// signatureFunc returns a function that calculates the signature for a metric
// based on the provided labels.
func signatureFunc(labels ...clientmodel.LabelName) func(m clientmodel.COWMetric) uint64 {
if len(labels) == 0 {
return func(m clientmodel.COWMetric) uint64 {
m.Delete(clientmodel.MetricNameLabel)
return uint64(m.Metric.Fingerprint())
}
}
return func(m clientmodel.COWMetric) uint64 {
return clientmodel.SignatureForLabels(m.Metric, labels)
}
}
// vectorBinop evaluates a binary operation between two vector, excluding AND and OR.
func (ev *evaluator) vectorBinop(op itemType, lhs, rhs Vector, matching *VectorMatching) Vector {
if matching.Card == CardManyToMany {
panic("many-to-many only allowed for AND and OR")
}
var (
result = Vector{}
sigf = signatureFunc(matching.On...)
resultLabels = append(matching.On, matching.Include...)
)
// The control flow below handles one-to-one or many-to-one matching.
// For one-to-many, swap sidedness and account for the swap when calculating
// values.
if matching.Card == CardOneToMany {
lhs, rhs = rhs, lhs
}
// All samples from the rhs hashed by the matching label/values.
rightSigs := map[uint64]*Sample{}
// Add all rhs samples to a map so we can easily find matches later.
for _, rs := range rhs {
sig := sigf(rs.Metric)
// The rhs is guaranteed to be the 'one' side. Having multiple samples
// with the same signature means that the matching is many-to-many.
if _, found := rightSigs[sig]; found {
// Many-to-many matching not allowed.
ev.errorf("many-to-many matching not allowed: matching labels must be unique on one side")
}
rightSigs[sig] = rs
}
// Tracks the match-signature. For one-to-one operations the value is nil. For many-to-one
// the value is a set of signatures to detect duplicated result elements.
matchedSigs := map[uint64]map[uint64]struct{}{}
// For all lhs samples find a respective rhs sample and perform
// the binary operation.
for _, ls := range lhs {
sig := sigf(ls.Metric)
rs, found := rightSigs[sig] // Look for a match in the rhs vector.
if !found {
continue
}
// Account for potentially swapped sidedness.
vl, vr := ls.Value, rs.Value
if matching.Card == CardOneToMany {
vl, vr = vr, vl
}
value, keep := vectorElemBinop(op, vl, vr)
if !keep {
continue
}
metric := resultMetric(ls.Metric, op, resultLabels...)
insertedSigs, exists := matchedSigs[sig]
if matching.Card == CardOneToOne {
if exists {
ev.errorf("multiple matches for labels: many-to-one matching must be explicit (group_left/group_right)")
}
matchedSigs[sig] = nil // Set existance to true.
} else {
// In many-to-one matching the grouping labels have to ensure a unique metric
// for the result vector. Check whether those labels have already been added for
// the same matching labels.
insertSig := clientmodel.SignatureForLabels(metric.Metric, matching.Include)
if !exists {
insertedSigs = map[uint64]struct{}{}
matchedSigs[sig] = insertedSigs
} else if _, duplicate := insertedSigs[insertSig]; duplicate {
ev.errorf("multiple matches for labels: grouping labels must ensure unique matches")
}
insertedSigs[insertSig] = struct{}{}
}
result = append(result, &Sample{
Metric: metric,
Value: value,
Timestamp: ev.Timestamp,
})
}
return result
}
// 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
}