValue,
h.unitMagnitude != other.unitMagnitude,
h.significantFigures != other.significantFigures,
h.subBucketHalfCountMagnitude != other.subBucketHalfCountMagnitude,
h.subBucketHalfCount != other.subBucketHalfCount,
h.subBucketMask != other.subBucketMask,
h.subBucketCount != other.subBucketCount,
h.bucketCount != other.bucketCount,
h.countsLen != other.countsLen,
h.totalCount != other.totalCount:
return false
default:
for i, c := range h.counts {
if c != other.counts[i] {
return false
}
}
}
return true
}
// Export returns a snapshot view of the Histogram. This can be later passed to
// Import to construct a new Histogram with the same state.
func (h *Histogram) Export() *Snapshot {
return &Snapshot{
LowestTrackableValue: h.lowestTrackableValue,
HighestTrackableValue: h.highestTrackableValue,
SignificantFigures: h.significantFigures,
Counts: h.counts,
}
}
// Import returns a new Histogram populated from the Snapshot data.
func Import(s *Snapshot) *Histogram {
h := New(s.LowestTrackableValue, s.HighestTrackableValue, int(s.SignificantFigures))
h.counts = s.Counts
totalCount := int64(0)
for i := int32(0); i < h.countsLen; i++ {
countAtIndex := h.counts[i]
if countAtIndex > 0 {
totalCount += countAtIndex
}
}
h.totalCount = totalCount
return h
}
func (h *Histogram) iterator() *iterator {
return &iterator{
h: h,
subBucketIdx: -1,
}
}
func (h *Histogram) rIterator() *rIterator {
return &rIterator{
iterator: iterator{
h: h,
subBucketIdx: -1,
},
}
}
func (h *Histogram) pIterator(ticksPerHalfDistance int32) *pIterator {
return &pIterator{
iterator: iterator{
h: h,
subBucketIdx: -1,
},
ticksPerHalfDistance: ticksPerHalfDistance,
}
}
func (h *Histogram) sizeOfEquivalentValueRange(v int64) int64 {
bucketIdx := h.getBucketIndex(v)
subBucketIdx := h.getSubBucketIdx(v, bucketIdx)
adjustedBucket := bucketIdx
if subBucketIdx >= h.subBucketCount {
adjustedBucket++
}
return int64(1) << uint(h.unitMagnitude+int64(adjustedBucket))
}
func (h *Histogram) valueFromIndex(bucketIdx, subBucketIdx int32) int64 {
return int64(subBucketIdx) << uint(int64(bucketIdx)+h.unitMagnitude)
}
func (h *Histogram) lowestEquivalentValue(v int64) int64 {
bucketIdx := h.getBucketIndex(v)
subBucketIdx := h.getSubBucketIdx(v, bucketIdx)
return h.valueFromIndex(bucketIdx, subBucketIdx)
}
func (h *Histogram) nextNonEquivalentValue(v int64) int64 {
return h.lowestEquivalentValue(v) + h.sizeOfEquivalentValueRange(v)
}
func (h *Histogram) highestEquivalentValue(v int64) int64 {
return h.nextNonEquivalentValue(v) - 1
}
func (h *Histogram) medianEquivalentValue(v int64) int64 {
return h.lowestEquivalentValue(v) + (h.sizeOfEquivalentValueRange(v) >> 1)
}
func (h *Histogram) getCountAtIndex(bucketIdx, subBucketIdx int32) int64 {
return h.counts[h.countsIndex(bucketIdx, subBucketIdx)]
}
func (h *Histogram) countsIndex(bucketIdx, subBucketIdx int32) int32 {
bucketBaseIdx := (bucketIdx + 1) << uint(h.subBucketHalfCountMagnitude)
offsetInBucket := subBucketIdx - h.subBucketHalfCount
return bucketBaseIdx + offsetInBucket
}
func (h *Histogram) getBucketIndex(v int64) int32 {
pow2Ceiling := bitLen(v | h.subBucketMask)
return int32(pow2Ceiling - int64(h.unitMagnitude) -
int64(h.subBucketHalfCountMagnitude+1))
}
func (h *Histogram) getSubBucketIdx(v int64, idx int32) int32 {
return int32(v >> uint(int64(idx)+int64(h.unitMagnitude)))
}
func (h *Histogram) countsIndexFor(v int64) int {
bucketIdx := h.getBucketIndex(v)
subBucketIdx := h.getSubBucketIdx(v, bucketIdx)
return int(h.countsIndex(bucketIdx, subBucketIdx))
}
type iterator struct {
h *Histogram
bucketIdx, subBucketIdx int32
countAtIdx, countToIdx, valueFromIdx int64
highestEquivalentValue int64
}
func (i *iterator) next() bool {
if i.countToIdx >= i.h.totalCount {
return false
}
// increment bucket
i.subBucketIdx++
if i.subBucketIdx >= i.h.subBucketCount {
i.subBucketIdx = i.h.subBucketHalfCount
i.bucketIdx++
}
if i.bucketIdx >= i.h.bucketCount {
return false
}
i.countAtIdx = i.h.getCountAtIndex(i.bucketIdx, i.subBucketIdx)
i.countToIdx += i.countAtIdx
i.valueFromIdx = i.h.valueFromIndex(i.bucketIdx, i.subBucketIdx)
i.highestEquivalentValue = i.h.highestEquivalentValue(i.valueFromIdx)
return true
}
type rIterator struct {
iterator
countAddedThisStep int64
}
func (r *rIterator) next() bool {
for r.iterator.next() {
if r.countAtIdx != 0 {
r.countAddedThisStep = r.countAtIdx
return true
}
}
return false
}
type pIterator struct {
iterator
seenLastValue bool
ticksPerHalfDistance int32
percentileToIteratorTo float64
percentile float64
}
func (p *pIterator) next() bool {
if !(p.countToIdx < p.h.totalCount) {
if p.seenLastValue {
return false
}
p.seenLastValue = true
p.percentile = 100
return true
}
if p.subBucketIdx == -1 && !p.iterator.next() {
return false
}
var done = false
for !done {
currentPercentile := (100.0 * float64(p.countToIdx)) / float64(p.h.totalCount)
if p.countAtIdx != 0 && p.percentileToIteratorTo <= currentPercentile {
p.percentile = p.percentileToIteratorTo
halfDistance := math.Trunc(math.Pow(2, math.Trunc(math.Log2(100.0/(100.0-p.percentileToIteratorTo)))+1))
percentileReportingTicks := float64(p.ticksPerHalfDistance) * halfDistance
p.percentileToIteratorTo += 100.0 / percentileReportingTicks
return true
}
done = !p.iterator.next()
}
return true
}
func bitLen(x int64) (n int64) {
for ; x >= 0x8000; x >>= 16 {
n += 16
}
if x >= 0x80 {
x >>= 8
n += 8
}
if x >= 0x8 {
x >>= 4
n += 4
}
if x >= 0x2 {
x >>= 2
n += 2
}
if x >= 0x1 {
n++
}
return
}
Godeps/_workspace/src/github.com/codahale/hdrhistogram/hdr_test.go deleted 100644 → 0
package hdrhistogram_test
import (
"reflect"
"testing"
"github.com/ipfs/go-ipfs/Godeps/_workspace/src/github.com/codahale/hdrhistogram"
)
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 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 TestMean(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.Mean(), 500000.013312; v != want {
t.Errorf("Mean 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 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 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 TestReset(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)
}
}
h.Reset()
if v, want := h.Max(), int64(0); v != want {
t.Errorf("Max 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 TestMin(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.Min(), int64(0); v != want {
t.Errorf("Min was %v, but expected %v", v, want)
}
}
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 TestRecordCorrectedValue(t *testing.T) {
h := hdrhistogram.New(1, 100000, 3)
if err := h.RecordCorrectedValue(10, 100); err != nil {
t.Fatal(err)
}
if v, want := h.ValueAtQuantile(75), int64(10); v != want {
t.Errorf("Corrected value was %v, but expected %v", v, want)
}
}
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 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 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 BenchmarkNew(b *testing.B) {
b.ReportAllocs()
for i := 0; i < b.N; i++ {
hdrhistogram.New(1, 120000, 3) // this could track 1ms-2min
}
}
func TestUnitMagnitudeOverflow(t *testing.T) {
h := hdrhistogram.New(0, 200, 4)
if err := h.RecordValue(11); err != nil {
t.Fatal(err)
}
}
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 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")
}
}
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")
}
}
Godeps/_workspace/src/github.com/codahale/hdrhistogram/window.go deleted 100644 → 0
package hdrhistogram
// A WindowedHistogram combines histograms to provide windowed statistics.
type WindowedHistogram struct {
idx int
h []Histogram
m *Histogram
Current *Histogram
}
// NewWindowed creates a new WindowedHistogram with N underlying histograms with
// the given parameters.
func NewWindowed(n int, minValue, maxValue int64, sigfigs int) *WindowedHistogram {
w := WindowedHistogram{
idx: -1,
h: make([]Histogram, n),
m: New(minValue, maxValue, sigfigs),
}
for i := range w.h {
w.h[i] = *New(minValue, maxValue, sigfigs)
}
w.Rotate()
return &w
}
// Merge returns a histogram which includes the recorded values from all the
// sections of the window.
func (w *WindowedHistogram) Merge() *Histogram {
w.m.Reset()
for _, h := range w.h {
w.m.Merge(&h)
}
return w.m
}
// Rotate resets the oldest histogram and rotates it to be used as the current
// histogram.
func (w *WindowedHistogram) Rotate() {
w.idx++
w.Current = &w.h[w.idx%len(w.h)]
w.Current.Reset()
}
Godeps/_workspace/src/github.com/codahale/hdrhistogram/window_test.go deleted 100644 → 0
package hdrhistogram_test
import (
"testing"
"github.com/ipfs/go-ipfs/Godeps/_workspace/src/github.com/codahale/hdrhistogram"
)
func TestWindowedHistogram(t *testing.T) {
w := hdrhistogram.NewWindowed(2, 1, 1000, 3)
for i := 0; i < 100; i++ {
w.Current.RecordValue(int64(i))
}
w.Rotate()
for i := 100; i < 200; i++ {
w.Current.RecordValue(int64(i))
}
w.Rotate()
for i := 200; i < 300; i++ {
w.Current.RecordValue(int64(i))
}
if v, want := w.Merge().ValueAtQuantile(50), int64(199); v != want {
t.Errorf("Median was %v, but expected %v", v, want)
}
}
func BenchmarkWindowedHistogramRecordAndRotate(b *testing.B) {
w := hdrhistogram.NewWindowed(3, 1, 10000000, 3)
b.ReportAllocs()
b.ResetTimer()
for i := 0; i < b.N; i++ {
if err := w.Current.RecordValue(100); err != nil {
b.Fatal(err)
}
if i%100000 == 1 {
w.Rotate()
}
}
}
func BenchmarkWindowedHistogramMerge(b *testing.B) {
w := hdrhistogram.NewWindowed(3, 1, 10000000, 3)
for i := 0; i < 10000000; i++ {
if err := w.Current.RecordValue(100); err != nil {
b.Fatal(err)
}
if i%100000 == 1 {
w.Rotate()
}
}
b.ReportAllocs()
b.ResetTimer()
for i := 0; i < b.N; i++ {
w.Merge()
}
}
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