stats worker as metric producer.

stats/view/aggregation_data.go

@@ -30,6 +30,7 @@ type AggregationData interface {
 	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
@@ -61,6 +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.
 //
@@ -87,6 +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.
 //
@@ -208,6 +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
@@ -232,3 +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")
+	}
+}

stats/view/view.go

@@ -24,6 +24,7 @@ import (
 	"sync/atomic"
 	"time"
 
+	"go.opencensus.io/metric/metricdata"
 	"go.opencensus.io/stats"
 	"go.opencensus.io/tag"
 )
@@ -116,15 +117,17 @@ func dropZeroBounds(bounds ...float64) []float64 {
 
 // viewInternal is the internal representation of a View.
 type viewInternal struct {
-	view       *View  // view is the canonicalized View definition associated with this view.
-	subscribed uint32 // 1 if someone is subscribed and data need to be exported, use atomic to access
-	collector  *collector
+	view             *View  // view is the canonicalized View definition associated with this view.
+	subscribed       uint32 // 1 if someone is subscribed and data need to be exported, use atomic to access
+	collector        *collector
+	metricDescriptor *metricdata.Descriptor
 }
 
 func newViewInternal(v *View) (*viewInternal, error) {
 	return &viewInternal{
-		view:      v,
-		collector: &collector{make(map[string]AggregationData), v.Aggregation},
+		view:             v,
+		collector:        &collector{make(map[string]AggregationData), v.Aggregation},
+		metricDescriptor: viewToMetricDescriptor(v),
 	}, nil
 }
 

stats/view/view_to_metric.go

@@ -0,0 +1,131 @@
+// Copyright 2019, OpenCensus Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+package view
+
+import (
+	"time"
+
+	"go.opencensus.io/metric/metricdata"
+	"go.opencensus.io/stats"
+)
+
+func getUnit(unit string) metricdata.Unit {
+	switch unit {
+	case "1":
+		return metricdata.UnitDimensionless
+	case "ms":
+		return metricdata.UnitMilliseconds
+	case "By":
+		return metricdata.UnitBytes
+	}
+	return metricdata.UnitDimensionless
+}
+
+func getType(v *View) metricdata.Type {
+	m := v.Measure
+	agg := v.Aggregation
+
+	switch agg.Type {
+	case AggTypeSum:
+		switch m.(type) {
+		case *stats.Int64Measure:
+			return metricdata.TypeCumulativeInt64
+		case *stats.Float64Measure:
+			return metricdata.TypeCumulativeFloat64
+		default:
+			panic("unexpected measure type")
+		}
+	case AggTypeDistribution:
+		return metricdata.TypeCumulativeDistribution
+	case AggTypeLastValue:
+		switch m.(type) {
+		case *stats.Int64Measure:
+			return metricdata.TypeGaugeInt64
+		case *stats.Float64Measure:
+			return metricdata.TypeGaugeFloat64
+		default:
+			panic("unexpected measure type")
+		}
+	case AggTypeCount:
+		switch m.(type) {
+		case *stats.Int64Measure:
+			return metricdata.TypeCumulativeInt64
+		case *stats.Float64Measure:
+			return metricdata.TypeCumulativeInt64
+		default:
+			panic("unexpected measure type")
+		}
+	default:
+		panic("unexpected aggregation type")
+	}
+}
+
+func getLableKeys(v *View) []string {
+	labelKeys := []string{}
+	for _, k := range v.TagKeys {
+		labelKeys = append(labelKeys, k.Name())
+	}
+	return labelKeys
+}
+
+func viewToMetricDescriptor(v *View) *metricdata.Descriptor {
+	return &metricdata.Descriptor{
+		Name:        v.Name,
+		Description: v.Description,
+		Unit:        getUnit(v.Measure.Unit()),
+		Type:        getType(v),
+		LabelKeys:   getLableKeys(v),
+	}
+}
+
+func toLabelValues(row *Row) []metricdata.LabelValue {
+	labelValues := []metricdata.LabelValue{}
+	for _, tag := range row.Tags {
+		labelValues = append(labelValues, metricdata.NewLabelValue(tag.Value))
+	}
+	return labelValues
+}
+
+func rowToTimeseries(v *viewInternal, row *Row, now time.Time, startTime time.Time) *metricdata.TimeSeries {
+	return &metricdata.TimeSeries{
+		Points:      []metricdata.Point{row.Data.toPoint(v.metricDescriptor.Type, now)},
+		LabelValues: toLabelValues(row),
+		StartTime:   startTime,
+	}
+}
+
+func viewToMetric(v *viewInternal, now time.Time, startTime time.Time) *metricdata.Metric {
+	if v.metricDescriptor.Type == metricdata.TypeGaugeInt64 ||
+		v.metricDescriptor.Type == metricdata.TypeGaugeFloat64 {
+		startTime = time.Time{}
+	}
+
+	rows := v.collectedRows()
+	if len(rows) == 0 {
+		return nil
+	}
+
+	ts := []*metricdata.TimeSeries{}
+	for _, row := range rows {
+		ts = append(ts, rowToTimeseries(v, row, now, startTime))
+	}
+
+	m := &metricdata.Metric{
+		Descriptor: *v.metricDescriptor,
+		TimeSeries: ts,
+	}
+	return m
+}