deps: update buildkit, vendor changes

Signed-off-by: Laura Brehm <laurabrehm@hey.com>

vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go

@@ -0,0 +1,222 @@
+// Copyright The OpenTelemetry Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
+
+import (
+	"context"
+	"sync"
+	"time"
+
+	"go.opentelemetry.io/otel/attribute"
+	"go.opentelemetry.io/otel/sdk/metric/metricdata"
+)
+
+// valueMap is the storage for sums.
+type valueMap[N int64 | float64] struct {
+	sync.Mutex
+	values map[attribute.Set]N
+}
+
+func newValueMap[N int64 | float64]() *valueMap[N] {
+	return &valueMap[N]{values: make(map[attribute.Set]N)}
+}
+
+func (s *valueMap[N]) measure(_ context.Context, value N, attr attribute.Set) {
+	s.Lock()
+	s.values[attr] += value
+	s.Unlock()
+}
+
+// newSum returns an aggregator that summarizes a set of measurements as their
+// arithmetic sum. Each sum is scoped by attributes and the aggregation cycle
+// the measurements were made in.
+func newSum[N int64 | float64](monotonic bool) *sum[N] {
+	return &sum[N]{
+		valueMap:  newValueMap[N](),
+		monotonic: monotonic,
+		start:     now(),
+	}
+}
+
+// sum summarizes a set of measurements made as their arithmetic sum.
+type sum[N int64 | float64] struct {
+	*valueMap[N]
+
+	monotonic bool
+	start     time.Time
+}
+
+func (s *sum[N]) delta(dest *metricdata.Aggregation) int {
+	t := now()
+
+	// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
+	// use the zero-value sData and hope for better alignment next cycle.
+	sData, _ := (*dest).(metricdata.Sum[N])
+	sData.Temporality = metricdata.DeltaTemporality
+	sData.IsMonotonic = s.monotonic
+
+	s.Lock()
+	defer s.Unlock()
+
+	n := len(s.values)
+	dPts := reset(sData.DataPoints, n, n)
+
+	var i int
+	for attr, value := range s.values {
+		dPts[i].Attributes = attr
+		dPts[i].StartTime = s.start
+		dPts[i].Time = t
+		dPts[i].Value = value
+		// Do not report stale values.
+		delete(s.values, attr)
+		i++
+	}
+	// The delta collection cycle resets.
+	s.start = t
+
+	sData.DataPoints = dPts
+	*dest = sData
+
+	return n
+}
+
+func (s *sum[N]) cumulative(dest *metricdata.Aggregation) int {
+	t := now()
+
+	// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
+	// use the zero-value sData and hope for better alignment next cycle.
+	sData, _ := (*dest).(metricdata.Sum[N])
+	sData.Temporality = metricdata.CumulativeTemporality
+	sData.IsMonotonic = s.monotonic
+
+	s.Lock()
+	defer s.Unlock()
+
+	n := len(s.values)
+	dPts := reset(sData.DataPoints, n, n)
+
+	var i int
+	for attr, value := range s.values {
+		dPts[i].Attributes = attr
+		dPts[i].StartTime = s.start
+		dPts[i].Time = t
+		dPts[i].Value = value
+		// TODO (#3006): This will use an unbounded amount of memory if there
+		// are unbounded number of attribute sets being aggregated. Attribute
+		// sets that become "stale" need to be forgotten so this will not
+		// overload the system.
+		i++
+	}
+
+	sData.DataPoints = dPts
+	*dest = sData
+
+	return n
+}
+
+// newPrecomputedSum returns an aggregator that summarizes a set of
+// observatrions as their arithmetic sum. Each sum is scoped by attributes and
+// the aggregation cycle the measurements were made in.
+func newPrecomputedSum[N int64 | float64](monotonic bool) *precomputedSum[N] {
+	return &precomputedSum[N]{
+		valueMap:  newValueMap[N](),
+		monotonic: monotonic,
+		start:     now(),
+	}
+}
+
+// precomputedSum summarizes a set of observatrions as their arithmetic sum.
+type precomputedSum[N int64 | float64] struct {
+	*valueMap[N]
+
+	monotonic bool
+	start     time.Time
+
+	reported map[attribute.Set]N
+}
+
+func (s *precomputedSum[N]) delta(dest *metricdata.Aggregation) int {
+	t := now()
+	newReported := make(map[attribute.Set]N)
+
+	// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
+	// use the zero-value sData and hope for better alignment next cycle.
+	sData, _ := (*dest).(metricdata.Sum[N])
+	sData.Temporality = metricdata.DeltaTemporality
+	sData.IsMonotonic = s.monotonic
+
+	s.Lock()
+	defer s.Unlock()
+
+	n := len(s.values)
+	dPts := reset(sData.DataPoints, n, n)
+
+	var i int
+	for attr, value := range s.values {
+		delta := value - s.reported[attr]
+
+		dPts[i].Attributes = attr
+		dPts[i].StartTime = s.start
+		dPts[i].Time = t
+		dPts[i].Value = delta
+
+		newReported[attr] = value
+		// Unused attribute sets do not report.
+		delete(s.values, attr)
+		i++
+	}
+	// Unused attribute sets are forgotten.
+	s.reported = newReported
+	// The delta collection cycle resets.
+	s.start = t
+
+	sData.DataPoints = dPts
+	*dest = sData
+
+	return n
+}
+
+func (s *precomputedSum[N]) cumulative(dest *metricdata.Aggregation) int {
+	t := now()
+
+	// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
+	// use the zero-value sData and hope for better alignment next cycle.
+	sData, _ := (*dest).(metricdata.Sum[N])
+	sData.Temporality = metricdata.CumulativeTemporality
+	sData.IsMonotonic = s.monotonic
+
+	s.Lock()
+	defer s.Unlock()
+
+	n := len(s.values)
+	dPts := reset(sData.DataPoints, n, n)
+
+	var i int
+	for attr, value := range s.values {
+		dPts[i].Attributes = attr
+		dPts[i].StartTime = s.start
+		dPts[i].Time = t
+		dPts[i].Value = value
+
+		// Unused attribute sets do not report.
+		delete(s.values, attr)
+		i++
+	}
+
+	sData.DataPoints = dPts
+	*dest = sData
+
+	return n
+}