1 files changed, 0 insertions, 237 deletions
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go
deleted file mode 100644
index 8e132ad61..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go
+++ /dev/null
@@ -1,237 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-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"
-)
-
-type sumValue[N int64 | float64] struct {
-	n     N
-	res   FilteredExemplarReservoir[N]
-	attrs attribute.Set
-}
-
-// valueMap is the storage for sums.
-type valueMap[N int64 | float64] struct {
-	sync.Mutex
-	newRes func(attribute.Set) FilteredExemplarReservoir[N]
-	limit  limiter[sumValue[N]]
-	values map[attribute.Distinct]sumValue[N]
-}
-
-func newValueMap[N int64 | float64](limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *valueMap[N] {
-	return &valueMap[N]{
-		newRes: r,
-		limit:  newLimiter[sumValue[N]](limit),
-		values: make(map[attribute.Distinct]sumValue[N]),
-	}
-}
-
-func (s *valueMap[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
-	s.Lock()
-	defer s.Unlock()
-
-	attr := s.limit.Attributes(fltrAttr, s.values)
-	v, ok := s.values[attr.Equivalent()]
-	if !ok {
-		v.res = s.newRes(attr)
-	}
-
-	v.attrs = attr
-	v.n += value
-	v.res.Offer(ctx, value, droppedAttr)
-
-	s.values[attr.Equivalent()] = v
-}
-
-// 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, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *sum[N] {
-	return &sum[N]{
-		valueMap:  newValueMap[N](limit, r),
-		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 _, val := range s.values {
-		dPts[i].Attributes = val.attrs
-		dPts[i].StartTime = s.start
-		dPts[i].Time = t
-		dPts[i].Value = val.n
-		collectExemplars(&dPts[i].Exemplars, val.res.Collect)
-		i++
-	}
-	// Do not report stale values.
-	clear(s.values)
-	// 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 _, value := range s.values {
-		dPts[i].Attributes = value.attrs
-		dPts[i].StartTime = s.start
-		dPts[i].Time = t
-		dPts[i].Value = value.n
-		collectExemplars(&dPts[i].Exemplars, value.res.Collect)
-		// 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
-// observations 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, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *precomputedSum[N] {
-	return &precomputedSum[N]{
-		valueMap:  newValueMap[N](limit, r),
-		monotonic: monotonic,
-		start:     now(),
-	}
-}
-
-// precomputedSum summarizes a set of observations as their arithmetic sum.
-type precomputedSum[N int64 | float64] struct {
-	*valueMap[N]
-
-	monotonic bool
-	start     time.Time
-
-	reported map[attribute.Distinct]N
-}
-
-func (s *precomputedSum[N]) delta(dest *metricdata.Aggregation) int {
-	t := now()
-	newReported := make(map[attribute.Distinct]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 key, value := range s.values {
-		delta := value.n - s.reported[key]
-
-		dPts[i].Attributes = value.attrs
-		dPts[i].StartTime = s.start
-		dPts[i].Time = t
-		dPts[i].Value = delta
-		collectExemplars(&dPts[i].Exemplars, value.res.Collect)
-
-		newReported[key] = value.n
-		i++
-	}
-	// Unused attribute sets do not report.
-	clear(s.values)
-	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 _, val := range s.values {
-		dPts[i].Attributes = val.attrs
-		dPts[i].StartTime = s.start
-		dPts[i].Time = t
-		dPts[i].Value = val.n
-		collectExemplars(&dPts[i].Exemplars, val.res.Collect)
-
-		i++
-	}
-	// Unused attribute sets do not report.
-	clear(s.values)
-
-	sData.DataPoints = dPts
-	*dest = sData
-
-	return n
-}