Golang model.SignatureForLabels函数代码示例

// signatureFunc returns a function that calculates the signature for a metric
// based on the provided labels.
func signatureFunc(labels ...model.LabelName) func(m metric.Metric) uint64 {
	if len(labels) == 0 {
		return func(m metric.Metric) uint64 {
			m.Del(model.MetricNameLabel)
			return uint64(m.Metric.Fingerprint())
		}
	}
	return func(m metric.Metric) uint64 {
		return model.SignatureForLabels(m.Metric, labels...)
	}
}

// signatureFunc returns a function that calculates the signature for a metric
// ignoring the provided labels. If on, then the given labels are only used instead.
func signatureFunc(on bool, labels ...model.LabelName) func(m metric.Metric) uint64 {
	if !on {
		return func(m metric.Metric) uint64 {
			tmp := m.Metric.Clone()
			for _, l := range labels {
				delete(tmp, l)
			}
			delete(tmp, model.MetricNameLabel)
			return uint64(tmp.Fingerprint())
		}
	}
	return func(m metric.Metric) uint64 {
		return model.SignatureForLabels(m.Metric, labels...)
	}
}

// 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
			}
//.........这里部分代码省略.........

// vectorBinop evaluates a binary operation between two vector, excluding AND and OR.
func (ev *evaluator) vectorBinop(op itemType, lhs, rhs vector, matching *VectorMatching, returnBool bool) 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 returnBool {
			if keep {
				value = 1.0
			} else {
				value = 0.0
			}
		} else 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 existence 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 := model.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 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,
//.........这里部分代码省略.........