1 files changed, 0 insertions, 232 deletions
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go
deleted file mode 100644
index d577ae2c1..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go
+++ /dev/null
@@ -1,232 +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"
-	"slices"
-	"sort"
-	"sync"
-	"time"
-
-	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/metricdata"
-)
-
-type buckets[N int64 | float64] struct {
-	attrs attribute.Set
-	res   FilteredExemplarReservoir[N]
-
-	counts   []uint64
-	count    uint64
-	total    N
-	min, max N
-}
-
-// newBuckets returns buckets with n bins.
-func newBuckets[N int64 | float64](attrs attribute.Set, n int) *buckets[N] {
-	return &buckets[N]{attrs: attrs, counts: make([]uint64, n)}
-}
-
-func (b *buckets[N]) sum(value N) { b.total += value }
-
-func (b *buckets[N]) bin(idx int, value N) {
-	b.counts[idx]++
-	b.count++
-	if value < b.min {
-		b.min = value
-	} else if value > b.max {
-		b.max = value
-	}
-}
-
-// histValues summarizes a set of measurements as an histValues with
-// explicitly defined buckets.
-type histValues[N int64 | float64] struct {
-	noSum  bool
-	bounds []float64
-
-	newRes   func(attribute.Set) FilteredExemplarReservoir[N]
-	limit    limiter[*buckets[N]]
-	values   map[attribute.Distinct]*buckets[N]
-	valuesMu sync.Mutex
-}
-
-func newHistValues[N int64 | float64](bounds []float64, noSum bool, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *histValues[N] {
-	// The responsibility of keeping all buckets correctly associated with the
-	// passed boundaries is ultimately this type's responsibility. Make a copy
-	// here so we can always guarantee this. Or, in the case of failure, have
-	// complete control over the fix.
-	b := slices.Clone(bounds)
-	slices.Sort(b)
-	return &histValues[N]{
-		noSum:  noSum,
-		bounds: b,
-		newRes: r,
-		limit:  newLimiter[*buckets[N]](limit),
-		values: make(map[attribute.Distinct]*buckets[N]),
-	}
-}
-
-// Aggregate records the measurement value, scoped by attr, and aggregates it
-// into a histogram.
-func (s *histValues[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
-	// This search will return an index in the range [0, len(s.bounds)], where
-	// it will return len(s.bounds) if value is greater than the last element
-	// of s.bounds. This aligns with the buckets in that the length of buckets
-	// is len(s.bounds)+1, with the last bucket representing:
-	// (s.bounds[len(s.bounds)-1], +∞).
-	idx := sort.SearchFloat64s(s.bounds, float64(value))
-
-	s.valuesMu.Lock()
-	defer s.valuesMu.Unlock()
-
-	attr := s.limit.Attributes(fltrAttr, s.values)
-	b, ok := s.values[attr.Equivalent()]
-	if !ok {
-		// N+1 buckets. For example:
-		//
-		//   bounds = [0, 5, 10]
-		//
-		// Then,
-		//
-		//   buckets = (-∞, 0], (0, 5.0], (5.0, 10.0], (10.0, +∞)
-		b = newBuckets[N](attr, len(s.bounds)+1)
-		b.res = s.newRes(attr)
-
-		// Ensure min and max are recorded values (not zero), for new buckets.
-		b.min, b.max = value, value
-		s.values[attr.Equivalent()] = b
-	}
-	b.bin(idx, value)
-	if !s.noSum {
-		b.sum(value)
-	}
-	b.res.Offer(ctx, value, droppedAttr)
-}
-
-// newHistogram returns an Aggregator that summarizes a set of measurements as
-// an histogram.
-func newHistogram[N int64 | float64](boundaries []float64, noMinMax, noSum bool, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *histogram[N] {
-	return &histogram[N]{
-		histValues: newHistValues[N](boundaries, noSum, limit, r),
-		noMinMax:   noMinMax,
-		start:      now(),
-	}
-}
-
-// histogram summarizes a set of measurements as an histogram with explicitly
-// defined buckets.
-type histogram[N int64 | float64] struct {
-	*histValues[N]
-
-	noMinMax bool
-	start    time.Time
-}
-
-func (s *histogram[N]) delta(dest *metricdata.Aggregation) int {
-	t := now()
-
-	// If *dest is not a metricdata.Histogram, memory reuse is missed. In that
-	// case, use the zero-value h and hope for better alignment next cycle.
-	h, _ := (*dest).(metricdata.Histogram[N])
-	h.Temporality = metricdata.DeltaTemporality
-
-	s.valuesMu.Lock()
-	defer s.valuesMu.Unlock()
-
-	// Do not allow modification of our copy of bounds.
-	bounds := slices.Clone(s.bounds)
-
-	n := len(s.values)
-	hDPts := reset(h.DataPoints, n, n)
-
-	var i int
-	for _, val := range s.values {
-		hDPts[i].Attributes = val.attrs
-		hDPts[i].StartTime = s.start
-		hDPts[i].Time = t
-		hDPts[i].Count = val.count
-		hDPts[i].Bounds = bounds
-		hDPts[i].BucketCounts = val.counts
-
-		if !s.noSum {
-			hDPts[i].Sum = val.total
-		}
-
-		if !s.noMinMax {
-			hDPts[i].Min = metricdata.NewExtrema(val.min)
-			hDPts[i].Max = metricdata.NewExtrema(val.max)
-		}
-
-		collectExemplars(&hDPts[i].Exemplars, val.res.Collect)
-
-		i++
-	}
-	// Unused attribute sets do not report.
-	clear(s.values)
-	// The delta collection cycle resets.
-	s.start = t
-
-	h.DataPoints = hDPts
-	*dest = h
-
-	return n
-}
-
-func (s *histogram[N]) cumulative(dest *metricdata.Aggregation) int {
-	t := now()
-
-	// If *dest is not a metricdata.Histogram, memory reuse is missed. In that
-	// case, use the zero-value h and hope for better alignment next cycle.
-	h, _ := (*dest).(metricdata.Histogram[N])
-	h.Temporality = metricdata.CumulativeTemporality
-
-	s.valuesMu.Lock()
-	defer s.valuesMu.Unlock()
-
-	// Do not allow modification of our copy of bounds.
-	bounds := slices.Clone(s.bounds)
-
-	n := len(s.values)
-	hDPts := reset(h.DataPoints, n, n)
-
-	var i int
-	for _, val := range s.values {
-		hDPts[i].Attributes = val.attrs
-		hDPts[i].StartTime = s.start
-		hDPts[i].Time = t
-		hDPts[i].Count = val.count
-		hDPts[i].Bounds = bounds
-
-		// The HistogramDataPoint field values returned need to be copies of
-		// the buckets value as we will keep updating them.
-		//
-		// TODO (#3047): Making copies for bounds and counts incurs a large
-		// memory allocation footprint. Alternatives should be explored.
-		hDPts[i].BucketCounts = slices.Clone(val.counts)
-
-		if !s.noSum {
-			hDPts[i].Sum = val.total
-		}
-
-		if !s.noMinMax {
-			hDPts[i].Min = metricdata.NewExtrema(val.min)
-			hDPts[i].Max = metricdata.NewExtrema(val.max)
-		}
-
-		collectExemplars(&hDPts[i].Exemplars, val.res.Collect)
-
-		i++
-		// 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.
-	}
-
-	h.DataPoints = hDPts
-	*dest = h
-
-	return n
-}