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![Paddy](/assets/img/avatar_default.png)
Switch to using Go modules. This migrates our vendor.json to use Go 1.11's modules system, and replaces the vendor folder with the output of go mod vendor. The vendored code should remain basically the same; I believe some tree shaking of packages and support scripts/licenses/READMEs/etc. happened. This also fixes Travis and our Makefile to no longer use govendor.
236 lines
6.0 KiB
Go
236 lines
6.0 KiB
Go
// Copyright 2017, OpenCensus Authors
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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package view
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import (
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"math"
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"go.opencensus.io/exemplar"
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)
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// AggregationData represents an aggregated value from a collection.
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// They are reported on the view data during exporting.
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// Mosts users won't directly access aggregration data.
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type AggregationData interface {
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isAggregationData() bool
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addSample(e *exemplar.Exemplar)
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clone() AggregationData
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equal(other AggregationData) bool
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}
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const epsilon = 1e-9
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// CountData is the aggregated data for the Count aggregation.
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// A count aggregation processes data and counts the recordings.
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//
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// Most users won't directly access count data.
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type CountData struct {
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Value int64
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}
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func (a *CountData) isAggregationData() bool { return true }
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func (a *CountData) addSample(_ *exemplar.Exemplar) {
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a.Value = a.Value + 1
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}
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func (a *CountData) clone() AggregationData {
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return &CountData{Value: a.Value}
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}
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func (a *CountData) equal(other AggregationData) bool {
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a2, ok := other.(*CountData)
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if !ok {
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return false
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}
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return a.Value == a2.Value
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}
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// SumData is the aggregated data for the Sum aggregation.
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// A sum aggregation processes data and sums up the recordings.
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//
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// Most users won't directly access sum data.
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type SumData struct {
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Value float64
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}
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func (a *SumData) isAggregationData() bool { return true }
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func (a *SumData) addSample(e *exemplar.Exemplar) {
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a.Value += e.Value
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}
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func (a *SumData) clone() AggregationData {
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return &SumData{Value: a.Value}
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}
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func (a *SumData) equal(other AggregationData) bool {
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a2, ok := other.(*SumData)
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if !ok {
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return false
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}
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return math.Pow(a.Value-a2.Value, 2) < epsilon
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}
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// DistributionData is the aggregated data for the
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// Distribution aggregation.
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//
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// Most users won't directly access distribution data.
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//
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// For a distribution with N bounds, the associated DistributionData will have
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// N+1 buckets.
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type DistributionData struct {
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Count int64 // number of data points aggregated
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Min float64 // minimum value in the distribution
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Max float64 // max value in the distribution
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Mean float64 // mean of the distribution
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SumOfSquaredDev float64 // sum of the squared deviation from the mean
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CountPerBucket []int64 // number of occurrences per bucket
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// ExemplarsPerBucket is slice the same length as CountPerBucket containing
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// an exemplar for the associated bucket, or nil.
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ExemplarsPerBucket []*exemplar.Exemplar
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bounds []float64 // histogram distribution of the values
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}
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func newDistributionData(bounds []float64) *DistributionData {
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bucketCount := len(bounds) + 1
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return &DistributionData{
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CountPerBucket: make([]int64, bucketCount),
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ExemplarsPerBucket: make([]*exemplar.Exemplar, bucketCount),
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bounds: bounds,
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Min: math.MaxFloat64,
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Max: math.SmallestNonzeroFloat64,
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}
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}
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// Sum returns the sum of all samples collected.
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func (a *DistributionData) Sum() float64 { return a.Mean * float64(a.Count) }
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func (a *DistributionData) variance() float64 {
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if a.Count <= 1 {
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return 0
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}
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return a.SumOfSquaredDev / float64(a.Count-1)
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}
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func (a *DistributionData) isAggregationData() bool { return true }
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func (a *DistributionData) addSample(e *exemplar.Exemplar) {
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f := e.Value
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if f < a.Min {
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a.Min = f
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}
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if f > a.Max {
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a.Max = f
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}
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a.Count++
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a.addToBucket(e)
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if a.Count == 1 {
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a.Mean = f
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return
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}
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oldMean := a.Mean
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a.Mean = a.Mean + (f-a.Mean)/float64(a.Count)
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a.SumOfSquaredDev = a.SumOfSquaredDev + (f-oldMean)*(f-a.Mean)
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}
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func (a *DistributionData) addToBucket(e *exemplar.Exemplar) {
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var count *int64
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var ex **exemplar.Exemplar
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for i, b := range a.bounds {
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if e.Value < b {
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count = &a.CountPerBucket[i]
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ex = &a.ExemplarsPerBucket[i]
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break
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}
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}
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if count == nil {
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count = &a.CountPerBucket[len(a.bounds)]
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ex = &a.ExemplarsPerBucket[len(a.bounds)]
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}
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*count++
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*ex = maybeRetainExemplar(*ex, e)
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}
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func maybeRetainExemplar(old, cur *exemplar.Exemplar) *exemplar.Exemplar {
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if old == nil {
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return cur
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}
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// Heuristic to pick the "better" exemplar: first keep the one with a
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// sampled trace attachment, if neither have a trace attachment, pick the
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// one with more attachments.
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_, haveTraceID := cur.Attachments[exemplar.KeyTraceID]
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if haveTraceID || len(cur.Attachments) >= len(old.Attachments) {
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return cur
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}
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return old
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}
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func (a *DistributionData) clone() AggregationData {
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c := *a
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c.CountPerBucket = append([]int64(nil), a.CountPerBucket...)
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c.ExemplarsPerBucket = append([]*exemplar.Exemplar(nil), a.ExemplarsPerBucket...)
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return &c
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}
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func (a *DistributionData) equal(other AggregationData) bool {
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a2, ok := other.(*DistributionData)
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if !ok {
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return false
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}
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if a2 == nil {
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return false
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}
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if len(a.CountPerBucket) != len(a2.CountPerBucket) {
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return false
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}
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for i := range a.CountPerBucket {
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if a.CountPerBucket[i] != a2.CountPerBucket[i] {
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return false
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}
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}
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return a.Count == a2.Count && a.Min == a2.Min && a.Max == a2.Max && math.Pow(a.Mean-a2.Mean, 2) < epsilon && math.Pow(a.variance()-a2.variance(), 2) < epsilon
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}
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// LastValueData returns the last value recorded for LastValue aggregation.
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type LastValueData struct {
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Value float64
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}
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func (l *LastValueData) isAggregationData() bool {
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return true
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}
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func (l *LastValueData) addSample(e *exemplar.Exemplar) {
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l.Value = e.Value
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}
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func (l *LastValueData) clone() AggregationData {
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return &LastValueData{l.Value}
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}
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func (l *LastValueData) equal(other AggregationData) bool {
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a2, ok := other.(*LastValueData)
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if !ok {
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return false
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}
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return l.Value == a2.Value
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}
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