[chore] Update usage of OTEL libraries (#2725)

* otel to 1.24
* prometheus exporter to 0.46
* bunotel to 1.1.17

Also:
* Use schemaless URL for metrics
* Add software version to tracing schema

1 files changed, 36 insertions, 9 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
index 8dec14237..4060a2f76 100644
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/aggregate.go
+++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/aggregate.go
@@ -19,6 +19,7 @@ import (
 	"time"
 
 	"go.opentelemetry.io/otel/attribute"
+	"go.opentelemetry.io/otel/sdk/metric/internal/exemplar"
 	"go.opentelemetry.io/otel/sdk/metric/metricdata"
 )
 
@@ -44,17 +45,43 @@ 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.Reservoir[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]) resFunc() func() exemplar.Reservoir[N] {
+	if b.ReservoirFunc != nil {
+		return b.ReservoirFunc
+	}
+
+	return exemplar.Drop[N]
 }
 
-func (b Builder[N]) filter(f Measure[N]) Measure[N] {
+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.
@@ -63,7 +90,7 @@ func (b Builder[N]) filter(f Measure[N]) Measure[N] {
 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())
 
 	return b.filter(lv.measure), func(dest *metricdata.Aggregation) int {
 		// Ignore if dest is not a metricdata.Gauge. The chance for memory
@@ -79,7 +106,7 @@ func (b Builder[N]) LastValue() (Measure[N], ComputeAggregation) {
 // 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 +117,7 @@ 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 +129,7 @@ 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 +141,7 @@ 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