[feature] Initial Prometheus metrics implementation (#2334)

* feat: Initial OTEL metrics

* docs: add metrics documentation

* fix: metrics endpoint conditional check

* feat: metrics endpoint basic auth

* fix: make metrics-auth-enabled default false

* fix: go fmt helpers.gen.go

* fix: add metric-related env vars to envparsing.sh

* fix: metrics docs

* fix: metrics related stuff in envparsing.sh

* fix: metrics docs

* chore: metrics docs wording

* fix: metrics stuff in envparsing?

* bump otel versions

---------

Co-authored-by: Tsuribori <user@acertaindebian>
Co-authored-by: Tsuribori <none@example.org>
Co-authored-by: tsmethurst <tobi.smethurst@protonmail.com>

6 files changed, 1104 insertions, 0 deletions

diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/aggregate.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/aggregate.go
new file mode 100644
index 000000000..8dec14237
--- /dev/null
+++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/aggregate.go
@@ -0,0 +1,133 @@
+// 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"
+	"time"
+
+	"go.opentelemetry.io/otel/attribute"
+	"go.opentelemetry.io/otel/sdk/metric/metricdata"
+)
+
+// now is used to return the current local time while allowing tests to
+// override the default time.Now function.
+var now = time.Now
+
+// Measure receives measurements to be aggregated.
+type Measure[N int64 | float64] func(context.Context, N, attribute.Set)
+
+// ComputeAggregation stores the aggregate of measurements into dest and
+// returns the number of aggregate data-points output.
+type ComputeAggregation func(dest *metricdata.Aggregation) int
+
+// Builder builds an aggregate function.
+type Builder[N int64 | float64] struct {
+	// Temporality is the temporality used for the returned aggregate function.
+	//
+	// If this is not provided a default of cumulative will be used (except for
+	// the last-value aggregate function where delta is the only appropriate
+	// temporality).
+	Temporality metricdata.Temporality
+	// Filter is the attribute filter the aggregate function will use on the
+	// input of measurements.
+	Filter attribute.Filter
+}
+
+func (b Builder[N]) filter(f Measure[N]) Measure[N] {
+	if b.Filter != nil {
+		fltr := b.Filter // Copy to make it immutable after assignment.
+		return func(ctx context.Context, n N, a attribute.Set) {
+			fAttr, _ := a.Filter(fltr)
+			f(ctx, n, fAttr)
+		}
+	}
+	return f
+}
+
+// LastValue returns a last-value aggregate function input and output.
+//
+// The Builder.Temporality is ignored and delta is use always.
+func (b Builder[N]) LastValue() (Measure[N], ComputeAggregation) {
+	// Delta temporality is the only temporality that makes semantic sense for
+	// a last-value aggregate.
+	lv := newLastValue[N]()
+
+	return b.filter(lv.measure), func(dest *metricdata.Aggregation) int {
+		// Ignore if dest is not a metricdata.Gauge. The chance for memory
+		// reuse of the DataPoints is missed (better luck next time).
+		gData, _ := (*dest).(metricdata.Gauge[N])
+		lv.computeAggregation(&gData.DataPoints)
+		*dest = gData
+
+		return len(gData.DataPoints)
+	}
+}
+
+// PrecomputedSum returns a sum aggregate function input and output. The
+// arguments passed to the input are expected to be the precomputed sum values.
+func (b Builder[N]) PrecomputedSum(monotonic bool) (Measure[N], ComputeAggregation) {
+	s := newPrecomputedSum[N](monotonic)
+	switch b.Temporality {
+	case metricdata.DeltaTemporality:
+		return b.filter(s.measure), s.delta
+	default:
+		return b.filter(s.measure), s.cumulative
+	}
+}
+
+// Sum returns a sum aggregate function input and output.
+func (b Builder[N]) Sum(monotonic bool) (Measure[N], ComputeAggregation) {
+	s := newSum[N](monotonic)
+	switch b.Temporality {
+	case metricdata.DeltaTemporality:
+		return b.filter(s.measure), s.delta
+	default:
+		return b.filter(s.measure), s.cumulative
+	}
+}
+
+// ExplicitBucketHistogram returns a histogram aggregate function input and
+// output.
+func (b Builder[N]) ExplicitBucketHistogram(boundaries []float64, noMinMax, noSum bool) (Measure[N], ComputeAggregation) {
+	h := newHistogram[N](boundaries, noMinMax, noSum)
+	switch b.Temporality {
+	case metricdata.DeltaTemporality:
+		return b.filter(h.measure), h.delta
+	default:
+		return b.filter(h.measure), h.cumulative
+	}
+}
+
+// ExponentialBucketHistogram returns a histogram aggregate function input and
+// output.
+func (b Builder[N]) ExponentialBucketHistogram(maxSize, maxScale int32, noMinMax, noSum bool) (Measure[N], ComputeAggregation) {
+	h := newExponentialHistogram[N](maxSize, maxScale, noMinMax, noSum)
+	switch b.Temporality {
+	case metricdata.DeltaTemporality:
+		return b.filter(h.measure), h.delta
+	default:
+		return b.filter(h.measure), h.cumulative
+	}
+}
+
+// reset ensures s has capacity and sets it length. If the capacity of s too
+// small, a new slice is returned with the specified capacity and length.
+func reset[T any](s []T, length, capacity int) []T {
+	if cap(s) < capacity {
+		return make([]T, length, capacity)
+	}
+	return s[:length]
+}
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/doc.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/doc.go
new file mode 100644
index 000000000..e83a2693f
--- /dev/null
+++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/doc.go
@@ -0,0 +1,18 @@
+// 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 provides aggregate types used compute aggregations and
+// cycle the state of metric measurements made by the SDK. These types and
+// functionality are meant only for internal SDK use.
+package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go
new file mode 100644
index 000000000..98b7dc1e0
--- /dev/null
+++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go
@@ -0,0 +1,432 @@
+// 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"
+	"errors"
+	"math"
+	"sync"
+	"time"
+
+	"go.opentelemetry.io/otel"
+	"go.opentelemetry.io/otel/attribute"
+	"go.opentelemetry.io/otel/sdk/metric/metricdata"
+)
+
+const (
+	expoMaxScale = 20
+	expoMinScale = -10
+
+	smallestNonZeroNormalFloat64 = 0x1p-1022
+
+	// These redefine the Math constants with a type, so the compiler won't coerce
+	// them into an int on 32 bit platforms.
+	maxInt64 int64 = math.MaxInt64
+	minInt64 int64 = math.MinInt64
+)
+
+// expoHistogramDataPoint is a single data point in an exponential histogram.
+type expoHistogramDataPoint[N int64 | float64] struct {
+	count uint64
+	min   N
+	max   N
+	sum   N
+
+	maxSize  int
+	noMinMax bool
+	noSum    bool
+
+	scale int
+
+	posBuckets expoBuckets
+	negBuckets expoBuckets
+	zeroCount  uint64
+}
+
+func newExpoHistogramDataPoint[N int64 | float64](maxSize, maxScale int, noMinMax, noSum bool) *expoHistogramDataPoint[N] {
+	f := math.MaxFloat64
+	max := N(f) // if N is int64, max will overflow to -9223372036854775808
+	min := N(-f)
+	if N(maxInt64) > N(f) {
+		max = N(maxInt64)
+		min = N(minInt64)
+	}
+	return &expoHistogramDataPoint[N]{
+		min:      max,
+		max:      min,
+		maxSize:  maxSize,
+		noMinMax: noMinMax,
+		noSum:    noSum,
+		scale:    maxScale,
+	}
+}
+
+// record adds a new measurement to the histogram. It will rescale the buckets if needed.
+func (p *expoHistogramDataPoint[N]) record(v N) {
+	p.count++
+
+	if !p.noMinMax {
+		if v < p.min {
+			p.min = v
+		}
+		if v > p.max {
+			p.max = v
+		}
+	}
+	if !p.noSum {
+		p.sum += v
+	}
+
+	absV := math.Abs(float64(v))
+
+	if float64(absV) == 0.0 {
+		p.zeroCount++
+		return
+	}
+
+	bin := p.getBin(absV)
+
+	bucket := &p.posBuckets
+	if v < 0 {
+		bucket = &p.negBuckets
+	}
+
+	// If the new bin would make the counts larger than maxScale, we need to
+	// downscale current measurements.
+	if scaleDelta := p.scaleChange(bin, bucket.startBin, len(bucket.counts)); scaleDelta > 0 {
+		if p.scale-scaleDelta < expoMinScale {
+			// With a scale of -10 there is only two buckets for the whole range of float64 values.
+			// This can only happen if there is a max size of 1.
+			otel.Handle(errors.New("exponential histogram scale underflow"))
+			return
+		}
+		// Downscale
+		p.scale -= scaleDelta
+		p.posBuckets.downscale(scaleDelta)
+		p.negBuckets.downscale(scaleDelta)
+
+		bin = p.getBin(absV)
+	}
+
+	bucket.record(bin)
+}
+
+// getBin returns the bin v should be recorded into.
+func (p *expoHistogramDataPoint[N]) getBin(v float64) int {
+	frac, exp := math.Frexp(v)
+	if p.scale <= 0 {
+		// Because of the choice of fraction is always 1 power of two higher than we want.
+		correction := 1
+		if frac == .5 {
+			// If v is an exact power of two the frac will be .5 and the exp
+			// will be one higher than we want.
+			correction = 2
+		}
+		return (exp - correction) >> (-p.scale)
+	}
+	return exp<<p.scale + int(math.Log(frac)*scaleFactors[p.scale]) - 1
+}
+
+// scaleFactors are constants used in calculating the logarithm index. They are
+// equivalent to 2^index/log(2).
+var scaleFactors = [21]float64{
+	math.Ldexp(math.Log2E, 0),
+	math.Ldexp(math.Log2E, 1),
+	math.Ldexp(math.Log2E, 2),
+	math.Ldexp(math.Log2E, 3),
+	math.Ldexp(math.Log2E, 4),
+	math.Ldexp(math.Log2E, 5),
+	math.Ldexp(math.Log2E, 6),
+	math.Ldexp(math.Log2E, 7),
+	math.Ldexp(math.Log2E, 8),
+	math.Ldexp(math.Log2E, 9),
+	math.Ldexp(math.Log2E, 10),
+	math.Ldexp(math.Log2E, 11),
+	math.Ldexp(math.Log2E, 12),
+	math.Ldexp(math.Log2E, 13),
+	math.Ldexp(math.Log2E, 14),
+	math.Ldexp(math.Log2E, 15),
+	math.Ldexp(math.Log2E, 16),
+	math.Ldexp(math.Log2E, 17),
+	math.Ldexp(math.Log2E, 18),
+	math.Ldexp(math.Log2E, 19),
+	math.Ldexp(math.Log2E, 20),
+}
+
+// scaleChange returns the magnitude of the scale change needed to fit bin in
+// the bucket. If no scale change is needed 0 is returned.
+func (p *expoHistogramDataPoint[N]) scaleChange(bin, startBin, length int) int {
+	if length == 0 {
+		// No need to rescale if there are no buckets.
+		return 0
+	}
+
+	low := startBin
+	high := bin
+	if startBin >= bin {
+		low = bin
+		high = startBin + length - 1
+	}
+
+	count := 0
+	for high-low >= p.maxSize {
+		low = low >> 1
+		high = high >> 1
+		count++
+		if count > expoMaxScale-expoMinScale {
+			return count
+		}
+	}
+	return count
+}
+
+// expoBuckets is a set of buckets in an exponential histogram.
+type expoBuckets struct {
+	startBin int
+	counts   []uint64
+}
+
+// record increments the count for the given bin, and expands the buckets if needed.
+// Size changes must be done before calling this function.
+func (b *expoBuckets) record(bin int) {
+	if len(b.counts) == 0 {
+		b.counts = []uint64{1}
+		b.startBin = bin
+		return
+	}
+
+	endBin := b.startBin + len(b.counts) - 1
+
+	// if the new bin is inside the current range
+	if bin >= b.startBin && bin <= endBin {
+		b.counts[bin-b.startBin]++
+		return
+	}
+	// if the new bin is before the current start add spaces to the counts
+	if bin < b.startBin {
+		origLen := len(b.counts)
+		newLength := endBin - bin + 1
+		shift := b.startBin - bin
+
+		if newLength > cap(b.counts) {
+			b.counts = append(b.counts, make([]uint64, newLength-len(b.counts))...)
+		}
+
+		copy(b.counts[shift:origLen+shift], b.counts[:])
+		b.counts = b.counts[:newLength]
+		for i := 1; i < shift; i++ {
+			b.counts[i] = 0
+		}
+		b.startBin = bin
+		b.counts[0] = 1
+		return
+	}
+	// if the new is after the end add spaces to the end
+	if bin > endBin {
+		if bin-b.startBin < cap(b.counts) {
+			b.counts = b.counts[:bin-b.startBin+1]
+			for i := endBin + 1 - b.startBin; i < len(b.counts); i++ {
+				b.counts[i] = 0
+			}
+			b.counts[bin-b.startBin] = 1
+			return
+		}
+
+		end := make([]uint64, bin-b.startBin-len(b.counts)+1)
+		b.counts = append(b.counts, end...)
+		b.counts[bin-b.startBin] = 1
+	}
+}
+
+// downscale shrinks a bucket by a factor of 2*s. It will sum counts into the
+// correct lower resolution bucket.
+func (b *expoBuckets) downscale(delta int) {
+	// Example
+	// delta = 2
+	// Original offset: -6
+	// Counts: [ 3,  1,  2,  3,  4,  5, 6, 7, 8, 9, 10]
+	// bins:    -6  -5, -4, -3, -2, -1, 0, 1, 2, 3, 4
+	// new bins:-2, -2, -1, -1, -1, -1, 0, 0, 0, 0, 1
+	// new Offset: -2
+	// new Counts: [4, 14, 30, 10]
+
+	if len(b.counts) <= 1 || delta < 1 {
+		b.startBin = b.startBin >> delta
+		return
+	}
+
+	steps := 1 << delta
+	offset := b.startBin % steps
+	offset = (offset + steps) % steps // to make offset positive
+	for i := 1; i < len(b.counts); i++ {
+		idx := i + offset
+		if idx%steps == 0 {
+			b.counts[idx/steps] = b.counts[i]
+			continue
+		}
+		b.counts[idx/steps] += b.counts[i]
+	}
+
+	lastIdx := (len(b.counts) - 1 + offset) / steps
+	b.counts = b.counts[:lastIdx+1]
+	b.startBin = b.startBin >> delta
+}
+
+// newExponentialHistogram returns an Aggregator that summarizes a set of
+// measurements as an exponential histogram. Each histogram is scoped by attributes
+// and the aggregation cycle the measurements were made in.
+func newExponentialHistogram[N int64 | float64](maxSize, maxScale int32, noMinMax, noSum bool) *expoHistogram[N] {
+	return &expoHistogram[N]{
+		noSum:    noSum,
+		noMinMax: noMinMax,
+		maxSize:  int(maxSize),
+		maxScale: int(maxScale),
+
+		values: make(map[attribute.Set]*expoHistogramDataPoint[N]),
+
+		start: now(),
+	}
+}
+
+// expoHistogram summarizes a set of measurements as an histogram with exponentially
+// defined buckets.
+type expoHistogram[N int64 | float64] struct {
+	noSum    bool
+	noMinMax bool
+	maxSize  int
+	maxScale int
+
+	values   map[attribute.Set]*expoHistogramDataPoint[N]
+	valuesMu sync.Mutex
+
+	start time.Time
+}
+
+func (e *expoHistogram[N]) measure(_ context.Context, value N, attr attribute.Set) {
+	// Ignore NaN and infinity.
+	if math.IsInf(float64(value), 0) || math.IsNaN(float64(value)) {
+		return
+	}
+
+	e.valuesMu.Lock()
+	defer e.valuesMu.Unlock()
+
+	v, ok := e.values[attr]
+	if !ok {
+		v = newExpoHistogramDataPoint[N](e.maxSize, e.maxScale, e.noMinMax, e.noSum)
+		e.values[attr] = v
+	}
+	v.record(value)
+}
+
+func (e *expoHistogram[N]) delta(dest *metricdata.Aggregation) int {
+	t := now()
+
+	// If *dest is not a metricdata.ExponentialHistogram, memory reuse is missed.
+	// In that case, use the zero-value h and hope for better alignment next cycle.
+	h, _ := (*dest).(metricdata.ExponentialHistogram[N])
+	h.Temporality = metricdata.DeltaTemporality
+
+	e.valuesMu.Lock()
+	defer e.valuesMu.Unlock()
+
+	n := len(e.values)
+	hDPts := reset(h.DataPoints, n, n)
+
+	var i int
+	for a, b := range e.values {
+		hDPts[i].Attributes = a
+		hDPts[i].StartTime = e.start
+		hDPts[i].Time = t
+		hDPts[i].Count = b.count
+		hDPts[i].Scale = int32(b.scale)
+		hDPts[i].ZeroCount = b.zeroCount
+		hDPts[i].ZeroThreshold = 0.0
+
+		hDPts[i].PositiveBucket.Offset = int32(b.posBuckets.startBin)
+		hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(b.posBuckets.counts), len(b.posBuckets.counts))
+		copy(hDPts[i].PositiveBucket.Counts, b.posBuckets.counts)
+
+		hDPts[i].NegativeBucket.Offset = int32(b.negBuckets.startBin)
+		hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(b.negBuckets.counts), len(b.negBuckets.counts))
+
+		if !e.noSum {
+			hDPts[i].Sum = b.sum
+		}
+		if !e.noMinMax {
+			hDPts[i].Min = metricdata.NewExtrema(b.min)
+			hDPts[i].Max = metricdata.NewExtrema(b.max)
+		}
+
+		delete(e.values, a)
+		i++
+	}
+	e.start = t
+	h.DataPoints = hDPts
+	*dest = h
+	return n
+}
+
+func (e *expoHistogram[N]) cumulative(dest *metricdata.Aggregation) int {
+	t := now()
+
+	// If *dest is not a metricdata.ExponentialHistogram, memory reuse is missed.
+	// In that case, use the zero-value h and hope for better alignment next cycle.
+	h, _ := (*dest).(metricdata.ExponentialHistogram[N])
+	h.Temporality = metricdata.CumulativeTemporality
+
+	e.valuesMu.Lock()
+	defer e.valuesMu.Unlock()
+
+	n := len(e.values)
+	hDPts := reset(h.DataPoints, n, n)
+
+	var i int
+	for a, b := range e.values {
+		hDPts[i].Attributes = a
+		hDPts[i].StartTime = e.start
+		hDPts[i].Time = t
+		hDPts[i].Count = b.count
+		hDPts[i].Scale = int32(b.scale)
+		hDPts[i].ZeroCount = b.zeroCount
+		hDPts[i].ZeroThreshold = 0.0
+
+		hDPts[i].PositiveBucket.Offset = int32(b.posBuckets.startBin)
+		hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(b.posBuckets.counts), len(b.posBuckets.counts))
+		copy(hDPts[i].PositiveBucket.Counts, b.posBuckets.counts)
+
+		hDPts[i].NegativeBucket.Offset = int32(b.negBuckets.startBin)
+		hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(b.negBuckets.counts), len(b.negBuckets.counts))
+
+		if !e.noSum {
+			hDPts[i].Sum = b.sum
+		}
+		if !e.noMinMax {
+			hDPts[i].Min = metricdata.NewExtrema(b.min)
+			hDPts[i].Max = metricdata.NewExtrema(b.max)
+		}
+
+		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
+}
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
new file mode 100644
index 000000000..62ec51e1f
--- /dev/null
+++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go
@@ -0,0 +1,231 @@
+// 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"
+	"sort"
+	"sync"
+	"time"
+
+	"go.opentelemetry.io/otel/attribute"
+	"go.opentelemetry.io/otel/sdk/metric/metricdata"
+)
+
+type buckets[N int64 | float64] struct {
+	counts   []uint64
+	count    uint64
+	total    N
+	min, max N
+}
+
+// newBuckets returns buckets with n bins.
+func newBuckets[N int64 | float64](n int) *buckets[N] {
+	return &buckets[N]{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
+
+	values   map[attribute.Set]*buckets[N]
+	valuesMu sync.Mutex
+}
+
+func newHistValues[N int64 | float64](bounds []float64, noSum bool) *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 := make([]float64, len(bounds))
+	copy(b, bounds)
+	sort.Float64s(b)
+	return &histValues[N]{
+		noSum:  noSum,
+		bounds: b,
+		values: make(map[attribute.Set]*buckets[N]),
+	}
+}
+
+// Aggregate records the measurement value, scoped by attr, and aggregates it
+// into a histogram.
+func (s *histValues[N]) measure(_ context.Context, value N, attr attribute.Set) {
+	// 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()
+
+	b, ok := s.values[attr]
+	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](len(s.bounds) + 1)
+		// Ensure min and max are recorded values (not zero), for new buckets.
+		b.min, b.max = value, value
+		s.values[attr] = b
+	}
+	b.bin(idx, value)
+	if !s.noSum {
+		b.sum(value)
+	}
+}
+
+// newHistogram returns an Aggregator that summarizes a set of measurements as
+// an histogram.
+func newHistogram[N int64 | float64](boundaries []float64, noMinMax, noSum bool) *histogram[N] {
+	return &histogram[N]{
+		histValues: newHistValues[N](boundaries, noSum),
+		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 := make([]float64, len(s.bounds))
+	copy(bounds, s.bounds)
+
+	n := len(s.values)
+	hDPts := reset(h.DataPoints, n, n)
+
+	var i int
+	for a, b := range s.values {
+		hDPts[i].Attributes = a
+		hDPts[i].StartTime = s.start
+		hDPts[i].Time = t
+		hDPts[i].Count = b.count
+		hDPts[i].Bounds = bounds
+		hDPts[i].BucketCounts = b.counts
+
+		if !s.noSum {
+			hDPts[i].Sum = b.total
+		}
+
+		if !s.noMinMax {
+			hDPts[i].Min = metricdata.NewExtrema(b.min)
+			hDPts[i].Max = metricdata.NewExtrema(b.max)
+		}
+
+		// Unused attribute sets do not report.
+		delete(s.values, a)
+		i++
+	}
+	// 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 := make([]float64, len(s.bounds))
+	copy(bounds, s.bounds)
+
+	n := len(s.values)
+	hDPts := reset(h.DataPoints, n, n)
+
+	var i int
+	for a, b := range s.values {
+		// 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.
+		counts := make([]uint64, len(b.counts))
+		copy(counts, b.counts)
+
+		hDPts[i].Attributes = a
+		hDPts[i].StartTime = s.start
+		hDPts[i].Time = t
+		hDPts[i].Count = b.count
+		hDPts[i].Bounds = bounds
+		hDPts[i].BucketCounts = counts
+
+		if !s.noSum {
+			hDPts[i].Sum = b.total
+		}
+
+		if !s.noMinMax {
+			hDPts[i].Min = metricdata.NewExtrema(b.min)
+			hDPts[i].Max = metricdata.NewExtrema(b.max)
+		}
+		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
+}
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go
new file mode 100644
index 000000000..6af2d6061
--- /dev/null
+++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go
@@ -0,0 +1,68 @@
+// 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"
+)
+
+// datapoint is timestamped measurement data.
+type datapoint[N int64 | float64] struct {
+	timestamp time.Time
+	value     N
+}
+
+func newLastValue[N int64 | float64]() *lastValue[N] {
+	return &lastValue[N]{values: make(map[attribute.Set]datapoint[N])}
+}
+
+// lastValue summarizes a set of measurements as the last one made.
+type lastValue[N int64 | float64] struct {
+	sync.Mutex
+
+	values map[attribute.Set]datapoint[N]
+}
+
+func (s *lastValue[N]) measure(ctx context.Context, value N, attr attribute.Set) {
+	d := datapoint[N]{timestamp: now(), value: value}
+	s.Lock()
+	s.values[attr] = d
+	s.Unlock()
+}
+
+func (s *lastValue[N]) computeAggregation(dest *[]metricdata.DataPoint[N]) {
+	s.Lock()
+	defer s.Unlock()
+
+	n := len(s.values)
+	*dest = reset(*dest, n, n)
+
+	var i int
+	for a, v := range s.values {
+		(*dest)[i].Attributes = a
+		// The event time is the only meaningful timestamp, StartTime is
+		// ignored.
+		(*dest)[i].Time = v.timestamp
+		(*dest)[i].Value = v.value
+		// Do not report stale values.
+		delete(s.values, a)
+		i++
+	}
+}
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
new file mode 100644
index 000000000..1e52ff0d1
--- /dev/null
+++ b/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
+}