Remove all mentions of mean aggregation (#651)

Fixes #650.

README.md

@@ -103,18 +103,16 @@ set of recorded data points (measurements).
 
 Views have two parts: the tags to group by and the aggregation type used.
 
-Currently four types of aggregations are supported:
+Currently three types of aggregations are supported:
 * CountAggregation is used to count the number of times a sample was recorded.
 * DistributionAggregation is used to provide a histogram of the values of the samples.
 * SumAggregation is used to sum up all sample values.
-* MeanAggregation is used to calculate the mean of sample values.
 
 [embedmd]:# (stats.go aggs)
 ```go
 distAgg := view.Distribution(0, 1<<32, 2<<32, 3<<32)
 countAgg := view.Count()
 sumAgg := view.Sum()
-meanAgg := view.Mean()
 ```
 
 Here we create a view with the DistributionAggregation over our measure.

exporter/prometheus/prometheus.go

@@ -144,10 +144,8 @@ func (o *Options) onError(err error) {
 // ExportView exports to the Prometheus if view data has one or more rows.
 // Each OpenCensus AggregationData will be converted to
 // corresponding Prometheus Metric: SumData will be converted
-// to Untyped Metric, CountData will be Counter Metric,
-// DistributionData will be Histogram Metric, and MeanData
-// will be Summary Metric. Please note the Summary Metric from
-// MeanData does not have any quantiles.
+// to Untyped Metric, CountData will be a Counter Metric,
+// DistributionData will be a Histogram Metric.
 func (e *Exporter) ExportView(vd *view.Data) {
 	if len(vd.Rows) == 0 {
 		return

exporter/stackdriver/stats.go

@@ -261,8 +261,6 @@ func (e *statsExporter) createMeasure(ctx context.Context, vd *view.Data) error
 		case *stats.Float64Measure:
 			valueType = metricpb.MetricDescriptor_DOUBLE
 		}
-	case view.AggTypeMean:
-		valueType = metricpb.MetricDescriptor_DISTRIBUTION
 	case view.AggTypeDistribution:
 		valueType = metricpb.MetricDescriptor_DISTRIBUTION
 	default:
@@ -392,7 +390,7 @@ func equalAggTagKeys(md *metricpb.MetricDescriptor, agg *view.Aggregation, keys
 	case metricpb.MetricDescriptor_DOUBLE:
 		aggTypeMatch = agg.Type == view.AggTypeSum
 	case metricpb.MetricDescriptor_DISTRIBUTION:
-		aggTypeMatch = agg.Type == view.AggTypeMean || agg.Type == view.AggTypeDistribution
+		aggTypeMatch = agg.Type == view.AggTypeDistribution
 	}
 
 	if !aggTypeMatch {

internal/readme/source.md

@@ -91,11 +91,10 @@ set of recorded data points (measurements).
 
 Views have two parts: the tags to group by and the aggregation type used.
 
-Currently four types of aggregations are supported:
+Currently three types of aggregations are supported:
 * CountAggregation is used to count the number of times a sample was recorded.
 * DistributionAggregation is used to provide a histogram of the values of the samples.
 * SumAggregation is used to sum up all sample values.
-* MeanAggregation is used to calculate the mean of sample values.
 
 [embedmd]:# (stats.go aggs)
 

stats/view/aggregation.go

@@ -24,12 +24,11 @@ const (
 	AggTypeNone         AggType = iota // no aggregation; reserved for future use.
 	AggTypeCount                       // the count aggregation, see Count.
 	AggTypeSum                         // the sum aggregation, see Sum.
-	AggTypeMean                        // the mean aggregation, see Mean.
 	AggTypeDistribution                // the distribution aggregation, see Distribution.
 )
 
 // Aggregation represents a data aggregation method. Use one of the functions:
-// Count, Sum, Mean, or Distribution to construct an Aggregation.
+// Count, Sum, or Distribution to construct an Aggregation.
 type Aggregation struct {
 	Type    AggType   // Type is the AggType of this Aggregation.
 	Buckets []float64 // Buckets are the bucket endpoints if this Aggregation represents a distribution, see Distribution.

stats/view/aggregation_data.go

@@ -44,7 +44,7 @@ func newCountData(v int64) *CountData {
 
 func (a *CountData) isAggregationData() bool { return true }
 
-func (a *CountData) addSample(_ float64) {
+func (a *CountData) addSample(v float64) {
 	*a = *a + 1
 }
 

stats/view/doc.go

@@ -21,11 +21,10 @@ A view allows recorded measurements to be filtered and aggregated over a time wi
 
 All recorded measurements can be filtered by a list of tags.
 
-OpenCensus provides several aggregation methods: count, distribution, sum and mean.
+OpenCensus provides several aggregation methods: count, distribution and sum.
 Count aggregation only counts the number of measurement points. Distribution
 aggregation provides statistical summary of the aggregated data. Sum distribution
-sums up the measurement points. Mean provides the mean of the recorded measurements.
-Aggregations can either happen cumulatively or over an interval.
+sums up the measurement points. Aggregations are cumulative.
 
 Users can dynamically create and delete views.
 

stats/view/view.go

@@ -176,7 +176,7 @@ func (r *Row) String() string {
 	return buffer.String()
 }
 
-// same returns true if both Rows are equal. Tags are expected to be ordered
+// Equal returns true if both rows are equal. Tags are expected to be ordered
 // by the key name. Even both rows have the same tags but the tags appear in
 // different orders it will return false.
 func (r *Row) Equal(other *Row) bool {