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Diffstat (limited to 'vendor/golang.org/x/net/trace/histogram.go')
| -rw-r--r-- | vendor/golang.org/x/net/trace/histogram.go | 365 |
1 files changed, 365 insertions, 0 deletions
diff --git a/vendor/golang.org/x/net/trace/histogram.go b/vendor/golang.org/x/net/trace/histogram.go new file mode 100644 index 0000000..9bf4286 --- /dev/null +++ b/vendor/golang.org/x/net/trace/histogram.go @@ -0,0 +1,365 @@ +// Copyright 2015 The Go Authors. All rights reserved. +// Use of this source code is governed by a BSD-style +// license that can be found in the LICENSE file. + +package trace + +// This file implements histogramming for RPC statistics collection. + +import ( + "bytes" + "fmt" + "html/template" + "log" + "math" + "sync" + + "golang.org/x/net/internal/timeseries" +) + +const ( + bucketCount = 38 +) + +// histogram keeps counts of values in buckets that are spaced +// out in powers of 2: 0-1, 2-3, 4-7... +// histogram implements timeseries.Observable +type histogram struct { + sum int64 // running total of measurements + sumOfSquares float64 // square of running total + buckets []int64 // bucketed values for histogram + value int // holds a single value as an optimization + valueCount int64 // number of values recorded for single value +} + +// AddMeasurement records a value measurement observation to the histogram. +func (h *histogram) addMeasurement(value int64) { + // TODO: assert invariant + h.sum += value + h.sumOfSquares += float64(value) * float64(value) + + bucketIndex := getBucket(value) + + if h.valueCount == 0 || (h.valueCount > 0 && h.value == bucketIndex) { + h.value = bucketIndex + h.valueCount++ + } else { + h.allocateBuckets() + h.buckets[bucketIndex]++ + } +} + +func (h *histogram) allocateBuckets() { + if h.buckets == nil { + h.buckets = make([]int64, bucketCount) + h.buckets[h.value] = h.valueCount + h.value = 0 + h.valueCount = -1 + } +} + +func log2(i int64) int { + n := 0 + for ; i >= 0x100; i >>= 8 { + n += 8 + } + for ; i > 0; i >>= 1 { + n += 1 + } + return n +} + +func getBucket(i int64) (index int) { + index = log2(i) - 1 + if index < 0 { + index = 0 + } + if index >= bucketCount { + index = bucketCount - 1 + } + return +} + +// Total returns the number of recorded observations. +func (h *histogram) total() (total int64) { + if h.valueCount >= 0 { + total = h.valueCount + } + for _, val := range h.buckets { + total += int64(val) + } + return +} + +// Average returns the average value of recorded observations. +func (h *histogram) average() float64 { + t := h.total() + if t == 0 { + return 0 + } + return float64(h.sum) / float64(t) +} + +// Variance returns the variance of recorded observations. +func (h *histogram) variance() float64 { + t := float64(h.total()) + if t == 0 { + return 0 + } + s := float64(h.sum) / t + return h.sumOfSquares/t - s*s +} + +// StandardDeviation returns the standard deviation of recorded observations. +func (h *histogram) standardDeviation() float64 { + return math.Sqrt(h.variance()) +} + +// PercentileBoundary estimates the value that the given fraction of recorded +// observations are less than. +func (h *histogram) percentileBoundary(percentile float64) int64 { + total := h.total() + + // Corner cases (make sure result is strictly less than Total()) + if total == 0 { + return 0 + } else if total == 1 { + return int64(h.average()) + } + + percentOfTotal := round(float64(total) * percentile) + var runningTotal int64 + + for i := range h.buckets { + value := h.buckets[i] + runningTotal += value + if runningTotal == percentOfTotal { + // We hit an exact bucket boundary. If the next bucket has data, it is a + // good estimate of the value. If the bucket is empty, we interpolate the + // midpoint between the next bucket's boundary and the next non-zero + // bucket. If the remaining buckets are all empty, then we use the + // boundary for the next bucket as the estimate. + j := uint8(i + 1) + min := bucketBoundary(j) + if runningTotal < total { + for h.buckets[j] == 0 { + j++ + } + } + max := bucketBoundary(j) + return min + round(float64(max-min)/2) + } else if runningTotal > percentOfTotal { + // The value is in this bucket. Interpolate the value. + delta := runningTotal - percentOfTotal + percentBucket := float64(value-delta) / float64(value) + bucketMin := bucketBoundary(uint8(i)) + nextBucketMin := bucketBoundary(uint8(i + 1)) + bucketSize := nextBucketMin - bucketMin + return bucketMin + round(percentBucket*float64(bucketSize)) + } + } + return bucketBoundary(bucketCount - 1) +} + +// Median returns the estimated median of the observed values. +func (h *histogram) median() int64 { + return h.percentileBoundary(0.5) +} + +// Add adds other to h. +func (h *histogram) Add(other timeseries.Observable) { + o := other.(*histogram) + if o.valueCount == 0 { + // Other histogram is empty + } else if h.valueCount >= 0 && o.valueCount > 0 && h.value == o.value { + // Both have a single bucketed value, aggregate them + h.valueCount += o.valueCount + } else { + // Two different values necessitate buckets in this histogram + h.allocateBuckets() + if o.valueCount >= 0 { + h.buckets[o.value] += o.valueCount + } else { + for i := range h.buckets { + h.buckets[i] += o.buckets[i] + } + } + } + h.sumOfSquares += o.sumOfSquares + h.sum += o.sum +} + +// Clear resets the histogram to an empty state, removing all observed values. +func (h *histogram) Clear() { + h.buckets = nil + h.value = 0 + h.valueCount = 0 + h.sum = 0 + h.sumOfSquares = 0 +} + +// CopyFrom copies from other, which must be a *histogram, into h. +func (h *histogram) CopyFrom(other timeseries.Observable) { + o := other.(*histogram) + if o.valueCount == -1 { + h.allocateBuckets() + copy(h.buckets, o.buckets) + } + h.sum = o.sum + h.sumOfSquares = o.sumOfSquares + h.value = o.value + h.valueCount = o.valueCount +} + +// Multiply scales the histogram by the specified ratio. +func (h *histogram) Multiply(ratio float64) { + if h.valueCount == -1 { + for i := range h.buckets { + h.buckets[i] = int64(float64(h.buckets[i]) * ratio) + } + } else { + h.valueCount = int64(float64(h.valueCount) * ratio) + } + h.sum = int64(float64(h.sum) * ratio) + h.sumOfSquares = h.sumOfSquares * ratio +} + +// New creates a new histogram. +func (h *histogram) New() timeseries.Observable { + r := new(histogram) + r.Clear() + return r +} + +func (h *histogram) String() string { + return fmt.Sprintf("%d, %f, %d, %d, %v", + h.sum, h.sumOfSquares, h.value, h.valueCount, h.buckets) +} + +// round returns the closest int64 to the argument +func round(in float64) int64 { + return int64(math.Floor(in + 0.5)) +} + +// bucketBoundary returns the first value in the bucket. +func bucketBoundary(bucket uint8) int64 { + if bucket == 0 { + return 0 + } + return 1 << bucket +} + +// bucketData holds data about a specific bucket for use in distTmpl. +type bucketData struct { + Lower, Upper int64 + N int64 + Pct, CumulativePct float64 + GraphWidth int +} + +// data holds data about a Distribution for use in distTmpl. +type data struct { + Buckets []*bucketData + Count, Median int64 + Mean, StandardDeviation float64 +} + +// maxHTMLBarWidth is the maximum width of the HTML bar for visualizing buckets. +const maxHTMLBarWidth = 350.0 + +// newData returns data representing h for use in distTmpl. +func (h *histogram) newData() *data { + // Force the allocation of buckets to simplify the rendering implementation + h.allocateBuckets() + // We scale the bars on the right so that the largest bar is + // maxHTMLBarWidth pixels in width. + maxBucket := int64(0) + for _, n := range h.buckets { + if n > maxBucket { + maxBucket = n + } + } + total := h.total() + barsizeMult := maxHTMLBarWidth / float64(maxBucket) + var pctMult float64 + if total == 0 { + pctMult = 1.0 + } else { + pctMult = 100.0 / float64(total) + } + + buckets := make([]*bucketData, len(h.buckets)) + runningTotal := int64(0) + for i, n := range h.buckets { + if n == 0 { + continue + } + runningTotal += n + var upperBound int64 + if i < bucketCount-1 { + upperBound = bucketBoundary(uint8(i + 1)) + } else { + upperBound = math.MaxInt64 + } + buckets[i] = &bucketData{ + Lower: bucketBoundary(uint8(i)), + Upper: upperBound, + N: n, + Pct: float64(n) * pctMult, + CumulativePct: float64(runningTotal) * pctMult, + GraphWidth: int(float64(n) * barsizeMult), + } + } + return &data{ + Buckets: buckets, + Count: total, + Median: h.median(), + Mean: h.average(), + StandardDeviation: h.standardDeviation(), + } +} + +func (h *histogram) html() template.HTML { + buf := new(bytes.Buffer) + if err := distTmpl().Execute(buf, h.newData()); err != nil { + buf.Reset() + log.Printf("net/trace: couldn't execute template: %v", err) + } + return template.HTML(buf.String()) +} + +var distTmplCache *template.Template +var distTmplOnce sync.Once + +func distTmpl() *template.Template { + distTmplOnce.Do(func() { + // Input: data + distTmplCache = template.Must(template.New("distTmpl").Parse(` +<table> +<tr> + <td style="padding:0.25em">Count: {{.Count}}</td> + <td style="padding:0.25em">Mean: {{printf "%.0f" .Mean}}</td> + <td style="padding:0.25em">StdDev: {{printf "%.0f" .StandardDeviation}}</td> + <td style="padding:0.25em">Median: {{.Median}}</td> +</tr> +</table> +<hr> +<table> +{{range $b := .Buckets}} +{{if $b}} + <tr> + <td style="padding:0 0 0 0.25em">[</td> + <td style="text-align:right;padding:0 0.25em">{{.Lower}},</td> + <td style="text-align:right;padding:0 0.25em">{{.Upper}})</td> + <td style="text-align:right;padding:0 0.25em">{{.N}}</td> + <td style="text-align:right;padding:0 0.25em">{{printf "%#.3f" .Pct}}%</td> + <td style="text-align:right;padding:0 0.25em">{{printf "%#.3f" .CumulativePct}}%</td> + <td><div style="background-color: blue; height: 1em; width: {{.GraphWidth}};"></div></td> + </tr> +{{end}} +{{end}} +</table> +`)) + }) + return distTmplCache +} |