Golang COWMetric.Delete方法代码示例

// labelIntersection returns the metric of common label/value pairs of two input metrics.
func labelIntersection(metric1, metric2 clientmodel.COWMetric) clientmodel.COWMetric {
	for label, value := range metric1.Metric {
		if metric2.Metric[label] != value {
			metric1.Delete(label)
		}
	}
	return metric1
}

// Eval implements the VectorNode interface and returns the aggregated
// Vector.
func (node *VectorAggregation) Eval(timestamp clientmodel.Timestamp) Vector {
	vector := node.vector.Eval(timestamp)
	result := map[uint64]*groupedAggregation{}
	for _, sample := range vector {
		groupingKey := node.labelsToGroupingKey(sample.Metric.Metric)
		if groupedResult, ok := result[groupingKey]; ok {
			if node.keepExtraLabels {
				groupedResult.labels = labelIntersection(groupedResult.labels, sample.Metric)
			}

			switch node.aggrType {
			case Sum:
				groupedResult.value += sample.Value
			case Avg:
				groupedResult.value += sample.Value
				groupedResult.groupCount++
			case Max:
				if groupedResult.value < sample.Value {
					groupedResult.value = sample.Value
				}
			case Min:
				if groupedResult.value > sample.Value {
					groupedResult.value = sample.Value
				}
			case Count:
				groupedResult.groupCount++
			default:
				panic("Unknown aggregation type")
			}
		} else {
			var m clientmodel.COWMetric
			if node.keepExtraLabels {
				m = sample.Metric
				m.Delete(clientmodel.MetricNameLabel)
			} else {
				m = clientmodel.COWMetric{
					Metric: clientmodel.Metric{},
					Copied: true,
				}
				for _, l := range node.groupBy {
					if v, ok := sample.Metric.Metric[l]; ok {
						m.Set(l, v)
					}
				}
			}
			result[groupingKey] = &groupedAggregation{
				labels:     m,
				value:      sample.Value,
				groupCount: 1,
			}
		}
	}

	return node.groupedAggregationsToVector(result, timestamp)
}

// resultMetric returns the metric for the given sample(s) based on the vector
// binary operation and the matching options.
func resultMetric(met clientmodel.COWMetric, op itemType, labels ...clientmodel.LabelName) clientmodel.COWMetric {
	if len(labels) == 0 {
		if shouldDropMetricName(op) {
			met.Delete(clientmodel.MetricNameLabel)
		}
		return met
	}
	// As we definitly write, creating a new metric is the easiest solution.
	m := clientmodel.Metric{}
	for _, ln := range labels {
		// Included labels from the `group_x` modifier are taken from the "many"-side.
		if v, ok := met.Metric[ln]; ok {
			m[ln] = v
		}
	}
	return clientmodel.COWMetric{Metric: m, Copied: false}
}

// aggregation evaluates an aggregation operation on a vector.
func (ev *evaluator) aggregation(op itemType, grouping clientmodel.LabelNames, keepExtra bool, vector Vector) Vector {

	result := map[uint64]*groupedAggregation{}

	for _, sample := range vector {
		groupingKey := clientmodel.SignatureForLabels(sample.Metric.Metric, grouping)

		groupedResult, ok := result[groupingKey]
		// Add a new group if it doesn't exist.
		if !ok {
			var m clientmodel.COWMetric
			if keepExtra {
				m = sample.Metric
				m.Delete(clientmodel.MetricNameLabel)
			} else {
				m = clientmodel.COWMetric{
					Metric: clientmodel.Metric{},
					Copied: true,
				}
				for _, l := range grouping {
					if v, ok := sample.Metric.Metric[l]; ok {
						m.Set(l, v)
					}
				}
			}
			result[groupingKey] = &groupedAggregation{
				labels:           m,
				value:            sample.Value,
				valuesSquaredSum: sample.Value * sample.Value,
				groupCount:       1,
			}
			continue
		}
		// Add the sample to the existing group.
		if keepExtra {
			groupedResult.labels = labelIntersection(groupedResult.labels, sample.Metric)
		}

		switch op {
		case itemSum:
			groupedResult.value += sample.Value
		case itemAvg:
			groupedResult.value += sample.Value
			groupedResult.groupCount++
		case itemMax:
			if groupedResult.value < sample.Value {
				groupedResult.value = sample.Value
			}
		case itemMin:
			if groupedResult.value > sample.Value {
				groupedResult.value = sample.Value
			}
		case itemCount:
			groupedResult.groupCount++
		case itemStdvar, itemStddev:
			groupedResult.value += sample.Value
			groupedResult.valuesSquaredSum += sample.Value * sample.Value
			groupedResult.groupCount++
		default:
			panic(fmt.Errorf("expected aggregation operator but got %q", op))
		}
	}

	// Construct the result vector from the aggregated groups.
	resultVector := make(Vector, 0, len(result))

	for _, aggr := range result {
		switch op {
		case itemAvg:
			aggr.value = aggr.value / clientmodel.SampleValue(aggr.groupCount)
		case itemCount:
			aggr.value = clientmodel.SampleValue(aggr.groupCount)
		case itemStdvar:
			avg := float64(aggr.value) / float64(aggr.groupCount)
			aggr.value = clientmodel.SampleValue(float64(aggr.valuesSquaredSum)/float64(aggr.groupCount) - avg*avg)
		case itemStddev:
			avg := float64(aggr.value) / float64(aggr.groupCount)
			aggr.value = clientmodel.SampleValue(math.Sqrt(float64(aggr.valuesSquaredSum)/float64(aggr.groupCount) - avg*avg))
		default:
			// For other aggregations, we already have the right value.
		}
		sample := &Sample{
			Metric:    aggr.labels,
			Value:     aggr.value,
			Timestamp: ev.Timestamp,
		}
		resultVector = append(resultVector, sample)
	}
	return resultVector
}