vendor: update buildkit to v0.19.0-rc1

Signed-off-by: Tonis Tiigi <tonistiigi@gmail.com>

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

@@ -8,7 +8,6 @@ import (
 	"time"
 
 	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
 	"go.opentelemetry.io/otel/sdk/metric/metricdata"
 )
 
@@ -38,8 +37,8 @@ type Builder[N int64 | float64] struct {
 	// 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]
+	// DropReservoir reservoir will be used.
+	ReservoirFunc func() FilteredExemplarReservoir[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"
@@ -50,12 +49,12 @@ type Builder[N int64 | float64] struct {
 	AggregationLimit int
 }
 
-func (b Builder[N]) resFunc() func() exemplar.FilteredReservoir[N] {
+func (b Builder[N]) resFunc() func() FilteredExemplarReservoir[N] {
 	if b.ReservoirFunc != nil {
 		return b.ReservoirFunc
 	}
 
-	return exemplar.Drop
+	return DropReservoir
 }
 
 type fltrMeasure[N int64 | float64] func(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue)

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

@@ -0,0 +1,24 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
+
+import (
+	"context"
+
+	"go.opentelemetry.io/otel/attribute"
+	"go.opentelemetry.io/otel/sdk/metric/exemplar"
+)
+
+// DropReservoir returns a [FilteredReservoir] that drops all measurements it is offered.
+func DropReservoir[N int64 | float64]() FilteredExemplarReservoir[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.Exemplar) {
+	*dest = (*dest)[:0]
+}

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

@@ -6,7 +6,7 @@ package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggreg
 import (
 	"sync"
 
-	"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
+	"go.opentelemetry.io/otel/sdk/metric/exemplar"
 	"go.opentelemetry.io/otel/sdk/metric/metricdata"
 )
 

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

@@ -12,7 +12,6 @@ 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"
 )
 
@@ -31,7 +30,7 @@ const (
 // expoHistogramDataPoint is a single data point in an exponential histogram.
 type expoHistogramDataPoint[N int64 | float64] struct {
 	attrs attribute.Set
-	res   exemplar.FilteredReservoir[N]
+	res   FilteredExemplarReservoir[N]
 
 	count uint64
 	min   N
@@ -42,14 +41,14 @@ type expoHistogramDataPoint[N int64 | float64] struct {
 	noMinMax bool
 	noSum    bool
 
-	scale int
+	scale int32
 
 	posBuckets expoBuckets
 	negBuckets expoBuckets
 	zeroCount  uint64
 }
 
-func newExpoHistogramDataPoint[N int64 | float64](attrs attribute.Set, maxSize, maxScale int, noMinMax, noSum bool) *expoHistogramDataPoint[N] {
+func newExpoHistogramDataPoint[N int64 | float64](attrs attribute.Set, maxSize int, maxScale int32, noMinMax, noSum bool) *expoHistogramDataPoint[N] {
 	f := math.MaxFloat64
 	max := N(f) // if N is int64, max will overflow to -9223372036854775808
 	min := N(-f)
@@ -119,11 +118,13 @@ func (p *expoHistogramDataPoint[N]) record(v N) {
 }
 
 // getBin returns the bin v should be recorded into.
-func (p *expoHistogramDataPoint[N]) getBin(v float64) int {
-	frac, exp := math.Frexp(v)
+func (p *expoHistogramDataPoint[N]) getBin(v float64) int32 {
+	frac, expInt := math.Frexp(v)
+	// 11-bit exponential.
+	exp := int32(expInt) // nolint: gosec
 	if p.scale <= 0 {
 		// Because of the choice of fraction is always 1 power of two higher than we want.
-		correction := 1
+		var correction int32 = 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.
@@ -131,7 +132,7 @@ func (p *expoHistogramDataPoint[N]) getBin(v float64) int {
 		}
 		return (exp - correction) >> (-p.scale)
 	}
-	return exp<<p.scale + int(math.Log(frac)*scaleFactors[p.scale]) - 1
+	return exp<<p.scale + int32(math.Log(frac)*scaleFactors[p.scale]) - 1
 }
 
 // scaleFactors are constants used in calculating the logarithm index. They are
@@ -162,20 +163,20 @@ var scaleFactors = [21]float64{
 
 // 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 {
+func (p *expoHistogramDataPoint[N]) scaleChange(bin, startBin int32, length int) int32 {
 	if length == 0 {
 		// No need to rescale if there are no buckets.
 		return 0
 	}
 
-	low := startBin
-	high := bin
+	low := int(startBin)
+	high := int(bin)
 	if startBin >= bin {
-		low = bin
-		high = startBin + length - 1
+		low = int(bin)
+		high = int(startBin) + length - 1
 	}
 
-	count := 0
+	var count int32
 	for high-low >= p.maxSize {
 		low = low >> 1
 		high = high >> 1
@@ -189,39 +190,39 @@ func (p *expoHistogramDataPoint[N]) scaleChange(bin, startBin, length int) int {
 
 // expoBuckets is a set of buckets in an exponential histogram.
 type expoBuckets struct {
-	startBin int
+	startBin int32
 	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) {
+func (b *expoBuckets) record(bin int32) {
 	if len(b.counts) == 0 {
 		b.counts = []uint64{1}
 		b.startBin = bin
 		return
 	}
 
-	endBin := b.startBin + len(b.counts) - 1
+	endBin := int(b.startBin) + len(b.counts) - 1
 
 	// if the new bin is inside the current range
-	if bin >= b.startBin && bin <= endBin {
+	if bin >= b.startBin && int(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
+		newLength := endBin - int(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[:])
+		copy(b.counts[shift:origLen+int(shift)], b.counts[:])
 		b.counts = b.counts[:newLength]
-		for i := 1; i < shift; i++ {
+		for i := 1; i < int(shift); i++ {
 			b.counts[i] = 0
 		}
 		b.startBin = bin
@@ -229,17 +230,17 @@ func (b *expoBuckets) record(bin int) {
 		return
 	}
 	// if the new is after the end add spaces to the end
-	if bin > endBin {
-		if bin-b.startBin < cap(b.counts) {
+	if int(bin) > endBin {
+		if int(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++ {
+			for i := endBin + 1 - int(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)
+		end := make([]uint64, int(bin-b.startBin)-len(b.counts)+1)
 		b.counts = append(b.counts, end...)
 		b.counts[bin-b.startBin] = 1
 	}
@@ -247,7 +248,7 @@ func (b *expoBuckets) record(bin int) {
 
 // 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) {
+func (b *expoBuckets) downscale(delta int32) {
 	// Example
 	// delta = 2
 	// Original offset: -6
@@ -262,19 +263,19 @@ func (b *expoBuckets) downscale(delta int) {
 		return
 	}
 
-	steps := 1 << delta
+	steps := int32(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]
+		idx := i + int(offset)
+		if idx%int(steps) == 0 {
+			b.counts[idx/int(steps)] = b.counts[i]
 			continue
 		}
-		b.counts[idx/steps] += b.counts[i]
+		b.counts[idx/int(steps)] += b.counts[i]
 	}
 
-	lastIdx := (len(b.counts) - 1 + offset) / steps
+	lastIdx := (len(b.counts) - 1 + int(offset)) / int(steps)
 	b.counts = b.counts[:lastIdx+1]
 	b.startBin = b.startBin >> delta
 }
@@ -282,12 +283,12 @@ 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, limit int, r func() exemplar.FilteredReservoir[N]) *expoHistogram[N] {
+func newExponentialHistogram[N int64 | float64](maxSize, maxScale int32, noMinMax, noSum bool, limit int, r func() FilteredExemplarReservoir[N]) *expoHistogram[N] {
 	return &expoHistogram[N]{
 		noSum:    noSum,
 		noMinMax: noMinMax,
 		maxSize:  int(maxSize),
-		maxScale: int(maxScale),
+		maxScale: maxScale,
 
 		newRes: r,
 		limit:  newLimiter[*expoHistogramDataPoint[N]](limit),
@@ -303,9 +304,9 @@ type expoHistogram[N int64 | float64] struct {
 	noSum    bool
 	noMinMax bool
 	maxSize  int
-	maxScale int
+	maxScale int32
 
-	newRes   func() exemplar.FilteredReservoir[N]
+	newRes   func() FilteredExemplarReservoir[N]
 	limit    limiter[*expoHistogramDataPoint[N]]
 	values   map[attribute.Distinct]*expoHistogramDataPoint[N]
 	valuesMu sync.Mutex
@@ -354,15 +355,15 @@ func (e *expoHistogram[N]) delta(dest *metricdata.Aggregation) int {
 		hDPts[i].StartTime = e.start
 		hDPts[i].Time = t
 		hDPts[i].Count = val.count
-		hDPts[i].Scale = int32(val.scale)
+		hDPts[i].Scale = val.scale
 		hDPts[i].ZeroCount = val.zeroCount
 		hDPts[i].ZeroThreshold = 0.0
 
-		hDPts[i].PositiveBucket.Offset = int32(val.posBuckets.startBin)
+		hDPts[i].PositiveBucket.Offset = 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(val.negBuckets.startBin)
+		hDPts[i].NegativeBucket.Offset = 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)
 
@@ -407,15 +408,15 @@ func (e *expoHistogram[N]) cumulative(dest *metricdata.Aggregation) int {
 		hDPts[i].StartTime = e.start
 		hDPts[i].Time = t
 		hDPts[i].Count = val.count
-		hDPts[i].Scale = int32(val.scale)
+		hDPts[i].Scale = val.scale
 		hDPts[i].ZeroCount = val.zeroCount
 		hDPts[i].ZeroThreshold = 0.0
 
-		hDPts[i].PositiveBucket.Offset = int32(val.posBuckets.startBin)
+		hDPts[i].PositiveBucket.Offset = 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(val.negBuckets.startBin)
+		hDPts[i].NegativeBucket.Offset = 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)
 

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

@@ -0,0 +1,50 @@
+// Copyright The OpenTelemetry Authors
+// SPDX-License-Identifier: Apache-2.0
+
+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/exemplar"
+)
+
+// FilteredExemplarReservoir wraps a [exemplar.Reservoir] with a filter.
+type FilteredExemplarReservoir[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.Exemplar)
+}
+
+// filteredExemplarReservoir handles the pre-sampled exemplar of measurements made.
+type filteredExemplarReservoir[N int64 | float64] struct {
+	filter    exemplar.Filter
+	reservoir exemplar.Reservoir
+}
+
+// NewFilteredExemplarReservoir creates a [FilteredExemplarReservoir] which only offers values
+// that are allowed by the filter.
+func NewFilteredExemplarReservoir[N int64 | float64](f exemplar.Filter, r exemplar.Reservoir) FilteredExemplarReservoir[N] {
+	return &filteredExemplarReservoir[N]{
+		filter:    f,
+		reservoir: r,
+	}
+}
+
+func (f *filteredExemplarReservoir[N]) Offer(ctx context.Context, val N, attr []attribute.KeyValue) {
+	if f.filter(ctx) {
+		// only record the current time if we are sampling this measurement.
+		f.reservoir.Offer(ctx, time.Now(), exemplar.NewValue(val), attr)
+	}
+}
+
+func (f *filteredExemplarReservoir[N]) Collect(dest *[]exemplar.Exemplar) { f.reservoir.Collect(dest) }

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

@@ -11,13 +11,12 @@ import (
 	"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]
+	res   FilteredExemplarReservoir[N]
 
 	counts   []uint64
 	count    uint64
@@ -48,13 +47,13 @@ type histValues[N int64 | float64] struct {
 	noSum  bool
 	bounds []float64
 
-	newRes   func() exemplar.FilteredReservoir[N]
+	newRes   func() FilteredExemplarReservoir[N]
 	limit    limiter[*buckets[N]]
 	values   map[attribute.Distinct]*buckets[N]
 	valuesMu sync.Mutex
 }
 
-func newHistValues[N int64 | float64](bounds []float64, noSum bool, limit int, r func() exemplar.FilteredReservoir[N]) *histValues[N] {
+func newHistValues[N int64 | float64](bounds []float64, noSum bool, limit int, r func() FilteredExemplarReservoir[N]) *histValues[N] {
 	// The responsibility of keeping all buckets correctly associated with the
 	// passed boundaries is ultimately this type's responsibility. Make a copy
 	// here so we can always guarantee this. Or, in the case of failure, have
@@ -109,7 +108,7 @@ func (s *histValues[N]) measure(ctx context.Context, value N, fltrAttr attribute
 
 // newHistogram returns an Aggregator that summarizes a set of measurements as
 // an histogram.
-func newHistogram[N int64 | float64](boundaries []float64, noMinMax, noSum bool, limit int, r func() exemplar.FilteredReservoir[N]) *histogram[N] {
+func newHistogram[N int64 | float64](boundaries []float64, noMinMax, noSum bool, limit int, r func() FilteredExemplarReservoir[N]) *histogram[N] {
 	return &histogram[N]{
 		histValues: newHistValues[N](boundaries, noSum, limit, r),
 		noMinMax:   noMinMax,

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

@@ -9,7 +9,6 @@ import (
 	"time"
 
 	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
 	"go.opentelemetry.io/otel/sdk/metric/metricdata"
 )
 
@@ -17,10 +16,10 @@ import (
 type datapoint[N int64 | float64] struct {
 	attrs attribute.Set
 	value N
-	res   exemplar.FilteredReservoir[N]
+	res   FilteredExemplarReservoir[N]
 }
 
-func newLastValue[N int64 | float64](limit int, r func() exemplar.FilteredReservoir[N]) *lastValue[N] {
+func newLastValue[N int64 | float64](limit int, r func() FilteredExemplarReservoir[N]) *lastValue[N] {
 	return &lastValue[N]{
 		newRes: r,
 		limit:  newLimiter[datapoint[N]](limit),
@@ -33,7 +32,7 @@ func newLastValue[N int64 | float64](limit int, r func() exemplar.FilteredReserv
 type lastValue[N int64 | float64] struct {
 	sync.Mutex
 
-	newRes func() exemplar.FilteredReservoir[N]
+	newRes func() FilteredExemplarReservoir[N]
 	limit  limiter[datapoint[N]]
 	values map[attribute.Distinct]datapoint[N]
 	start  time.Time
@@ -115,7 +114,7 @@ func (s *lastValue[N]) copyDpts(dest *[]metricdata.DataPoint[N], t time.Time) in
 
 // 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] {
+func newPrecomputedLastValue[N int64 | float64](limit int, r func() FilteredExemplarReservoir[N]) *precomputedLastValue[N] {
 	return &precomputedLastValue[N]{lastValue: newLastValue[N](limit, r)}
 }
 

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

@@ -9,25 +9,24 @@ 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]
+	res   FilteredExemplarReservoir[N]
 	attrs attribute.Set
 }
 
 // valueMap is the storage for sums.
 type valueMap[N int64 | float64] struct {
 	sync.Mutex
-	newRes func() exemplar.FilteredReservoir[N]
+	newRes func() FilteredExemplarReservoir[N]
 	limit  limiter[sumValue[N]]
 	values map[attribute.Distinct]sumValue[N]
 }
 
-func newValueMap[N int64 | float64](limit int, r func() exemplar.FilteredReservoir[N]) *valueMap[N] {
+func newValueMap[N int64 | float64](limit int, r func() FilteredExemplarReservoir[N]) *valueMap[N] {
 	return &valueMap[N]{
 		newRes: r,
 		limit:  newLimiter[sumValue[N]](limit),
@@ -55,7 +54,7 @@ func (s *valueMap[N]) measure(ctx context.Context, value N, fltrAttr attribute.S
 // newSum returns an aggregator that summarizes a set of measurements as their
 // arithmetic sum. Each sum is scoped by attributes and the aggregation cycle
 // the measurements were made in.
-func newSum[N int64 | float64](monotonic bool, limit int, r func() exemplar.FilteredReservoir[N]) *sum[N] {
+func newSum[N int64 | float64](monotonic bool, limit int, r func() FilteredExemplarReservoir[N]) *sum[N] {
 	return &sum[N]{
 		valueMap:  newValueMap[N](limit, r),
 		monotonic: monotonic,
@@ -142,9 +141,9 @@ 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
+// observations as their arithmetic sum. Each sum is scoped by attributes and
 // the aggregation cycle the measurements were made in.
-func newPrecomputedSum[N int64 | float64](monotonic bool, limit int, r func() exemplar.FilteredReservoir[N]) *precomputedSum[N] {
+func newPrecomputedSum[N int64 | float64](monotonic bool, limit int, r func() FilteredExemplarReservoir[N]) *precomputedSum[N] {
 	return &precomputedSum[N]{
 		valueMap:  newValueMap[N](limit, r),
 		monotonic: monotonic,
@@ -152,7 +151,7 @@ func newPrecomputedSum[N int64 | float64](monotonic bool, limit int, r func() ex
 	}
 }
 
-// precomputedSum summarizes a set of observatrions as their arithmetic sum.
+// precomputedSum summarizes a set of observations as their arithmetic sum.
 type precomputedSum[N int64 | float64] struct {
 	*valueMap[N]