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This commit is contained in:
Manuel Alejandro de Brito Fontes 2019-04-15 08:34:23 -04:00
parent 461954facb
commit 1720059244
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763 changed files with 24896 additions and 177398 deletions
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138
vendor/go.opencensus.io/stats/view/aggregation_data.go generated vendored
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@ -17,8 +17,9 @@ package view
import (
"math"
"time"
"go.opencensus.io/exemplar"
"go.opencensus.io/metric/metricdata"
)
// AggregationData represents an aggregated value from a collection.
@ -26,9 +27,10 @@ import (
// Mosts users won't directly access aggregration data.
type AggregationData interface {
isAggregationData() bool
addSample(e *exemplar.Exemplar)
addSample(v float64, attachments map[string]interface{}, t time.Time)
clone() AggregationData
equal(other AggregationData) bool
toPoint(t metricdata.Type, time time.Time) metricdata.Point
}
const epsilon = 1e-9
@ -43,7 +45,7 @@ type CountData struct {
func (a *CountData) isAggregationData() bool { return true }
func (a *CountData) addSample(_ *exemplar.Exemplar) {
func (a *CountData) addSample(_ float64, _ map[string]interface{}, _ time.Time) {
a.Value = a.Value + 1
}
@ -60,6 +62,15 @@ func (a *CountData) equal(other AggregationData) bool {
return a.Value == a2.Value
}
func (a *CountData) toPoint(metricType metricdata.Type, t time.Time) metricdata.Point {
switch metricType {
case metricdata.TypeCumulativeInt64:
return metricdata.NewInt64Point(t, a.Value)
default:
panic("unsupported metricdata.Type")
}
}
// SumData is the aggregated data for the Sum aggregation.
// A sum aggregation processes data and sums up the recordings.
//
@ -70,8 +81,8 @@ type SumData struct {
func (a *SumData) isAggregationData() bool { return true }
func (a *SumData) addSample(e *exemplar.Exemplar) {
a.Value += e.Value
func (a *SumData) addSample(v float64, _ map[string]interface{}, _ time.Time) {
a.Value += v
}
func (a *SumData) clone() AggregationData {
@ -86,6 +97,17 @@ func (a *SumData) equal(other AggregationData) bool {
return math.Pow(a.Value-a2.Value, 2) < epsilon
}
func (a *SumData) toPoint(metricType metricdata.Type, t time.Time) metricdata.Point {
switch metricType {
case metricdata.TypeCumulativeInt64:
return metricdata.NewInt64Point(t, int64(a.Value))
case metricdata.TypeCumulativeFloat64:
return metricdata.NewFloat64Point(t, a.Value)
default:
panic("unsupported metricdata.Type")
}
}
// DistributionData is the aggregated data for the
// Distribution aggregation.
//
@ -102,7 +124,7 @@ type DistributionData struct {
CountPerBucket []int64 // number of occurrences per bucket
// ExemplarsPerBucket is slice the same length as CountPerBucket containing
// an exemplar for the associated bucket, or nil.
ExemplarsPerBucket []*exemplar.Exemplar
ExemplarsPerBucket []*metricdata.Exemplar
bounds []float64 // histogram distribution of the values
}
@ -110,7 +132,7 @@ func newDistributionData(bounds []float64) *DistributionData {
bucketCount := len(bounds) + 1
return &DistributionData{
CountPerBucket: make([]int64, bucketCount),
ExemplarsPerBucket: make([]*exemplar.Exemplar, bucketCount),
ExemplarsPerBucket: make([]*metricdata.Exemplar, bucketCount),
bounds: bounds,
Min: math.MaxFloat64,
Max: math.SmallestNonzeroFloat64,
@ -129,64 +151,62 @@ func (a *DistributionData) variance() float64 {
func (a *DistributionData) isAggregationData() bool { return true }
func (a *DistributionData) addSample(e *exemplar.Exemplar) {
f := e.Value
if f < a.Min {
a.Min = f
// TODO(songy23): support exemplar attachments.
func (a *DistributionData) addSample(v float64, attachments map[string]interface{}, t time.Time) {
if v < a.Min {
a.Min = v
}
if f > a.Max {
a.Max = f
if v > a.Max {
a.Max = v
}
a.Count++
a.addToBucket(e)
a.addToBucket(v, attachments, t)
if a.Count == 1 {
a.Mean = f
a.Mean = v
return
}
oldMean := a.Mean
a.Mean = a.Mean + (f-a.Mean)/float64(a.Count)
a.SumOfSquaredDev = a.SumOfSquaredDev + (f-oldMean)*(f-a.Mean)
a.Mean = a.Mean + (v-a.Mean)/float64(a.Count)
a.SumOfSquaredDev = a.SumOfSquaredDev + (v-oldMean)*(v-a.Mean)
}
func (a *DistributionData) addToBucket(e *exemplar.Exemplar) {
func (a *DistributionData) addToBucket(v float64, attachments map[string]interface{}, t time.Time) {
var count *int64
var ex **exemplar.Exemplar
for i, b := range a.bounds {
if e.Value < b {
var i int
var b float64
for i, b = range a.bounds {
if v < b {
count = &a.CountPerBucket[i]
ex = &a.ExemplarsPerBucket[i]
break
}
}
if count == nil {
count = &a.CountPerBucket[len(a.bounds)]
ex = &a.ExemplarsPerBucket[len(a.bounds)]
if count == nil { // Last bucket.
i = len(a.bounds)
count = &a.CountPerBucket[i]
}
*count++
*ex = maybeRetainExemplar(*ex, e)
if exemplar := getExemplar(v, attachments, t); exemplar != nil {
a.ExemplarsPerBucket[i] = exemplar
}
}
func maybeRetainExemplar(old, cur *exemplar.Exemplar) *exemplar.Exemplar {
if old == nil {
return cur
func getExemplar(v float64, attachments map[string]interface{}, t time.Time) *metricdata.Exemplar {
if len(attachments) == 0 {
return nil
}
// Heuristic to pick the "better" exemplar: first keep the one with a
// sampled trace attachment, if neither have a trace attachment, pick the
// one with more attachments.
_, haveTraceID := cur.Attachments[exemplar.KeyTraceID]
if haveTraceID || len(cur.Attachments) >= len(old.Attachments) {
return cur
return &metricdata.Exemplar{
Value: v,
Timestamp: t,
Attachments: attachments,
}
return old
}
func (a *DistributionData) clone() AggregationData {
c := *a
c.CountPerBucket = append([]int64(nil), a.CountPerBucket...)
c.ExemplarsPerBucket = append([]*exemplar.Exemplar(nil), a.ExemplarsPerBucket...)
c.ExemplarsPerBucket = append([]*metricdata.Exemplar(nil), a.ExemplarsPerBucket...)
return &c
}
@ -209,6 +229,33 @@ func (a *DistributionData) equal(other AggregationData) bool {
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
}
func (a *DistributionData) toPoint(metricType metricdata.Type, t time.Time) metricdata.Point {
switch metricType {
case metricdata.TypeCumulativeDistribution:
buckets := []metricdata.Bucket{}
for i := 0; i < len(a.CountPerBucket); i++ {
buckets = append(buckets, metricdata.Bucket{
Count: a.CountPerBucket[i],
Exemplar: a.ExemplarsPerBucket[i],
})
}
bucketOptions := &metricdata.BucketOptions{Bounds: a.bounds}
val := &metricdata.Distribution{
Count: a.Count,
Sum: a.Sum(),
SumOfSquaredDeviation: a.SumOfSquaredDev,
BucketOptions: bucketOptions,
Buckets: buckets,
}
return metricdata.NewDistributionPoint(t, val)
default:
// TODO: [rghetia] when we have a use case for TypeGaugeDistribution.
panic("unsupported metricdata.Type")
}
}
// LastValueData returns the last value recorded for LastValue aggregation.
type LastValueData struct {
Value float64
@ -218,8 +265,8 @@ func (l *LastValueData) isAggregationData() bool {
return true
}
func (l *LastValueData) addSample(e *exemplar.Exemplar) {
l.Value = e.Value
func (l *LastValueData) addSample(v float64, _ map[string]interface{}, _ time.Time) {
l.Value = v
}
func (l *LastValueData) clone() AggregationData {
@ -233,3 +280,14 @@ func (l *LastValueData) equal(other AggregationData) bool {
}
return l.Value == a2.Value
}
func (l *LastValueData) toPoint(metricType metricdata.Type, t time.Time) metricdata.Point {
switch metricType {
case metricdata.TypeGaugeInt64:
return metricdata.NewInt64Point(t, int64(l.Value))
case metricdata.TypeGaugeFloat64:
return metricdata.NewFloat64Point(t, l.Value)
default:
panic("unsupported metricdata.Type")
}
}