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vendor: update buildkit to v0.18.0-rc1

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Signed-off-by: Tonis Tiigi <tonistiigi@gmail.com>
This commit is contained in:
Tonis Tiigi
2024-11-21 12:57:27 -08:00
parent 1a039115bc
commit 13a426fca6
448 changed files with 35377 additions and 5707 deletions
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87
vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/aggregate.go generated vendored
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@ -1,16 +1,5 @@
// 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.
// SPDX-License-Identifier: Apache-2.0
package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
@ -19,6 +8,7 @@ import (
"time"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
@ -44,42 +34,73 @@ type Builder[N int64 | float64] struct {
// Filter is the attribute filter the aggregate function will use on the
// input of measurements.
Filter attribute.Filter
// ReservoirFunc is the factory function used by aggregate functions to
// create new exemplar reservoirs for a new seen attribute set.
//
// If this is not provided a default factory function that returns an
// exemplar.Drop reservoir will be used.
ReservoirFunc func() exemplar.FilteredReservoir[N]
// AggregationLimit is the cardinality limit of measurement attributes. Any
// measurement for new attributes once the limit has been reached will be
// aggregated into a single aggregate for the "otel.metric.overflow"
// attribute.
//
// If AggregationLimit is less than or equal to zero there will not be an
// aggregation limit imposed (i.e. unlimited attribute sets).
AggregationLimit int
}
func (b Builder[N]) filter(f Measure[N]) Measure[N] {
func (b Builder[N]) resFunc() func() exemplar.FilteredReservoir[N] {
if b.ReservoirFunc != nil {
return b.ReservoirFunc
}
return exemplar.Drop
}
type fltrMeasure[N int64 | float64] func(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue)
func (b Builder[N]) filter(f fltrMeasure[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)
fAttr, dropped := a.Filter(fltr)
f(ctx, n, fAttr, dropped)
}
}
return f
return func(ctx context.Context, n N, a attribute.Set) {
f(ctx, n, a, nil)
}
}
// 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]()
lv := newLastValue[N](b.AggregationLimit, b.resFunc())
switch b.Temporality {
case metricdata.DeltaTemporality:
return b.filter(lv.measure), lv.delta
default:
return b.filter(lv.measure), lv.cumulative
}
}
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)
// PrecomputedLastValue returns a last-value aggregate function input and
// output. The aggregation returned from the returned ComputeAggregation
// function will always only return values from the previous collection cycle.
func (b Builder[N]) PrecomputedLastValue() (Measure[N], ComputeAggregation) {
lv := newPrecomputedLastValue[N](b.AggregationLimit, b.resFunc())
switch b.Temporality {
case metricdata.DeltaTemporality:
return b.filter(lv.measure), lv.delta
default:
return b.filter(lv.measure), lv.cumulative
}
}
// 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)
s := newPrecomputedSum[N](monotonic, b.AggregationLimit, b.resFunc())
switch b.Temporality {
case metricdata.DeltaTemporality:
return b.filter(s.measure), s.delta
@ -90,7 +111,7 @@ func (b Builder[N]) PrecomputedSum(monotonic bool) (Measure[N], ComputeAggregati
// Sum returns a sum aggregate function input and output.
func (b Builder[N]) Sum(monotonic bool) (Measure[N], ComputeAggregation) {
s := newSum[N](monotonic)
s := newSum[N](monotonic, b.AggregationLimit, b.resFunc())
switch b.Temporality {
case metricdata.DeltaTemporality:
return b.filter(s.measure), s.delta
@ -102,7 +123,7 @@ func (b Builder[N]) Sum(monotonic bool) (Measure[N], ComputeAggregation) {
// 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)
h := newHistogram[N](boundaries, noMinMax, noSum, b.AggregationLimit, b.resFunc())
switch b.Temporality {
case metricdata.DeltaTemporality:
return b.filter(h.measure), h.delta
@ -114,7 +135,7 @@ func (b Builder[N]) ExplicitBucketHistogram(boundaries []float64, noMinMax, noSu
// 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)
h := newExponentialHistogram[N](maxSize, maxScale, noMinMax, noSum, b.AggregationLimit, b.resFunc())
switch b.Temporality {
case metricdata.DeltaTemporality:
return b.filter(h.measure), h.delta

13
vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/doc.go generated vendored
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@ -1,16 +1,5 @@
// 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.
// SPDX-License-Identifier: Apache-2.0
// Package aggregate provides aggregate types used compute aggregations and
// cycle the state of metric measurements made by the SDK. These types and

42
vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exemplar.go generated vendored Normal file
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@ -0,0 +1,42 @@
// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
import (
"sync"
"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
var exemplarPool = sync.Pool{
New: func() any { return new([]exemplar.Exemplar) },
}
func collectExemplars[N int64 | float64](out *[]metricdata.Exemplar[N], f func(*[]exemplar.Exemplar)) {
dest := exemplarPool.Get().(*[]exemplar.Exemplar)
defer func() {
*dest = (*dest)[:0]
exemplarPool.Put(dest)
}()
*dest = reset(*dest, len(*out), cap(*out))
f(dest)
*out = reset(*out, len(*dest), cap(*dest))
for i, e := range *dest {
(*out)[i].FilteredAttributes = e.FilteredAttributes
(*out)[i].Time = e.Time
(*out)[i].SpanID = e.SpanID
(*out)[i].TraceID = e.TraceID
switch e.Value.Type() {
case exemplar.Int64ValueType:
(*out)[i].Value = N(e.Value.Int64())
case exemplar.Float64ValueType:
(*out)[i].Value = N(e.Value.Float64())
}
}
}

104
vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go generated vendored
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@ -1,16 +1,5 @@
// 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.
// SPDX-License-Identifier: Apache-2.0
package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
@ -23,6 +12,7 @@ import (
"go.opentelemetry.io/otel"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
@ -40,6 +30,9 @@ const (
// expoHistogramDataPoint is a single data point in an exponential histogram.
type expoHistogramDataPoint[N int64 | float64] struct {
attrs attribute.Set
res exemplar.FilteredReservoir[N]
count uint64
min N
max N
@ -56,7 +49,7 @@ type expoHistogramDataPoint[N int64 | float64] struct {
zeroCount uint64
}
func newExpoHistogramDataPoint[N int64 | float64](maxSize, maxScale int, noMinMax, noSum bool) *expoHistogramDataPoint[N] {
func newExpoHistogramDataPoint[N int64 | float64](attrs attribute.Set, 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)
@ -65,6 +58,7 @@ func newExpoHistogramDataPoint[N int64 | float64](maxSize, maxScale int, noMinMa
min = N(minInt64)
}
return &expoHistogramDataPoint[N]{
attrs: attrs,
min: max,
max: min,
maxSize: maxSize,
@ -288,14 +282,16 @@ func (b *expoBuckets) downscale(delta int) {
// 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] {
func newExponentialHistogram[N int64 | float64](maxSize, maxScale int32, noMinMax, noSum bool, limit int, r func() exemplar.FilteredReservoir[N]) *expoHistogram[N] {
return &expoHistogram[N]{
noSum: noSum,
noMinMax: noMinMax,
maxSize: int(maxSize),
maxScale: int(maxScale),
values: make(map[attribute.Set]*expoHistogramDataPoint[N]),
newRes: r,
limit: newLimiter[*expoHistogramDataPoint[N]](limit),
values: make(map[attribute.Distinct]*expoHistogramDataPoint[N]),
start: now(),
}
@ -309,13 +305,15 @@ type expoHistogram[N int64 | float64] struct {
maxSize int
maxScale int
values map[attribute.Set]*expoHistogramDataPoint[N]
newRes func() exemplar.FilteredReservoir[N]
limit limiter[*expoHistogramDataPoint[N]]
values map[attribute.Distinct]*expoHistogramDataPoint[N]
valuesMu sync.Mutex
start time.Time
}
func (e *expoHistogram[N]) measure(_ context.Context, value N, attr attribute.Set) {
func (e *expoHistogram[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
// Ignore NaN and infinity.
if math.IsInf(float64(value), 0) || math.IsNaN(float64(value)) {
return
@ -324,12 +322,16 @@ func (e *expoHistogram[N]) measure(_ context.Context, value N, attr attribute.Se
e.valuesMu.Lock()
defer e.valuesMu.Unlock()
v, ok := e.values[attr]
attr := e.limit.Attributes(fltrAttr, e.values)
v, ok := e.values[attr.Equivalent()]
if !ok {
v = newExpoHistogramDataPoint[N](e.maxSize, e.maxScale, e.noMinMax, e.noSum)
e.values[attr] = v
v = newExpoHistogramDataPoint[N](attr, e.maxSize, e.maxScale, e.noMinMax, e.noSum)
v.res = e.newRes()
e.values[attr.Equivalent()] = v
}
v.record(value)
v.res.Offer(ctx, value, droppedAttr)
}
func (e *expoHistogram[N]) delta(dest *metricdata.Aggregation) int {
@ -347,33 +349,38 @@ func (e *expoHistogram[N]) delta(dest *metricdata.Aggregation) int {
hDPts := reset(h.DataPoints, n, n)
var i int
for a, b := range e.values {
hDPts[i].Attributes = a
for _, val := range e.values {
hDPts[i].Attributes = val.attrs
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].Count = val.count
hDPts[i].Scale = int32(val.scale)
hDPts[i].ZeroCount = val.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].PositiveBucket.Offset = int32(val.posBuckets.startBin)
hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(val.posBuckets.counts), len(val.posBuckets.counts))
copy(hDPts[i].PositiveBucket.Counts, val.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))
hDPts[i].NegativeBucket.Offset = int32(val.negBuckets.startBin)
hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(val.negBuckets.counts), len(val.negBuckets.counts))
copy(hDPts[i].NegativeBucket.Counts, val.negBuckets.counts)
if !e.noSum {
hDPts[i].Sum = b.sum
hDPts[i].Sum = val.sum
}
if !e.noMinMax {
hDPts[i].Min = metricdata.NewExtrema(b.min)
hDPts[i].Max = metricdata.NewExtrema(b.max)
hDPts[i].Min = metricdata.NewExtrema(val.min)
hDPts[i].Max = metricdata.NewExtrema(val.max)
}
delete(e.values, a)
collectExemplars(&hDPts[i].Exemplars, val.res.Collect)
i++
}
// Unused attribute sets do not report.
clear(e.values)
e.start = t
h.DataPoints = hDPts
*dest = h
@ -395,30 +402,33 @@ func (e *expoHistogram[N]) cumulative(dest *metricdata.Aggregation) int {
hDPts := reset(h.DataPoints, n, n)
var i int
for a, b := range e.values {
hDPts[i].Attributes = a
for _, val := range e.values {
hDPts[i].Attributes = val.attrs
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].Count = val.count
hDPts[i].Scale = int32(val.scale)
hDPts[i].ZeroCount = val.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].PositiveBucket.Offset = int32(val.posBuckets.startBin)
hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(val.posBuckets.counts), len(val.posBuckets.counts))
copy(hDPts[i].PositiveBucket.Counts, val.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))
hDPts[i].NegativeBucket.Offset = int32(val.negBuckets.startBin)
hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(val.negBuckets.counts), len(val.negBuckets.counts))
copy(hDPts[i].NegativeBucket.Counts, val.negBuckets.counts)
if !e.noSum {
hDPts[i].Sum = b.sum
hDPts[i].Sum = val.sum
}
if !e.noMinMax {
hDPts[i].Min = metricdata.NewExtrema(b.min)
hDPts[i].Max = metricdata.NewExtrema(b.max)
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

106
vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go generated vendored
View File

@ -1,30 +1,24 @@
// 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.
// 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/internal/exemplar"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
type buckets[N int64 | float64] struct {
attrs attribute.Set
res exemplar.FilteredReservoir[N]
counts []uint64
count uint64
total N
@ -32,8 +26,8 @@ type buckets[N int64 | float64] struct {
}
// newBuckets returns buckets with n bins.
func newBuckets[N int64 | float64](n int) *buckets[N] {
return &buckets[N]{counts: make([]uint64, n)}
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 }
@ -54,28 +48,31 @@ type histValues[N int64 | float64] struct {
noSum bool
bounds []float64
values map[attribute.Set]*buckets[N]
newRes func() exemplar.FilteredReservoir[N]
limit limiter[*buckets[N]]
values map[attribute.Distinct]*buckets[N]
valuesMu sync.Mutex
}
func newHistValues[N int64 | float64](bounds []float64, noSum bool) *histValues[N] {
func newHistValues[N int64 | float64](bounds []float64, noSum bool, limit int, r func() exemplar.FilteredReservoir[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 := make([]float64, len(bounds))
copy(b, bounds)
sort.Float64s(b)
b := slices.Clone(bounds)
slices.Sort(b)
return &histValues[N]{
noSum: noSum,
bounds: b,
values: make(map[attribute.Set]*buckets[N]),
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(_ context.Context, value N, attr attribute.Set) {
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
@ -86,7 +83,8 @@ func (s *histValues[N]) measure(_ context.Context, value N, attr attribute.Set)
s.valuesMu.Lock()
defer s.valuesMu.Unlock()
b, ok := s.values[attr]
attr := s.limit.Attributes(fltrAttr, s.values)
b, ok := s.values[attr.Equivalent()]
if !ok {
// N+1 buckets. For example:
//
@ -95,22 +93,25 @@ func (s *histValues[N]) measure(_ context.Context, value N, attr attribute.Set)
// Then,
//
// buckets = (-∞, 0], (0, 5.0], (5.0, 10.0], (10.0, +∞)
b = newBuckets[N](len(s.bounds) + 1)
b = newBuckets[N](attr, len(s.bounds)+1)
b.res = s.newRes()
// Ensure min and max are recorded values (not zero), for new buckets.
b.min, b.max = value, value
s.values[attr] = b
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) *histogram[N] {
func newHistogram[N int64 | float64](boundaries []float64, noMinMax, noSum bool, limit int, r func() exemplar.FilteredReservoir[N]) *histogram[N] {
return &histogram[N]{
histValues: newHistValues[N](boundaries, noSum),
histValues: newHistValues[N](boundaries, noSum, limit, r),
noMinMax: noMinMax,
start: now(),
}
@ -137,34 +138,35 @@ func (s *histogram[N]) delta(dest *metricdata.Aggregation) int {
defer s.valuesMu.Unlock()
// Do not allow modification of our copy of bounds.
bounds := make([]float64, len(s.bounds))
copy(bounds, s.bounds)
bounds := slices.Clone(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
for _, val := range s.values {
hDPts[i].Attributes = val.attrs
hDPts[i].StartTime = s.start
hDPts[i].Time = t
hDPts[i].Count = b.count
hDPts[i].Count = val.count
hDPts[i].Bounds = bounds
hDPts[i].BucketCounts = b.counts
hDPts[i].BucketCounts = val.counts
if !s.noSum {
hDPts[i].Sum = b.total
hDPts[i].Sum = val.total
}
if !s.noMinMax {
hDPts[i].Min = metricdata.NewExtrema(b.min)
hDPts[i].Max = metricdata.NewExtrema(b.max)
hDPts[i].Min = metricdata.NewExtrema(val.min)
hDPts[i].Max = metricdata.NewExtrema(val.max)
}
// Unused attribute sets do not report.
delete(s.values, a)
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
@ -186,37 +188,37 @@ func (s *histogram[N]) cumulative(dest *metricdata.Aggregation) int {
defer s.valuesMu.Unlock()
// Do not allow modification of our copy of bounds.
bounds := make([]float64, len(s.bounds))
copy(bounds, s.bounds)
bounds := slices.Clone(s.bounds)
n := len(s.values)
hDPts := reset(h.DataPoints, n, n)
var i int
for a, b := range s.values {
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.
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
hDPts[i].BucketCounts = slices.Clone(val.counts)
if !s.noSum {
hDPts[i].Sum = b.total
hDPts[i].Sum = val.total
}
if !s.noMinMax {
hDPts[i].Min = metricdata.NewExtrema(b.min)
hDPts[i].Max = metricdata.NewExtrema(b.max)
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

158
vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go generated vendored
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@ -1,16 +1,5 @@
// 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.
// SPDX-License-Identifier: Apache-2.0
package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
@ -20,49 +9,154 @@ import (
"time"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
// datapoint is timestamped measurement data.
type datapoint[N int64 | float64] struct {
timestamp time.Time
value N
attrs attribute.Set
value N
res exemplar.FilteredReservoir[N]
}
func newLastValue[N int64 | float64]() *lastValue[N] {
return &lastValue[N]{values: make(map[attribute.Set]datapoint[N])}
func newLastValue[N int64 | float64](limit int, r func() exemplar.FilteredReservoir[N]) *lastValue[N] {
return &lastValue[N]{
newRes: r,
limit: newLimiter[datapoint[N]](limit),
values: make(map[attribute.Distinct]datapoint[N]),
start: now(),
}
}
// 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]
newRes func() exemplar.FilteredReservoir[N]
limit limiter[datapoint[N]]
values map[attribute.Distinct]datapoint[N]
start time.Time
}
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]) {
func (s *lastValue[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)
d, ok := s.values[attr.Equivalent()]
if !ok {
d.res = s.newRes()
}
d.attrs = attr
d.value = value
d.res.Offer(ctx, value, droppedAttr)
s.values[attr.Equivalent()] = d
}
func (s *lastValue[N]) delta(dest *metricdata.Aggregation) int {
t := now()
// 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])
s.Lock()
defer s.Unlock()
n := s.copyDpts(&gData.DataPoints, t)
// Do not report stale values.
clear(s.values)
// Update start time for delta temporality.
s.start = t
*dest = gData
return n
}
func (s *lastValue[N]) cumulative(dest *metricdata.Aggregation) int {
t := now()
// 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])
s.Lock()
defer s.Unlock()
n := s.copyDpts(&gData.DataPoints, t)
// 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.
*dest = gData
return n
}
// copyDpts copies the datapoints held by s into dest. The number of datapoints
// copied is returned.
func (s *lastValue[N]) copyDpts(dest *[]metricdata.DataPoint[N], t time.Time) int {
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
for _, v := range s.values {
(*dest)[i].Attributes = v.attrs
(*dest)[i].StartTime = s.start
(*dest)[i].Time = t
(*dest)[i].Value = v.value
// Do not report stale values.
delete(s.values, a)
collectExemplars(&(*dest)[i].Exemplars, v.res.Collect)
i++
}
return n
}
// newPrecomputedLastValue returns an aggregator that summarizes a set of
// observations as the last one made.
func newPrecomputedLastValue[N int64 | float64](limit int, r func() exemplar.FilteredReservoir[N]) *precomputedLastValue[N] {
return &precomputedLastValue[N]{lastValue: newLastValue[N](limit, r)}
}
// precomputedLastValue summarizes a set of observations as the last one made.
type precomputedLastValue[N int64 | float64] struct {
*lastValue[N]
}
func (s *precomputedLastValue[N]) delta(dest *metricdata.Aggregation) int {
t := now()
// 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])
s.Lock()
defer s.Unlock()
n := s.copyDpts(&gData.DataPoints, t)
// Do not report stale values.
clear(s.values)
// Update start time for delta temporality.
s.start = t
*dest = gData
return n
}
func (s *precomputedLastValue[N]) cumulative(dest *metricdata.Aggregation) int {
t := now()
// 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])
s.Lock()
defer s.Unlock()
n := s.copyDpts(&gData.DataPoints, t)
// Do not report stale values.
clear(s.values)
*dest = gData
return n
}

42
vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/limit.go generated vendored Normal file
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@ -0,0 +1,42 @@
// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
import "go.opentelemetry.io/otel/attribute"
// overflowSet is the attribute set used to record a measurement when adding
// another distinct attribute set to the aggregate would exceed the aggregate
// limit.
var overflowSet = attribute.NewSet(attribute.Bool("otel.metric.overflow", true))
// limiter limits aggregate values.
type limiter[V any] struct {
// aggLimit is the maximum number of metric streams that can be aggregated.
//
// Any metric stream with attributes distinct from any set already
// aggregated once the aggLimit will be meet will instead be aggregated
// into an "overflow" metric stream. That stream will only contain the
// "otel.metric.overflow"=true attribute.
aggLimit int
}
// newLimiter returns a new Limiter with the provided aggregation limit.
func newLimiter[V any](aggregation int) limiter[V] {
return limiter[V]{aggLimit: aggregation}
}
// Attributes checks if adding a measurement for attrs will exceed the
// aggregation cardinality limit for the existing measurements. If it will,
// overflowSet is returned. Otherwise, if it will not exceed the limit, or the
// limit is not set (limit <= 0), attr is returned.
func (l limiter[V]) Attributes(attrs attribute.Set, measurements map[attribute.Distinct]V) attribute.Set {
if l.aggLimit > 0 {
_, exists := measurements[attrs.Equivalent()]
if !exists && len(measurements) >= l.aggLimit-1 {
return overflowSet
}
}
return attrs
}

104
vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go generated vendored
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@ -1,16 +1,5 @@
// 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.
// SPDX-License-Identifier: Apache-2.0
package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
@ -20,31 +9,55 @@ import (
"time"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
type sumValue[N int64 | float64] struct {
n N
res exemplar.FilteredReservoir[N]
attrs attribute.Set
}
// valueMap is the storage for sums.
type valueMap[N int64 | float64] struct {
sync.Mutex
values map[attribute.Set]N
newRes func() exemplar.FilteredReservoir[N]
limit limiter[sumValue[N]]
values map[attribute.Distinct]sumValue[N]
}
func newValueMap[N int64 | float64]() *valueMap[N] {
return &valueMap[N]{values: make(map[attribute.Set]N)}
func newValueMap[N int64 | float64](limit int, r func() exemplar.FilteredReservoir[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(_ context.Context, value N, attr attribute.Set) {
func (s *valueMap[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
s.Lock()
s.values[attr] += value
s.Unlock()
defer s.Unlock()
attr := s.limit.Attributes(fltrAttr, s.values)
v, ok := s.values[attr.Equivalent()]
if !ok {
v.res = s.newRes()
}
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) *sum[N] {
func newSum[N int64 | float64](monotonic bool, limit int, r func() exemplar.FilteredReservoir[N]) *sum[N] {
return &sum[N]{
valueMap: newValueMap[N](),
valueMap: newValueMap[N](limit, r),
monotonic: monotonic,
start: now(),
}
@ -74,15 +87,16 @@ func (s *sum[N]) delta(dest *metricdata.Aggregation) int {
dPts := reset(sData.DataPoints, n, n)
var i int
for attr, value := range s.values {
dPts[i].Attributes = attr
for _, val := range s.values {
dPts[i].Attributes = val.attrs
dPts[i].StartTime = s.start
dPts[i].Time = t
dPts[i].Value = value
// Do not report stale values.
delete(s.values, attr)
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
@ -108,11 +122,12 @@ func (s *sum[N]) cumulative(dest *metricdata.Aggregation) int {
dPts := reset(sData.DataPoints, n, n)
var i int
for attr, value := range s.values {
dPts[i].Attributes = attr
for _, value := range s.values {
dPts[i].Attributes = value.attrs
dPts[i].StartTime = s.start
dPts[i].Time = t
dPts[i].Value = value
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
@ -129,9 +144,9 @@ func (s *sum[N]) cumulative(dest *metricdata.Aggregation) int {
// 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] {
func newPrecomputedSum[N int64 | float64](monotonic bool, limit int, r func() exemplar.FilteredReservoir[N]) *precomputedSum[N] {
return &precomputedSum[N]{
valueMap: newValueMap[N](),
valueMap: newValueMap[N](limit, r),
monotonic: monotonic,
start: now(),
}
@ -144,12 +159,12 @@ type precomputedSum[N int64 | float64] struct {
monotonic bool
start time.Time
reported map[attribute.Set]N
reported map[attribute.Distinct]N
}
func (s *precomputedSum[N]) delta(dest *metricdata.Aggregation) int {
t := now()
newReported := make(map[attribute.Set]N)
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.
@ -164,20 +179,20 @@ func (s *precomputedSum[N]) delta(dest *metricdata.Aggregation) int {
dPts := reset(sData.DataPoints, n, n)
var i int
for attr, value := range s.values {
delta := value - s.reported[attr]
for key, value := range s.values {
delta := value.n - s.reported[key]
dPts[i].Attributes = attr
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[attr] = value
// Unused attribute sets do not report.
delete(s.values, attr)
newReported[key] = value.n
i++
}
// Unused attribute sets are forgotten.
// Unused attribute sets do not report.
clear(s.values)
s.reported = newReported
// The delta collection cycle resets.
s.start = t
@ -204,16 +219,17 @@ func (s *precomputedSum[N]) cumulative(dest *metricdata.Aggregation) int {
dPts := reset(sData.DataPoints, n, n)
var i int
for attr, value := range s.values {
dPts[i].Attributes = attr
for _, val := range s.values {
dPts[i].Attributes = val.attrs
dPts[i].StartTime = s.start
dPts[i].Time = t
dPts[i].Value = value
dPts[i].Value = val.n
collectExemplars(&dPts[i].Exemplars, val.res.Collect)
// Unused attribute sets do not report.
delete(s.values, attr)
i++
}
// Unused attribute sets do not report.
clear(s.values)
sData.DataPoints = dPts
*dest = sData

6
vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/doc.go generated vendored Normal file
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@ -0,0 +1,6 @@
// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
// Package exemplar provides an implementation of the OpenTelemetry exemplar
// reservoir to be used in metric collection pipelines.
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"

23
vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/drop.go generated vendored Normal file
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@ -0,0 +1,23 @@
// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
import (
"context"
"go.opentelemetry.io/otel/attribute"
)
// Drop returns a [FilteredReservoir] that drops all measurements it is offered.
func Drop[N int64 | float64]() FilteredReservoir[N] { return &dropRes[N]{} }
type dropRes[N int64 | float64] struct{}
// Offer does nothing, all measurements offered will be dropped.
func (r *dropRes[N]) Offer(context.Context, N, []attribute.KeyValue) {}
// Collect resets dest. No exemplars will ever be returned.
func (r *dropRes[N]) Collect(dest *[]Exemplar) {
*dest = (*dest)[:0]
}

29
vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/exemplar.go generated vendored Normal file
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@ -0,0 +1,29 @@
// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
import (
"time"
"go.opentelemetry.io/otel/attribute"
)
// Exemplar is a measurement sampled from a timeseries providing a typical
// example.
type Exemplar struct {
// FilteredAttributes are the attributes recorded with the measurement but
// filtered out of the timeseries' aggregated data.
FilteredAttributes []attribute.KeyValue
// Time is the time when the measurement was recorded.
Time time.Time
// Value is the measured value.
Value Value
// SpanID is the ID of the span that was active during the measurement. If
// no span was active or the span was not sampled this will be empty.
SpanID []byte `json:",omitempty"`
// TraceID is the ID of the trace the active span belonged to during the
// measurement. If no span was active or the span was not sampled this will
// be empty.
TraceID []byte `json:",omitempty"`
}

29
vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/filter.go generated vendored Normal file
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@ -0,0 +1,29 @@
// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
import (
"context"
"go.opentelemetry.io/otel/trace"
)
// Filter determines if a measurement should be offered.
//
// The passed ctx needs to contain any baggage or span that were active
// when the measurement was made. This information may be used by the
// Reservoir in making a sampling decision.
type Filter func(context.Context) bool
// SampledFilter is a [Filter] that will only offer measurements
// if the passed context associated with the measurement contains a sampled
// [go.opentelemetry.io/otel/trace.SpanContext].
func SampledFilter(ctx context.Context) bool {
return trace.SpanContextFromContext(ctx).IsSampled()
}
// AlwaysOnFilter is a [Filter] that always offers measurements.
func AlwaysOnFilter(ctx context.Context) bool {
return true
}

49
vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/filtered_reservoir.go generated vendored Normal file
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@ -0,0 +1,49 @@
// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
import (
"context"
"time"
"go.opentelemetry.io/otel/attribute"
)
// FilteredReservoir wraps a [Reservoir] with a filter.
type FilteredReservoir[N int64 | float64] interface {
// Offer accepts the parameters associated with a measurement. The
// parameters will be stored as an exemplar if the filter decides to
// sample the measurement.
//
// The passed ctx needs to contain any baggage or span that were active
// when the measurement was made. This information may be used by the
// Reservoir in making a sampling decision.
Offer(ctx context.Context, val N, attr []attribute.KeyValue)
// Collect returns all the held exemplars in the reservoir.
Collect(dest *[]Exemplar)
}
// filteredReservoir handles the pre-sampled exemplar of measurements made.
type filteredReservoir[N int64 | float64] struct {
filter Filter
reservoir Reservoir
}
// NewFilteredReservoir creates a [FilteredReservoir] which only offers values
// that are allowed by the filter.
func NewFilteredReservoir[N int64 | float64](f Filter, r Reservoir) FilteredReservoir[N] {
return &filteredReservoir[N]{
filter: f,
reservoir: r,
}
}
func (f *filteredReservoir[N]) Offer(ctx context.Context, val N, attr []attribute.KeyValue) {
if f.filter(ctx) {
// only record the current time if we are sampling this measurment.
f.reservoir.Offer(ctx, time.Now(), NewValue(val), attr)
}
}
func (f *filteredReservoir[N]) Collect(dest *[]Exemplar) { f.reservoir.Collect(dest) }

46
vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/hist.go generated vendored Normal file
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@ -0,0 +1,46 @@
// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
import (
"context"
"slices"
"sort"
"time"
"go.opentelemetry.io/otel/attribute"
)
// Histogram returns a [Reservoir] that samples the last measurement that falls
// within a histogram bucket. The histogram bucket upper-boundaries are define
// by bounds.
//
// The passed bounds will be sorted by this function.
func Histogram(bounds []float64) Reservoir {
slices.Sort(bounds)
return &histRes{
bounds: bounds,
storage: newStorage(len(bounds) + 1),
}
}
type histRes struct {
*storage
// bounds are bucket bounds in ascending order.
bounds []float64
}
func (r *histRes) Offer(ctx context.Context, t time.Time, v Value, a []attribute.KeyValue) {
var x float64
switch v.Type() {
case Int64ValueType:
x = float64(v.Int64())
case Float64ValueType:
x = v.Float64()
default:
panic("unknown value type")
}
r.store[sort.SearchFloat64s(r.bounds, x)] = newMeasurement(ctx, t, v, a)
}

191
vendor/go.opentelemetry.io/otel/sdk/metric/internal/exemplar/rand.go generated vendored Normal file
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@ -0,0 +1,191 @@
// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
import (
"context"
"math"
"math/rand"
"sync"
"time"
"go.opentelemetry.io/otel/attribute"
)
var (
// rng is used to make sampling decisions.
//
// Do not use crypto/rand. There is no reason for the decrease in performance
// given this is not a security sensitive decision.
rng = rand.New(rand.NewSource(time.Now().UnixNano()))
// Ensure concurrent safe accecess to rng and its underlying source.
rngMu sync.Mutex
)
// random returns, as a float64, a uniform pseudo-random number in the open
// interval (0.0,1.0).
func random() float64 {
// TODO: This does not return a uniform number. rng.Float64 returns a
// uniformly random int in [0,2^53) that is divided by 2^53. Meaning it
// returns multiples of 2^-53, and not all floating point numbers between 0
// and 1 (i.e. for values less than 2^-4 the 4 last bits of the significand
// are always going to be 0).
//
// An alternative algorithm should be considered that will actually return
// a uniform number in the interval (0,1). For example, since the default
// rand source provides a uniform distribution for Int63, this can be
// converted following the prototypical code of Mersenne Twister 64 (Takuji
// Nishimura and Makoto Matsumoto:
// http://www.math.sci.hiroshima-u.ac.jp/m-mat/MT/VERSIONS/C-LANG/mt19937-64.c)
//
// (float64(rng.Int63()>>11) + 0.5) * (1.0 / 4503599627370496.0)
//
// There are likely many other methods to explore here as well.
rngMu.Lock()
defer rngMu.Unlock()
f := rng.Float64()
for f == 0 {
f = rng.Float64()
}
return f
}
// FixedSize returns a [Reservoir] that samples at most k exemplars. If there
// are k or less measurements made, the Reservoir will sample each one. If
// there are more than k, the Reservoir will then randomly sample all
// additional measurement with a decreasing probability.
func FixedSize(k int) Reservoir {
r := &randRes{storage: newStorage(k)}
r.reset()
return r
}
type randRes struct {
*storage
// count is the number of measurement seen.
count int64
// next is the next count that will store a measurement at a random index
// once the reservoir has been filled.
next int64
// w is the largest random number in a distribution that is used to compute
// the next next.
w float64
}
func (r *randRes) Offer(ctx context.Context, t time.Time, n Value, a []attribute.KeyValue) {
// The following algorithm is "Algorithm L" from Li, Kim-Hung (4 December
// 1994). "Reservoir-Sampling Algorithms of Time Complexity
// O(n(1+log(N/n)))". ACM Transactions on Mathematical Software. 20 (4):
// 481–493 (https://dl.acm.org/doi/10.1145/198429.198435).
//
// A high-level overview of "Algorithm L":
// 0) Pre-calculate the random count greater than the storage size when
// an exemplar will be replaced.
// 1) Accept all measurements offered until the configured storage size is
// reached.
// 2) Loop:
// a) When the pre-calculate count is reached, replace a random
// existing exemplar with the offered measurement.
// b) Calculate the next random count greater than the existing one
// which will replace another exemplars
//
// The way a "replacement" count is computed is by looking at `n` number of
// independent random numbers each corresponding to an offered measurement.
// Of these numbers the smallest `k` (the same size as the storage
// capacity) of them are kept as a subset. The maximum value in this
// subset, called `w` is used to weight another random number generation
// for the next count that will be considered.
//
// By weighting the next count computation like described, it is able to
// perform a uniformly-weighted sampling algorithm based on the number of
// samples the reservoir has seen so far. The sampling will "slow down" as
// more and more samples are offered so as to reduce a bias towards those
// offered just prior to the end of the collection.
//
// This algorithm is preferred because of its balance of simplicity and
// performance. It will compute three random numbers (the bulk of
// computation time) for each item that becomes part of the reservoir, but
// it does not spend any time on items that do not. In particular it has an
// asymptotic runtime of O(k(1 + log(n/k)) where n is the number of
// measurements offered and k is the reservoir size.
//
// See https://en.wikipedia.org/wiki/Reservoir_sampling for an overview of
// this and other reservoir sampling algorithms. See
// https://github.com/MrAlias/reservoir-sampling for a performance
// comparison of reservoir sampling algorithms.
if int(r.count) < cap(r.store) {
r.store[r.count] = newMeasurement(ctx, t, n, a)
} else {
if r.count == r.next {
// Overwrite a random existing measurement with the one offered.
idx := int(rng.Int63n(int64(cap(r.store))))
r.store[idx] = newMeasurement(ctx, t, n, a)
r.advance()
}
}
r.count++
}
// reset resets r to the initial state.
func (r *randRes) reset() {
// This resets the number of exemplars known.
r.count = 0
// Random index inserts should only happen after the storage is full.
r.next = int64(cap(r.store))
// Initial random number in the series used to generate r.next.
//
// This is set before r.advance to reset or initialize the random number
// series. Without doing so it would always be 0 or never restart a new
// random number series.
//
// This maps the uniform random number in (0,1) to a geometric distribution
// over the same interval. The mean of the distribution is inversely
// proportional to the storage capacity.
r.w = math.Exp(math.Log(random()) / float64(cap(r.store)))
r.advance()
}
// advance updates the count at which the offered measurement will overwrite an
// existing exemplar.
func (r *randRes) advance() {
// Calculate the next value in the random number series.
//
// The current value of r.w is based on the max of a distribution of random
// numbers (i.e. `w = max(u_1,u_2,...,u_k)` for `k` equal to the capacity
// of the storage and each `u` in the interval (0,w)). To calculate the
// next r.w we use the fact that when the next exemplar is selected to be
// included in the storage an existing one will be dropped, and the
// corresponding random number in the set used to calculate r.w will also
// be replaced. The replacement random number will also be within (0,w),
// therefore the next r.w will be based on the same distribution (i.e.
// `max(u_1,u_2,...,u_k)`). Therefore, we can sample the next r.w by
// computing the next random number `u` and take r.w as `w * u^(1/k)`.
r.w *= math.Exp(math.Log(random()) / float64(cap(r.store)))
// Use the new random number in the series to calculate the count of the
// next measurement that will be stored.
//
// Given 0 < r.w < 1, each iteration will result in subsequent r.w being
// smaller. This translates here into the next next being selected against
// a distribution with a higher mean (i.e. the expected value will increase
// and replacements become less likely)
//
// Important to note, the new r.next will always be at least 1 more than
// the last r.next.
r.next += int64(math.Log(random())/math.Log(1-r.w)) + 1
}
func (r *randRes) Collect(dest *[]Exemplar) {
r.storage.Collect(dest)
// Call reset here even though it will reset r.count and restart the random
// number series. This will persist any old exemplars as long as no new
// measurements are offered, but it will also prioritize those new
// measurements that are made over the older collection cycle ones.
r.reset()
}

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// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
import (
"context"
"time"
"go.opentelemetry.io/otel/attribute"
)
// Reservoir holds the sampled exemplar of measurements made.
type Reservoir interface {
// Offer accepts the parameters associated with a measurement. The
// parameters will be stored as an exemplar if the Reservoir decides to
// sample the measurement.
//
// The passed ctx needs to contain any baggage or span that were active
// when the measurement was made. This information may be used by the
// Reservoir in making a sampling decision.
//
// The time t is the time when the measurement was made. The val and attr
// parameters are the value and dropped (filtered) attributes of the
// measurement respectively.
Offer(ctx context.Context, t time.Time, val Value, attr []attribute.KeyValue)
// Collect returns all the held exemplars.
//
// The Reservoir state is preserved after this call.
Collect(dest *[]Exemplar)
}

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// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
import (
"context"
"time"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/trace"
)
// storage is an exemplar storage for [Reservoir] implementations.
type storage struct {
// store are the measurements sampled.
//
// This does not use []metricdata.Exemplar because it potentially would
// require an allocation for trace and span IDs in the hot path of Offer.
store []measurement
}
func newStorage(n int) *storage {
return &storage{store: make([]measurement, n)}
}
// Collect returns all the held exemplars.
//
// The Reservoir state is preserved after this call.
func (r *storage) Collect(dest *[]Exemplar) {
*dest = reset(*dest, len(r.store), len(r.store))
var n int
for _, m := range r.store {
if !m.valid {
continue
}
m.Exemplar(&(*dest)[n])
n++
}
*dest = (*dest)[:n]
}
// measurement is a measurement made by a telemetry system.
type measurement struct {
// FilteredAttributes are the attributes dropped during the measurement.
FilteredAttributes []attribute.KeyValue
// Time is the time when the measurement was made.
Time time.Time
// Value is the value of the measurement.
Value Value
// SpanContext is the SpanContext active when a measurement was made.
SpanContext trace.SpanContext
valid bool
}
// newMeasurement returns a new non-empty Measurement.
func newMeasurement(ctx context.Context, ts time.Time, v Value, droppedAttr []attribute.KeyValue) measurement {
return measurement{
FilteredAttributes: droppedAttr,
Time: ts,
Value: v,
SpanContext: trace.SpanContextFromContext(ctx),
valid: true,
}
}
// Exemplar returns m as an [Exemplar].
func (m measurement) Exemplar(dest *Exemplar) {
dest.FilteredAttributes = m.FilteredAttributes
dest.Time = m.Time
dest.Value = m.Value
if m.SpanContext.HasTraceID() {
traceID := m.SpanContext.TraceID()
dest.TraceID = traceID[:]
} else {
dest.TraceID = dest.TraceID[:0]
}
if m.SpanContext.HasSpanID() {
spanID := m.SpanContext.SpanID()
dest.SpanID = spanID[:]
} else {
dest.SpanID = dest.SpanID[:0]
}
}
func reset[T any](s []T, length, capacity int) []T {
if cap(s) < capacity {
return make([]T, length, capacity)
}
return s[:length]
}

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// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
package exemplar // import "go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
import "math"
// ValueType identifies the type of value used in exemplar data.
type ValueType uint8
const (
// UnknownValueType should not be used. It represents a misconfigured
// Value.
UnknownValueType ValueType = 0
// Int64ValueType represents a Value with int64 data.
Int64ValueType ValueType = 1
// Float64ValueType represents a Value with float64 data.
Float64ValueType ValueType = 2
)
// Value is the value of data held by an exemplar.
type Value struct {
t ValueType
val uint64
}
// NewValue returns a new [Value] for the provided value.
func NewValue[N int64 | float64](value N) Value {
switch v := any(value).(type) {
case int64:
return Value{t: Int64ValueType, val: uint64(v)}
case float64:
return Value{t: Float64ValueType, val: math.Float64bits(v)}
}
return Value{}
}
// Type returns the [ValueType] of data held by v.
func (v Value) Type() ValueType { return v.t }
// Int64 returns the value of v as an int64. If the ValueType of v is not an
// Int64ValueType, 0 is returned.
func (v Value) Int64() int64 {
if v.t == Int64ValueType {
return int64(v.val)
}
return 0
}
// Float64 returns the value of v as an float64. If the ValueType of v is not
// an Float64ValueType, 0 is returned.
func (v Value) Float64() float64 {
if v.t == Float64ValueType {
return math.Float64frombits(v.val)
}
return 0
}

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// 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.
// SPDX-License-Identifier: Apache-2.0
package internal // import "go.opentelemetry.io/otel/sdk/metric/internal"

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# Experimental Features
The metric SDK contains features that have not yet stabilized in the OpenTelemetry specification.
These features are added to the OpenTelemetry Go metric SDK prior to stabilization in the specification so that users can start experimenting with them and provide feedback.
These feature may change in backwards incompatible ways as feedback is applied.
See the [Compatibility and Stability](#compatibility-and-stability) section for more information.
## Features
- [Cardinality Limit](#cardinality-limit)
- [Exemplars](#exemplars)
### Cardinality Limit
The cardinality limit is the hard limit on the number of metric streams that can be collected for a single instrument.
This experimental feature can be enabled by setting the `OTEL_GO_X_CARDINALITY_LIMIT` environment value.
The value must be an integer value.
All other values are ignored.
If the value set is less than or equal to `0`, no limit will be applied.
#### Examples
Set the cardinality limit to 2000.
```console
export OTEL_GO_X_CARDINALITY_LIMIT=2000
```
Set an infinite cardinality limit (functionally equivalent to disabling the feature).
```console
export OTEL_GO_X_CARDINALITY_LIMIT=-1
```
Disable the cardinality limit.
```console
unset OTEL_GO_X_CARDINALITY_LIMIT
```
### Exemplars
A sample of measurements made may be exported directly as a set of exemplars.
This experimental feature can be enabled by setting the `OTEL_GO_X_EXEMPLAR` environment variable.
The value of must be the case-insensitive string of `"true"` to enable the feature.
All other values are ignored.
Exemplar filters are a supported.
The exemplar filter applies to all measurements made.
They filter these measurements, only allowing certain measurements to be passed to the underlying exemplar reservoir.
To change the exemplar filter from the default `"trace_based"` filter set the `OTEL_METRICS_EXEMPLAR_FILTER` environment variable.
The value must be the case-sensitive string defined by the [OpenTelemetry specification].
- `"always_on"`: allows all measurements
- `"always_off"`: denies all measurements
- `"trace_based"`: allows only sampled measurements
All values other than these will result in the default, `"trace_based"`, exemplar filter being used.
[OpenTelemetry specification]: https://github.com/open-telemetry/opentelemetry-specification/blob/a6ca2fd484c9e76fe1d8e1c79c99f08f4745b5ee/specification/configuration/sdk-environment-variables.md#exemplar
#### Examples
Enable exemplars to be exported.
```console
export OTEL_GO_X_EXEMPLAR=true
```
Disable exemplars from being exported.
```console
unset OTEL_GO_X_EXEMPLAR
```
Set the exemplar filter to allow all measurements.
```console
export OTEL_METRICS_EXEMPLAR_FILTER=always_on
```
Set the exemplar filter to deny all measurements.
```console
export OTEL_METRICS_EXEMPLAR_FILTER=always_off
```
Set the exemplar filter to only allow sampled measurements.
```console
export OTEL_METRICS_EXEMPLAR_FILTER=trace_based
```
Revert to the default exemplar filter (`"trace_based"`)
```console
unset OTEL_METRICS_EXEMPLAR_FILTER
```
## Compatibility and Stability
Experimental features do not fall within the scope of the OpenTelemetry Go versioning and stability [policy](../../../../VERSIONING.md).
These features may be removed or modified in successive version releases, including patch versions.
When an experimental feature is promoted to a stable feature, a migration path will be included in the changelog entry of the release.
There is no guarantee that any environment variable feature flags that enabled the experimental feature will be supported by the stable version.
If they are supported, they may be accompanied with a deprecation notice stating a timeline for the removal of that support.

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// Copyright The OpenTelemetry Authors
// SPDX-License-Identifier: Apache-2.0
// Package x contains support for OTel metric SDK experimental features.
//
// This package should only be used for features defined in the specification.
// It should not be used for experiments or new project ideas.
package x // import "go.opentelemetry.io/otel/sdk/metric/internal/x"
import (
"os"
"strconv"
"strings"
)
var (
// Exemplars is an experimental feature flag that defines if exemplars
// should be recorded for metric data-points.
//
// To enable this feature set the OTEL_GO_X_EXEMPLAR environment variable
// to the case-insensitive string value of "true" (i.e. "True" and "TRUE"
// will also enable this).
Exemplars = newFeature("EXEMPLAR", func(v string) (string, bool) {
if strings.ToLower(v) == "true" {
return v, true
}
return "", false
})
// CardinalityLimit is an experimental feature flag that defines if
// cardinality limits should be applied to the recorded metric data-points.
//
// To enable this feature set the OTEL_GO_X_CARDINALITY_LIMIT environment
// variable to the integer limit value you want to use.
//
// Setting OTEL_GO_X_CARDINALITY_LIMIT to a value less than or equal to 0
// will disable the cardinality limits.
CardinalityLimit = newFeature("CARDINALITY_LIMIT", func(v string) (int, bool) {
n, err := strconv.Atoi(v)
if err != nil {
return 0, false
}
return n, true
})
)
// Feature is an experimental feature control flag. It provides a uniform way
// to interact with these feature flags and parse their values.
type Feature[T any] struct {
key string
parse func(v string) (T, bool)
}
func newFeature[T any](suffix string, parse func(string) (T, bool)) Feature[T] {
const envKeyRoot = "OTEL_GO_X_"
return Feature[T]{
key: envKeyRoot + suffix,
parse: parse,
}
}
// Key returns the environment variable key that needs to be set to enable the
// feature.
func (f Feature[T]) Key() string { return f.key }
// Lookup returns the user configured value for the feature and true if the
// user has enabled the feature. Otherwise, if the feature is not enabled, a
// zero-value and false are returned.
func (f Feature[T]) Lookup() (v T, ok bool) {
// https://github.com/open-telemetry/opentelemetry-specification/blob/62effed618589a0bec416a87e559c0a9d96289bb/specification/configuration/sdk-environment-variables.md#parsing-empty-value
//
// > The SDK MUST interpret an empty value of an environment variable the
// > same way as when the variable is unset.
vRaw := os.Getenv(f.key)
if vRaw == "" {
return v, ok
}
return f.parse(vRaw)
}
// Enabled returns if the feature is enabled.
func (f Feature[T]) Enabled() bool {
_, ok := f.Lookup()
return ok
}