func()

in exporter/collector/metrics.go [1023:1068]

• 38 lines of code
• 6 McCabe index (conditional complexity)

func (m *metricMapper) histogramPoint(point pmetric.HistogramDataPoint, projectID string) *monitoringpb.TypedValue {
	counts := make([]int64, point.BucketCounts().Len())
	var mean, deviation, prevBound float64

	for i := 0; i < point.BucketCounts().Len(); i++ {
		counts[i] = int64(point.BucketCounts().At(i))
	}

	if !math.IsNaN(point.Sum()) && point.Count() > 0 { // Avoid divide-by-zero
		mean = float64(point.Sum() / float64(point.Count()))
	}

	bounds := point.ExplicitBounds()
	if m.cfg.MetricConfig.EnableSumOfSquaredDeviation {
		// Calculate the sum of squared deviation.
		for i := 0; i < bounds.Len(); i++ {
			// Assume all points in the bucket occur at the middle of the bucket range
			middleOfBucket := (prevBound + bounds.At(i)) / 2
			deviation += float64(counts[i]) * (middleOfBucket - mean) * (middleOfBucket - mean)
			prevBound = bounds.At(i)
		}
		// The infinity bucket is an implicit +Inf bound after the list of explicit bounds.
		// Assume points in the infinity bucket are at the top of the previous bucket
		middleOfInfBucket := prevBound
		deviation += float64(counts[len(counts)-1]) * (middleOfInfBucket - mean) * (middleOfInfBucket - mean)
	}

	return &monitoringpb.TypedValue{
		Value: &monitoringpb.TypedValue_DistributionValue{
			DistributionValue: &distribution.Distribution{
				Count:                 int64(point.Count()),
				Mean:                  mean,
				BucketCounts:          counts,
				SumOfSquaredDeviation: deviation,
				BucketOptions: &distribution.Distribution_BucketOptions{
					Options: &distribution.Distribution_BucketOptions_ExplicitBuckets{
						ExplicitBuckets: &distribution.Distribution_BucketOptions_Explicit{
							Bounds: bounds.AsRaw(),
						},
					},
				},
				Exemplars: m.exemplars(point.Exemplars(), projectID),
			},
		},
	}
}