func()

in exporter/metric/metric.go [457:562]

• 104 lines of code
• 28 McCabe index (conditional complexity)

func (me *metricExporter) recordToTspb(m metricdata.Metrics, mr *monitoredrespb.MonitoredResource, library instrumentation.Scope, extraLabels *attribute.Set) ([]*monitoringpb.TimeSeries, error) {
	var tss []*monitoringpb.TimeSeries
	var errs []error
	if m.Data == nil {
		return nil, nil
	}
	switch a := m.Data.(type) {
	case metricdata.Gauge[int64]:
		for _, point := range a.DataPoints {
			ts, err := gaugeToTimeSeries[int64](point, m, mr)
			if err != nil {
				errs = append(errs, err)
				continue
			}
			ts.Metric = me.recordToMpb(m, point.Attributes, library, extraLabels)
			tss = append(tss, ts)
		}
	case metricdata.Gauge[float64]:
		for _, point := range a.DataPoints {
			ts, err := gaugeToTimeSeries[float64](point, m, mr)
			if err != nil {
				errs = append(errs, err)
				continue
			}
			ts.Metric = me.recordToMpb(m, point.Attributes, library, extraLabels)
			tss = append(tss, ts)
		}
	case metricdata.Sum[int64]:
		for _, point := range a.DataPoints {
			var ts *monitoringpb.TimeSeries
			var err error
			if a.IsMonotonic {
				ts, err = sumToTimeSeries[int64](point, m, mr)
			} else {
				// Send non-monotonic sums as gauges
				ts, err = gaugeToTimeSeries[int64](point, m, mr)
			}
			if err != nil {
				errs = append(errs, err)
				continue
			}
			ts.Metric = me.recordToMpb(m, point.Attributes, library, extraLabels)
			tss = append(tss, ts)
		}
	case metricdata.Sum[float64]:
		for _, point := range a.DataPoints {
			var ts *monitoringpb.TimeSeries
			var err error
			if a.IsMonotonic {
				ts, err = sumToTimeSeries[float64](point, m, mr)
			} else {
				// Send non-monotonic sums as gauges
				ts, err = gaugeToTimeSeries[float64](point, m, mr)
			}
			if err != nil {
				errs = append(errs, err)
				continue
			}
			ts.Metric = me.recordToMpb(m, point.Attributes, library, extraLabels)
			tss = append(tss, ts)
		}
	case metricdata.Histogram[int64]:
		for _, point := range a.DataPoints {
			ts, err := histogramToTimeSeries(point, m, mr, me.o.enableSumOfSquaredDeviation, me.o.projectID)
			if err != nil {
				errs = append(errs, err)
				continue
			}
			ts.Metric = me.recordToMpb(m, point.Attributes, library, extraLabels)
			tss = append(tss, ts)
		}
	case metricdata.Histogram[float64]:
		for _, point := range a.DataPoints {
			ts, err := histogramToTimeSeries(point, m, mr, me.o.enableSumOfSquaredDeviation, me.o.projectID)
			if err != nil {
				errs = append(errs, err)
				continue
			}
			ts.Metric = me.recordToMpb(m, point.Attributes, library, extraLabels)
			tss = append(tss, ts)
		}
	case metricdata.ExponentialHistogram[int64]:
		for _, point := range a.DataPoints {
			ts, err := expHistogramToTimeSeries(point, m, mr, me.o.enableSumOfSquaredDeviation, me.o.projectID)
			if err != nil {
				errs = append(errs, err)
				continue
			}
			ts.Metric = me.recordToMpb(m, point.Attributes, library, extraLabels)
			tss = append(tss, ts)
		}
	case metricdata.ExponentialHistogram[float64]:
		for _, point := range a.DataPoints {
			ts, err := expHistogramToTimeSeries(point, m, mr, me.o.enableSumOfSquaredDeviation, me.o.projectID)
			if err != nil {
				errs = append(errs, err)
				continue
			}
			ts.Metric = me.recordToMpb(m, point.Attributes, library, extraLabels)
			tss = append(tss, ts)
		}
	default:
		errs = append(errs, errUnexpectedAggregationKind{kind: reflect.TypeOf(m.Data).String()})
	}
	return tss, errors.Join(errs...)
}