Move time.Now call into exemplar reservoir to improve performance

CHANGELOG.md

@@ -30,6 +30,7 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 - Upgrade `go.opentelemetry.io/otel/semconv/v1.25.0` to `go.opentelemetry.io/otel/semconv/v1.26.0` in `go.opentelemetry.io/otel/sdk/resource`. (#5490)
 - Upgrade `go.opentelemetry.io/otel/semconv/v1.25.0` to `go.opentelemetry.io/otel/semconv/v1.26.0` in `go.opentelemetry.io/otel/sdk/trace`. (#5490)
 - Use non-generic functions in the `Start` method of `"go.opentelemetry.io/otel/sdk/trace".Trace` to reduce memory allocation. (#5497)
+- Improve performance of metric instruments in `go.opentelemetry.io/otel/sdk/metric` by removing unnecessary calls to time.Now. (#5545)
 
 ### Fixed
 

sdk/metric/exemplar.go

@@ -19,67 +19,63 @@ import (
 // Note: This will only return non-nil values when the experimental exemplar
 // feature is enabled and the OTEL_METRICS_EXEMPLAR_FILTER environment variable
 // is not set to always_off.
-func reservoirFunc(agg Aggregation) func() exemplar.Reservoir {
+func reservoirFunc[N int64 | float64](agg Aggregation) func() exemplar.FilteredReservoir[N] {
 	if !x.Exemplars.Enabled() {
 		return nil
 	}
-
-	// https://github.com/open-telemetry/opentelemetry-specification/blob/d4b241f451674e8f611bb589477680341006ad2b/specification/metrics/sdk.md#exemplar-defaults
-	resF := func() func() exemplar.Reservoir {
-		// Explicit bucket histogram aggregation with more than 1 bucket will
-		// use AlignedHistogramBucketExemplarReservoir.
-		a, ok := agg.(AggregationExplicitBucketHistogram)
-		if ok && len(a.Boundaries) > 0 {
-			cp := slices.Clone(a.Boundaries)
-			return func() exemplar.Reservoir {
-				bounds := cp
-				return exemplar.Histogram(bounds)
-			}
-		}
-
-		var n int
-		if a, ok := agg.(AggregationBase2ExponentialHistogram); ok {
-			// Base2 Exponential Histogram Aggregation SHOULD use a
-			// SimpleFixedSizeExemplarReservoir with a reservoir equal to the
-			// smaller of the maximum number of buckets configured on the
-			// aggregation or twenty (e.g. min(20, max_buckets)).
-			n = int(a.MaxSize)
-			if n > 20 {
-				n = 20
-			}
-		} else {
-			// https://github.com/open-telemetry/opentelemetry-specification/blob/e94af89e3d0c01de30127a0f423e912f6cda7bed/specification/metrics/sdk.md#simplefixedsizeexemplarreservoir
-			//   This Exemplar reservoir MAY take a configuration parameter for
-			//   the size of the reservoir. If no size configuration is
-			//   provided, the default size MAY be the number of possible
-			//   concurrent threads (e.g. number of CPUs) to help reduce
-			//   contention. Otherwise, a default size of 1 SHOULD be used.
-			n = runtime.NumCPU()
-			if n < 1 {
-				// Should never be the case, but be defensive.
-				n = 1
-			}
-		}
-
-		return func() exemplar.Reservoir {
-			return exemplar.FixedSize(n)
-		}
-	}
-
 	// https://github.com/open-telemetry/opentelemetry-specification/blob/d4b241f451674e8f611bb589477680341006ad2b/specification/configuration/sdk-environment-variables.md#exemplar
 	const filterEnvKey = "OTEL_METRICS_EXEMPLAR_FILTER"
 
+	var filter exemplar.Filter
+
 	switch os.Getenv(filterEnvKey) {
 	case "always_on":
-		return resF()
+		filter = exemplar.AlwaysOnFilter
 	case "always_off":
 		return exemplar.Drop
 	case "trace_based":
 		fallthrough
 	default:
-		newR := resF()
-		return func() exemplar.Reservoir {
-			return exemplar.SampledFilter(newR())
+		filter = exemplar.SampledFilter
+	}
+
+	// https://github.com/open-telemetry/opentelemetry-specification/blob/d4b241f451674e8f611bb589477680341006ad2b/specification/metrics/sdk.md#exemplar-defaults
+	// Explicit bucket histogram aggregation with more than 1 bucket will
+	// use AlignedHistogramBucketExemplarReservoir.
+	a, ok := agg.(AggregationExplicitBucketHistogram)
+	if ok && len(a.Boundaries) > 0 {
+		cp := slices.Clone(a.Boundaries)
+		return func() exemplar.FilteredReservoir[N] {
+			bounds := cp
+			return exemplar.NewFilteredReservoir[N](filter, exemplar.Histogram(bounds))
 		}
 	}
+
+	var n int
+	if a, ok := agg.(AggregationBase2ExponentialHistogram); ok {
+		// Base2 Exponential Histogram Aggregation SHOULD use a
+		// SimpleFixedSizeExemplarReservoir with a reservoir equal to the
+		// smaller of the maximum number of buckets configured on the
+		// aggregation or twenty (e.g. min(20, max_buckets)).
+		n = int(a.MaxSize)
+		if n > 20 {
+			n = 20
+		}
+	} else {
+		// https://github.com/open-telemetry/opentelemetry-specification/blob/e94af89e3d0c01de30127a0f423e912f6cda7bed/specification/metrics/sdk.md#simplefixedsizeexemplarreservoir
+		//   This Exemplar reservoir MAY take a configuration parameter for
+		//   the size of the reservoir. If no size configuration is
+		//   provided, the default size MAY be the number of possible
+		//   concurrent threads (e.g. number of CPUs) to help reduce
+		//   contention. Otherwise, a default size of 1 SHOULD be used.
+		n = runtime.NumCPU()
+		if n < 1 {
+			// Should never be the case, but be defensive.
+			n = 1
+		}
+	}
+
+	return func() exemplar.FilteredReservoir[N] {
+		return exemplar.NewFilteredReservoir[N](filter, exemplar.FixedSize(n))
+	}
 }

sdk/metric/internal/aggregate/aggregate.go

@@ -39,7 +39,7 @@ type Builder[N int64 | float64] struct {
 	//
 	// If this is not provided a default factory function that returns an
 	// exemplar.Drop reservoir will be used.
-	ReservoirFunc func() exemplar.Reservoir
+	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"
@@ -50,7 +50,7 @@ type Builder[N int64 | float64] struct {
 	AggregationLimit int
 }
 
-func (b Builder[N]) resFunc() func() exemplar.Reservoir {
+func (b Builder[N]) resFunc() func() exemplar.FilteredReservoir[N] {
 	if b.ReservoirFunc != nil {
 		return b.ReservoirFunc
 	}

sdk/metric/internal/aggregate/aggregate_test.go

@@ -73,8 +73,8 @@ func (c *clock) Register() (unregister func()) {
 	return func() { now = orig }
 }
 
-func dropExemplars[N int64 | float64]() exemplar.Reservoir {
-	return exemplar.Drop()
+func dropExemplars[N int64 | float64]() exemplar.FilteredReservoir[N] {
+	return exemplar.Drop[N]()
 }
 
 func TestBuilderFilter(t *testing.T) {

sdk/metric/internal/aggregate/exponential_histogram.go

@@ -31,7 +31,7 @@ const (
 // expoHistogramDataPoint is a single data point in an exponential histogram.
 type expoHistogramDataPoint[N int64 | float64] struct {
 	attrs attribute.Set
-	res   exemplar.Reservoir
+	res   exemplar.FilteredReservoir[N]
 
 	count uint64
 	min   N
@@ -282,7 +282,7 @@ 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.Reservoir) *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,
@@ -305,7 +305,7 @@ type expoHistogram[N int64 | float64] struct {
 	maxSize  int
 	maxScale int
 
-	newRes   func() exemplar.Reservoir
+	newRes   func() exemplar.FilteredReservoir[N]
 	limit    limiter[*expoHistogramDataPoint[N]]
 	values   map[attribute.Distinct]*expoHistogramDataPoint[N]
 	valuesMu sync.Mutex
@@ -319,8 +319,6 @@ func (e *expoHistogram[N]) measure(ctx context.Context, value N, fltrAttr attrib
 		return
 	}
 
-	t := now()
-
 	e.valuesMu.Lock()
 	defer e.valuesMu.Unlock()
 
@@ -333,7 +331,7 @@ func (e *expoHistogram[N]) measure(ctx context.Context, value N, fltrAttr attrib
 		e.values[attr.Equivalent()] = v
 	}
 	v.record(value)
-	v.res.Offer(ctx, t, exemplar.NewValue(value), droppedAttr)
+	v.res.Offer(ctx, value, droppedAttr)
 }
 
 func (e *expoHistogram[N]) delta(dest *metricdata.Aggregation) int {

sdk/metric/internal/aggregate/exponential_histogram_test.go

@@ -778,7 +778,7 @@ func testDeltaExpoHist[N int64 | float64]() func(t *testing.T) {
 						{
 							Attributes: fltrAlice,
 							StartTime:  y2kPlus(1),
-							Time:       y2kPlus(9),
+							Time:       y2kPlus(2),
 							Count:      7,
 							Min:        metricdata.NewExtrema[N](-1),
 							Max:        metricdata.NewExtrema[N](16),
@@ -832,8 +832,8 @@ func testDeltaExpoHist[N int64 | float64]() func(t *testing.T) {
 					DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
 						{
 							Attributes: fltrAlice,
-							StartTime:  y2kPlus(10),
-							Time:       y2kPlus(24),
+							StartTime:  y2kPlus(3),
+							Time:       y2kPlus(4),
 							Count:      7,
 							Min:        metricdata.NewExtrema[N](-1),
 							Max:        metricdata.NewExtrema[N](16),
@@ -850,8 +850,8 @@ func testDeltaExpoHist[N int64 | float64]() func(t *testing.T) {
 						},
 						{
 							Attributes: overflowSet,
-							StartTime:  y2kPlus(10),
-							Time:       y2kPlus(24),
+							StartTime:  y2kPlus(3),
+							Time:       y2kPlus(4),
 							Count:      6,
 							Min:        metricdata.NewExtrema[N](1),
 							Max:        metricdata.NewExtrema[N](16),
@@ -905,7 +905,7 @@ func testCumulativeExpoHist[N int64 | float64]() func(t *testing.T) {
 						{
 							Attributes: fltrAlice,
 							StartTime:  y2kPlus(0),
-							Time:       y2kPlus(9),
+							Time:       y2kPlus(2),
 							Count:      7,
 							Min:        metricdata.NewExtrema[N](-1),
 							Max:        metricdata.NewExtrema[N](16),
@@ -938,7 +938,7 @@ func testCumulativeExpoHist[N int64 | float64]() func(t *testing.T) {
 						{
 							Attributes: fltrAlice,
 							StartTime:  y2kPlus(0),
-							Time:       y2kPlus(13),
+							Time:       y2kPlus(3),
 							Count:      10,
 							Min:        metricdata.NewExtrema[N](-1),
 							Max:        metricdata.NewExtrema[N](16),
@@ -967,7 +967,7 @@ func testCumulativeExpoHist[N int64 | float64]() func(t *testing.T) {
 						{
 							Attributes: fltrAlice,
 							StartTime:  y2kPlus(0),
-							Time:       y2kPlus(14),
+							Time:       y2kPlus(4),
 							Count:      10,
 							Min:        metricdata.NewExtrema[N](-1),
 							Max:        metricdata.NewExtrema[N](16),
@@ -1004,7 +1004,7 @@ func testCumulativeExpoHist[N int64 | float64]() func(t *testing.T) {
 						{
 							Attributes: fltrAlice,
 							StartTime:  y2kPlus(0),
-							Time:       y2kPlus(21),
+							Time:       y2kPlus(5),
 							Count:      10,
 							Min:        metricdata.NewExtrema[N](-1),
 							Max:        metricdata.NewExtrema[N](16),
@@ -1022,7 +1022,7 @@ func testCumulativeExpoHist[N int64 | float64]() func(t *testing.T) {
 						{
 							Attributes: overflowSet,
 							StartTime:  y2kPlus(0),
-							Time:       y2kPlus(21),
+							Time:       y2kPlus(5),
 							Count:      6,
 							Min:        metricdata.NewExtrema[N](1),
 							Max:        metricdata.NewExtrema[N](16),

sdk/metric/internal/aggregate/histogram.go

@@ -17,7 +17,7 @@ import (
 
 type buckets[N int64 | float64] struct {
 	attrs attribute.Set
-	res   exemplar.Reservoir
+	res   exemplar.FilteredReservoir[N]
 
 	counts   []uint64
 	count    uint64
@@ -48,13 +48,13 @@ type histValues[N int64 | float64] struct {
 	noSum  bool
 	bounds []float64
 
-	newRes   func() exemplar.Reservoir
+	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, limit int, r func() exemplar.Reservoir) *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
@@ -80,8 +80,6 @@ func (s *histValues[N]) measure(ctx context.Context, value N, fltrAttr attribute
 	// (s.bounds[len(s.bounds)-1], +∞).
 	idx := sort.SearchFloat64s(s.bounds, float64(value))
 
-	t := now()
-
 	s.valuesMu.Lock()
 	defer s.valuesMu.Unlock()
 
@@ -106,12 +104,12 @@ func (s *histValues[N]) measure(ctx context.Context, value N, fltrAttr attribute
 	if !s.noSum {
 		b.sum(value)
 	}
-	b.res.Offer(ctx, t, exemplar.NewValue(value), droppedAttr)
+	b.res.Offer(ctx, value, droppedAttr)
 }
 
 // newHistogram returns an Aggregator that summarizes a set of measurements as
 // an histogram.
-func newHistogram[N int64 | float64](boundaries []float64, noMinMax, noSum bool, limit int, r func() exemplar.Reservoir) *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, limit, r),
 		noMinMax:   noMinMax,

sdk/metric/internal/aggregate/histogram_test.go

@@ -80,8 +80,8 @@ func testDeltaHist[N int64 | float64](c conf[N]) func(t *testing.T) {
 				agg: metricdata.Histogram[N]{
 					Temporality: metricdata.DeltaTemporality,
 					DataPoints: []metricdata.HistogramDataPoint[N]{
-						c.hPt(fltrAlice, 2, 3, y2kPlus(1), y2kPlus(7)),
-						c.hPt(fltrBob, 10, 2, y2kPlus(1), y2kPlus(7)),
+						c.hPt(fltrAlice, 2, 3, y2kPlus(1), y2kPlus(2)),
+						c.hPt(fltrBob, 10, 2, y2kPlus(1), y2kPlus(2)),
 					},
 				},
 			},
@@ -96,8 +96,8 @@ func testDeltaHist[N int64 | float64](c conf[N]) func(t *testing.T) {
 				agg: metricdata.Histogram[N]{
 					Temporality: metricdata.DeltaTemporality,
 					DataPoints: []metricdata.HistogramDataPoint[N]{
-						c.hPt(fltrAlice, 10, 1, y2kPlus(7), y2kPlus(10)),
-						c.hPt(fltrBob, 3, 1, y2kPlus(7), y2kPlus(10)),
+						c.hPt(fltrAlice, 10, 1, y2kPlus(2), y2kPlus(3)),
+						c.hPt(fltrBob, 3, 1, y2kPlus(2), y2kPlus(3)),
 					},
 				},
 			},
@@ -126,9 +126,9 @@ func testDeltaHist[N int64 | float64](c conf[N]) func(t *testing.T) {
 				agg: metricdata.Histogram[N]{
 					Temporality: metricdata.DeltaTemporality,
 					DataPoints: []metricdata.HistogramDataPoint[N]{
-						c.hPt(fltrAlice, 1, 1, y2kPlus(11), y2kPlus(16)),
-						c.hPt(fltrBob, 1, 1, y2kPlus(11), y2kPlus(16)),
-						c.hPt(overflowSet, 1, 2, y2kPlus(11), y2kPlus(16)),
+						c.hPt(fltrAlice, 1, 1, y2kPlus(4), y2kPlus(5)),
+						c.hPt(fltrBob, 1, 1, y2kPlus(4), y2kPlus(5)),
+						c.hPt(overflowSet, 1, 2, y2kPlus(4), y2kPlus(5)),
 					},
 				},
 			},
@@ -167,8 +167,8 @@ func testCumulativeHist[N int64 | float64](c conf[N]) func(t *testing.T) {
 				agg: metricdata.Histogram[N]{
 					Temporality: metricdata.CumulativeTemporality,
 					DataPoints: []metricdata.HistogramDataPoint[N]{
-						c.hPt(fltrAlice, 2, 3, y2kPlus(0), y2kPlus(7)),
-						c.hPt(fltrBob, 10, 2, y2kPlus(0), y2kPlus(7)),
+						c.hPt(fltrAlice, 2, 3, y2kPlus(0), y2kPlus(2)),
+						c.hPt(fltrBob, 10, 2, y2kPlus(0), y2kPlus(2)),
 					},
 				},
 			},
@@ -183,8 +183,8 @@ func testCumulativeHist[N int64 | float64](c conf[N]) func(t *testing.T) {
 				agg: metricdata.Histogram[N]{
 					Temporality: metricdata.CumulativeTemporality,
 					DataPoints: []metricdata.HistogramDataPoint[N]{
-						c.hPt(fltrAlice, 2, 4, y2kPlus(0), y2kPlus(10)),
-						c.hPt(fltrBob, 10, 3, y2kPlus(0), y2kPlus(10)),
+						c.hPt(fltrAlice, 2, 4, y2kPlus(0), y2kPlus(3)),
+						c.hPt(fltrBob, 10, 3, y2kPlus(0), y2kPlus(3)),
 					},
 				},
 			},
@@ -196,8 +196,8 @@ func testCumulativeHist[N int64 | float64](c conf[N]) func(t *testing.T) {
 				agg: metricdata.Histogram[N]{
 					Temporality: metricdata.CumulativeTemporality,
 					DataPoints: []metricdata.HistogramDataPoint[N]{
-						c.hPt(fltrAlice, 2, 4, y2kPlus(0), y2kPlus(11)),
-						c.hPt(fltrBob, 10, 3, y2kPlus(0), y2kPlus(11)),
+						c.hPt(fltrAlice, 2, 4, y2kPlus(0), y2kPlus(4)),
+						c.hPt(fltrBob, 10, 3, y2kPlus(0), y2kPlus(4)),
 					},
 				},
 			},
@@ -213,9 +213,9 @@ func testCumulativeHist[N int64 | float64](c conf[N]) func(t *testing.T) {
 				agg: metricdata.Histogram[N]{
 					Temporality: metricdata.CumulativeTemporality,
 					DataPoints: []metricdata.HistogramDataPoint[N]{
-						c.hPt(fltrAlice, 2, 4, y2kPlus(0), y2kPlus(14)),
-						c.hPt(fltrBob, 10, 3, y2kPlus(0), y2kPlus(14)),
-						c.hPt(overflowSet, 1, 2, y2kPlus(0), y2kPlus(14)),
+						c.hPt(fltrAlice, 2, 4, y2kPlus(0), y2kPlus(5)),
+						c.hPt(fltrBob, 10, 3, y2kPlus(0), y2kPlus(5)),
+						c.hPt(overflowSet, 1, 2, y2kPlus(0), y2kPlus(5)),
 					},
 				},
 			},