X-Git-Url: http://git.osdn.net/view?p=bytom%2Fvapor.git;a=blobdiff_plain;f=vendor%2Fgithub.com%2Fcodahale%2Fhdrhistogram%2Fhdr.go;fp=vendor%2Fgithub.com%2Fcodahale%2Fhdrhistogram%2Fhdr.go;h=0000000000000000000000000000000000000000;hp=c97842926d678384cd3535475b36f61085bad51f;hb=54373c1a3efe0e373ec1605840a4363e4b246c46;hpb=ee01d543fdfe1fd0a4d548965c66f7923ea7b062 diff --git a/vendor/github.com/codahale/hdrhistogram/hdr.go b/vendor/github.com/codahale/hdrhistogram/hdr.go deleted file mode 100644 index c9784292..00000000 --- a/vendor/github.com/codahale/hdrhistogram/hdr.go +++ /dev/null @@ -1,564 +0,0 @@ -// 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.highestEquivalentValue(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 { - if h.totalCount == 0 { - return 0 - } - 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 { - if h.totalCount == 0 { - return 0 - } - - 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 -} - -// SignificantFigures returns the significant figures used to create the -// histogram -func (h *Histogram) SignificantFigures() int64 { - return h.significantFigures -} - -// LowestTrackableValue returns the lower bound on values that will be added -// to the histogram -func (h *Histogram) LowestTrackableValue() int64 { - return h.lowestTrackableValue -} - -// HighestTrackableValue returns the upper bound on values that will be added -// to the histogram -func (h *Histogram) HighestTrackableValue() int64 { - return h.highestTrackableValue -} - -// Histogram bar for plotting -type Bar struct { - From, To, Count int64 -} - -// Pretty print as csv for easy plotting -func (b Bar) String() string { - return fmt.Sprintf("%v, %v, %v\n", b.From, b.To, b.Count) -} - -// Distribution returns an ordered list of bars of the -// distribution of recorded values, counts can be normalized to a probability -func (h *Histogram) Distribution() (result []Bar) { - i := h.iterator() - for i.next() { - result = append(result, Bar{ - Count: i.countAtIdx, - From: h.lowestEquivalentValue(i.valueFromIdx), - To: 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: append([]int64(nil), h.counts...), // copy - } -} - -// Import returns a new Histogram populated from the Snapshot data (which the -// caller must stop accessing). -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 -}