// resultMetric returns the metric for the given sample(s) based on the vector
// binary operation and the matching options.
func resultMetric(lhs, rhs metric.Metric, op itemType, matching *VectorMatching) metric.Metric {
if shouldDropMetricName(op) {
lhs.Del(model.MetricNameLabel)
}
if !matching.On {
if matching.Card == CardOneToOne {
for _, l := range matching.MatchingLabels {
lhs.Del(l)
}
}
for _, ln := range matching.Include {
// Included labels from the `group_x` modifier are taken from the "one"-side.
value := rhs.Metric[ln]
if value != "" {
lhs.Set(ln, rhs.Metric[ln])
} else {
lhs.Del(ln)
}
}
return lhs
}
// As we definitely write, creating a new metric is the easiest solution.
m := model.Metric{}
if matching.Card == CardOneToOne {
for _, ln := range matching.MatchingLabels {
if v, ok := lhs.Metric[ln]; ok {
m[ln] = v
}
}
} else {
for k, v := range lhs.Metric {
m[k] = v
}
}
for _, ln := range matching.Include {
// Included labels from the `group_x` modifier are taken from the "one"-side .
if v, ok := rhs.Metric[ln]; ok {
m[ln] = v
} else {
delete(m, ln)
}
}
return metric.Metric{Metric: m, Copied: false}
}
// aggregation evaluates an aggregation operation on a vector.
func (ev *evaluator) aggregation(op itemType, grouping model.LabelNames, without bool, keepCommon bool, param Expr, vec vector) vector {
result := map[uint64]*groupedAggregation{}
var k int64
if op == itemTopK || op == itemBottomK {
k = ev.evalInt(param)
if k < 1 {
return vector{}
}
}
var q float64
if op == itemQuantile {
q = ev.evalFloat(param)
}
var valueLabel model.LabelName
if op == itemCountValues {
valueLabel = model.LabelName(ev.evalString(param).Value)
if !without {
grouping = append(grouping, valueLabel)
}
}
for _, s := range vec {
withoutMetric := s.Metric
if without {
for _, l := range grouping {
withoutMetric.Del(l)
}
withoutMetric.Del(model.MetricNameLabel)
if op == itemCountValues {
withoutMetric.Set(valueLabel, model.LabelValue(s.Value.String()))
}
} else {
if op == itemCountValues {
s.Metric.Set(valueLabel, model.LabelValue(s.Value.String()))
}
}
var groupingKey uint64
if without {
groupingKey = uint64(withoutMetric.Metric.Fingerprint())
} else {
groupingKey = model.SignatureForLabels(s.Metric.Metric, grouping...)
}
groupedResult, ok := result[groupingKey]
// Add a new group if it doesn't exist.
if !ok {
var m metric.Metric
if keepCommon {
m = s.Metric
m.Del(model.MetricNameLabel)
} else if without {
m = withoutMetric
} else {
m = metric.Metric{
Metric: model.Metric{},
Copied: true,
}
for _, l := range grouping {
if v, ok := s.Metric.Metric[l]; ok {
m.Set(l, v)
}
}
}
result[groupingKey] = &groupedAggregation{
labels: m,
value: s.Value,
valuesSquaredSum: s.Value * s.Value,
groupCount: 1,
}
if op == itemTopK || op == itemQuantile {
result[groupingKey].heap = make(vectorByValueHeap, 0, k)
heap.Push(&result[groupingKey].heap, &sample{Value: s.Value, Metric: s.Metric})
} else if op == itemBottomK {
result[groupingKey].reverseHeap = make(vectorByReverseValueHeap, 0, k)
heap.Push(&result[groupingKey].reverseHeap, &sample{Value: s.Value, Metric: s.Metric})
}
continue
}
// Add the sample to the existing group.
if keepCommon {
groupedResult.labels = labelIntersection(groupedResult.labels, s.Metric)
}
switch op {
case itemSum:
groupedResult.value += s.Value
case itemAvg:
groupedResult.value += s.Value
groupedResult.groupCount++
case itemMax:
if groupedResult.value < s.Value || math.IsNaN(float64(groupedResult.value)) {
groupedResult.value = s.Value
}
case itemMin:
if groupedResult.value > s.Value || math.IsNaN(float64(groupedResult.value)) {
groupedResult.value = s.Value
}
case itemCount, itemCountValues:
groupedResult.groupCount++
case itemStdvar, itemStddev:
groupedResult.value += s.Value
groupedResult.valuesSquaredSum += s.Value * s.Value
groupedResult.groupCount++
case itemTopK:
if int64(len(groupedResult.heap)) < k || groupedResult.heap[0].Value < s.Value || math.IsNaN(float64(groupedResult.heap[0].Value)) {
if int64(len(groupedResult.heap)) == k {
heap.Pop(&groupedResult.heap)
}
heap.Push(&groupedResult.heap, &sample{Value: s.Value, Metric: s.Metric})
}
case itemBottomK:
if int64(len(groupedResult.reverseHeap)) < k || groupedResult.reverseHeap[0].Value > s.Value || math.IsNaN(float64(groupedResult.reverseHeap[0].Value)) {
if int64(len(groupedResult.reverseHeap)) == k {
heap.Pop(&groupedResult.reverseHeap)
}
heap.Push(&groupedResult.reverseHeap, &sample{Value: s.Value, Metric: s.Metric})
}
case itemQuantile:
groupedResult.heap = append(groupedResult.heap, s)
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, itemCountValues:
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))
case itemTopK:
// The heap keeps the lowest value on top, so reverse it.
sort.Sort(sort.Reverse(aggr.heap))
for _, v := range aggr.heap {
resultVector = append(resultVector, &sample{
Metric: v.Metric,
Value: v.Value,
Timestamp: ev.Timestamp,
})
}
continue // Bypass default append.
case itemBottomK:
// The heap keeps the lowest value on top, so reverse it.
sort.Sort(sort.Reverse(aggr.reverseHeap))
for _, v := range aggr.reverseHeap {
resultVector = append(resultVector, &sample{
Metric: v.Metric,
Value: v.Value,
Timestamp: ev.Timestamp,
})
}
continue // Bypass default append.
case itemQuantile:
aggr.value = model.SampleValue(quantile(q, aggr.heap))
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
}
// aggregation evaluates an aggregation operation on a vector.
func (ev *evaluator) aggregation(op itemType, grouping model.LabelNames, without bool, keepExtra bool, vec vector) vector {
result := map[uint64]*groupedAggregation{}
for _, sample := range vec {
withoutMetric := sample.Metric
if without {
for _, l := range grouping {
withoutMetric.Del(l)
}
withoutMetric.Del(model.MetricNameLabel)
}
var groupingKey uint64
if without {
groupingKey = uint64(withoutMetric.Metric.Fingerprint())
} else {
groupingKey = model.SignatureForLabels(sample.Metric.Metric, grouping...)
}
groupedResult, ok := result[groupingKey]
// Add a new group if it doesn't exist.
if !ok {
var m metric.Metric
if keepExtra {
m = sample.Metric
m.Del(model.MetricNameLabel)
} else if without {
m = withoutMetric
} else {
m = metric.Metric{
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 || math.IsNaN(float64(groupedResult.value)) {
groupedResult.value = sample.Value
}
case itemMin:
if groupedResult.value > sample.Value || math.IsNaN(float64(groupedResult.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
}