feat: initial tracing support (#1623)

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
+}