// labelIntersection returns the metric of common label/value pairs of two input metrics.
func labelIntersection(metric1, metric2 model.COWMetric) model.COWMetric {
for label, value := range metric1.Metric {
if metric2.Metric[label] != value {
metric1.Del(label)
}
}
return metric1
}
// resultMetric returns the metric for the given sample(s) based on the vector
// binary operation and the matching options.
func resultMetric(met model.COWMetric, op itemType, labels ...model.LabelName) model.COWMetric {
if len(labels) == 0 {
if shouldDropMetricName(op) {
met.Del(model.MetricNameLabel)
}
return met
}
// As we definitly write, creating a new metric is the easiest solution.
m := model.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 model.COWMetric{Metric: m, Copied: false}
}
// aggregation evaluates an aggregation operation on a vector.
func (ev *evaluator) aggregation(op itemType, grouping model.LabelNames, keepExtra bool, vector Vector) Vector {
result := map[uint64]*groupedAggregation{}
for _, sample := range vector {
groupingKey := model.SignatureForLabels(sample.Metric.Metric, grouping...)
groupedResult, ok := result[groupingKey]
// Add a new group if it doesn't exist.
if !ok {
var m model.COWMetric
if keepExtra {
m = sample.Metric
m.Del(model.MetricNameLabel)
} else {
m = model.COWMetric{
Metric: model.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 / model.SampleValue(aggr.groupCount)
case itemCount:
aggr.value = model.SampleValue(aggr.groupCount)
case itemStdvar:
avg := float64(aggr.value) / float64(aggr.groupCount)
aggr.value = model.SampleValue(float64(aggr.valuesSquaredSum)/float64(aggr.groupCount) - avg*avg)
case itemStddev:
avg := float64(aggr.value) / float64(aggr.groupCount)
aggr.value = model.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
}