Add cardinality limiting to the metric SDK as an experimental feature (…

…#4457)

* Add agg limiting func

* Add unit test for limitAttr

* Add limiting to aggregate types

* Add internal x pkg for experimental feature-flagging

* Connect cardinality limit to metric SDK

* Replace limitAttr fn with limiter type

The Attribute method is still inlinable.

* Use x.CardinalityLimit directly

* Simplify limiter test

* Add limiter benchmark

* Document the AggregationLimit field

* Test sum limits

* Test limit for last value

* Test histogram limit

* Refactor expo hist test to use existing fixtures

The tests for the exponential histogram create their own testing
fixtures. There is nothing these new fixtures do that cannot already be
done with the existing testing fixtures used by all the other aggregate
functions. Unify the exponential histogram testing to use the existing
fixtures.

* Test the ExponentialHistogram limit

* Fix lint

* Add docs

* Rename aggregation field to aggLimit

---------

Co-authored-by: Robert Pająk <pellared@hotmail.com>

CHANGELOG.md

@@ -19,6 +19,8 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 - The `go.opentelemetry.io/otel/semconv/v1.24.0` package.
   The package contains semantic conventions from the `v1.24.0` version of the OpenTelemetry Semantic Conventions. (#4770)
 - Add `WithResourceAsConstantLabels` option to apply resource attributes for every metric emitted by the Prometheus exporter. (#4733)
+- Experimental cardinality limiting is added to the metric SDK.
+  See [metric documentation](./sdk/metric/EXPERIMENTAL.md#cardinality-limit) for more information about this feature and how to enable it. (#4457)
 
 ### Changed
 

sdk/metric/EXPERIMENTAL.md

@@ -0,0 +1,50 @@
+# 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)
+
+### 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
+```
+
+## 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.

sdk/metric/internal/aggregate/aggregate.go

@@ -44,6 +44,14 @@ type Builder[N int64 | float64] struct {
 	// Filter is the attribute filter the aggregate function will use on the
 	// input of measurements.
 	Filter attribute.Filter
+	// 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] {
@@ -63,7 +71,7 @@ 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)
 
 	return b.filter(lv.measure), func(dest *metricdata.Aggregation) int {
 		// Ignore if dest is not a metricdata.Gauge. The chance for memory
@@ -79,7 +87,7 @@ 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)
 	switch b.Temporality {
 	case metricdata.DeltaTemporality:
 		return b.filter(s.measure), s.delta
@@ -90,7 +98,7 @@ 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)
 	switch b.Temporality {
 	case metricdata.DeltaTemporality:
 		return b.filter(s.measure), s.delta
@@ -102,7 +110,7 @@ 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)
 	switch b.Temporality {
 	case metricdata.DeltaTemporality:
 		return b.filter(h.measure), h.delta
@@ -114,7 +122,7 @@ 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)
 	switch b.Temporality {
 	case metricdata.DeltaTemporality:
 		return b.filter(h.measure), h.delta

sdk/metric/internal/aggregate/aggregate_test.go

@@ -31,11 +31,15 @@ var (
 	keyUser    = "user"
 	userAlice  = attribute.String(keyUser, "Alice")
 	userBob    = attribute.String(keyUser, "Bob")
+	userCarol  = attribute.String(keyUser, "Carol")
+	userDave   = attribute.String(keyUser, "Dave")
 	adminTrue  = attribute.Bool("admin", true)
 	adminFalse = attribute.Bool("admin", false)
 
 	alice = attribute.NewSet(userAlice, adminTrue)
 	bob   = attribute.NewSet(userBob, adminFalse)
+	carol = attribute.NewSet(userCarol, adminFalse)
+	dave  = attribute.NewSet(userDave, adminFalse)
 
 	// Filtered.
 	attrFltr = func(kv attribute.KeyValue) bool {

sdk/metric/internal/aggregate/exponential_histogram.go

@@ -288,13 +288,14 @@ 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) *expoHistogram[N] {
 	return &expoHistogram[N]{
 		noSum:    noSum,
 		noMinMax: noMinMax,
 		maxSize:  int(maxSize),
 		maxScale: int(maxScale),
 
+		limit:  newLimiter[*expoHistogramDataPoint[N]](limit),
 		values: make(map[attribute.Set]*expoHistogramDataPoint[N]),
 
 		start: now(),
@@ -309,6 +310,7 @@ type expoHistogram[N int64 | float64] struct {
 	maxSize  int
 	maxScale int
 
+	limit    limiter[*expoHistogramDataPoint[N]]
 	values   map[attribute.Set]*expoHistogramDataPoint[N]
 	valuesMu sync.Mutex
 
@@ -324,6 +326,7 @@ func (e *expoHistogram[N]) measure(_ context.Context, value N, attr attribute.Se
 	e.valuesMu.Lock()
 	defer e.valuesMu.Unlock()
 
+	attr = e.limit.Attributes(attr, e.values)
 	v, ok := e.values[attr]
 	if !ok {
 		v = newExpoHistogramDataPoint[N](e.maxSize, e.maxScale, e.noMinMax, e.noSum)

sdk/metric/internal/aggregate/exponential_histogram_test.go

@@ -183,7 +183,7 @@ func testExpoHistogramMinMaxSumInt64(t *testing.T) {
 			restore := withHandler(t)
 			defer restore()
 
-			h := newExponentialHistogram[int64](4, 20, false, false)
+			h := newExponentialHistogram[int64](4, 20, false, false, 0)
 			for _, v := range tt.values {
 				h.measure(context.Background(), v, alice)
 			}
@@ -225,7 +225,7 @@ func testExpoHistogramMinMaxSumFloat64(t *testing.T) {
 			restore := withHandler(t)
 			defer restore()
 
-			h := newExponentialHistogram[float64](4, 20, false, false)
+			h := newExponentialHistogram[float64](4, 20, false, false, 0)
 			for _, v := range tt.values {
 				h.measure(context.Background(), v, alice)
 			}
@@ -747,8 +747,9 @@ func TestExponentialHistogramAggregation(t *testing.T) {
 
 func testDeltaExpoHist[N int64 | float64]() func(t *testing.T) {
 	in, out := Builder[N]{
-		Temporality: metricdata.DeltaTemporality,
-		Filter:      attrFltr,
+		Temporality:      metricdata.DeltaTemporality,
+		Filter:           attrFltr,
+		AggregationLimit: 2,
 	}.ExponentialBucketHistogram(4, 20, false, false)
 	ctx := context.Background()
 	return test[N](in, out, []teststep[N]{
@@ -805,13 +806,67 @@ func testDeltaExpoHist[N int64 | float64]() func(t *testing.T) {
 				},
 			},
 		},
+		{
+			input: []arg[N]{
+				{ctx, 4, alice},
+				{ctx, 4, alice},
+				{ctx, 4, alice},
+				{ctx, 2, alice},
+				{ctx, 16, alice},
+				{ctx, 1, alice},
+				// These will exceed the cardinality limit.
+				{ctx, 4, bob},
+				{ctx, 4, bob},
+				{ctx, 4, bob},
+				{ctx, 2, carol},
+				{ctx, 16, carol},
+				{ctx, 1, dave},
+			},
+			expect: output{
+				n: 2,
+				agg: metricdata.ExponentialHistogram[N]{
+					Temporality: metricdata.DeltaTemporality,
+					DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
+						{
+							Attributes: fltrAlice,
+							StartTime:  staticTime,
+							Time:       staticTime,
+							Count:      6,
+							Min:        metricdata.NewExtrema[N](1),
+							Max:        metricdata.NewExtrema[N](16),
+							Sum:        31,
+							Scale:      -1,
+							PositiveBucket: metricdata.ExponentialBucket{
+								Offset: -1,
+								Counts: []uint64{1, 4, 1},
+							},
+						},
+						{
+							Attributes: overflowSet,
+							StartTime:  staticTime,
+							Time:       staticTime,
+							Count:      6,
+							Min:        metricdata.NewExtrema[N](1),
+							Max:        metricdata.NewExtrema[N](16),
+							Sum:        31,
+							Scale:      -1,
+							PositiveBucket: metricdata.ExponentialBucket{
+								Offset: -1,
+								Counts: []uint64{1, 4, 1},
+							},
+						},
+					},
+				},
+			},
+		},
 	})
 }
 
 func testCumulativeExpoHist[N int64 | float64]() func(t *testing.T) {
 	in, out := Builder[N]{
-		Temporality: metricdata.CumulativeTemporality,
-		Filter:      attrFltr,
+		Temporality:      metricdata.CumulativeTemporality,
+		Filter:           attrFltr,
+		AggregationLimit: 2,
 	}.ExponentialBucketHistogram(4, 20, false, false)
 	ctx := context.Background()
 	return test[N](in, out, []teststep[N]{
@@ -911,6 +966,53 @@ func testCumulativeExpoHist[N int64 | float64]() func(t *testing.T) {
 				},
 			},
 		},
+		{
+			input: []arg[N]{
+				// These will exceed the cardinality limit.
+				{ctx, 4, bob},
+				{ctx, 4, bob},
+				{ctx, 4, bob},
+				{ctx, 2, carol},
+				{ctx, 16, carol},
+				{ctx, 1, dave},
+			},
+			expect: output{
+				n: 2,
+				agg: metricdata.ExponentialHistogram[N]{
+					Temporality: metricdata.CumulativeTemporality,
+					DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
+						{
+							Attributes: fltrAlice,
+							StartTime:  staticTime,
+							Time:       staticTime,
+							Count:      9,
+							Min:        metricdata.NewExtrema[N](1),
+							Max:        metricdata.NewExtrema[N](16),
+							Sum:        44,
+							Scale:      -1,
+							PositiveBucket: metricdata.ExponentialBucket{
+								Offset: -1,
+								Counts: []uint64{1, 6, 2},
+							},
+						},
+						{
+							Attributes: overflowSet,
+							StartTime:  staticTime,
+							Time:       staticTime,
+							Count:      6,
+							Min:        metricdata.NewExtrema[N](1),
+							Max:        metricdata.NewExtrema[N](16),
+							Sum:        31,
+							Scale:      -1,
+							PositiveBucket: metricdata.ExponentialBucket{
+								Offset: -1,
+								Counts: []uint64{1, 4, 1},
+							},
+						},
+					},
+				},
+			},
+		},
 	})
 }
 

sdk/metric/internal/aggregate/histogram.go

@@ -54,11 +54,12 @@ type histValues[N int64 | float64] struct {
 	noSum  bool
 	bounds []float64
 
+	limit    limiter[*buckets[N]]
 	values   map[attribute.Set]*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) *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
@@ -69,6 +70,7 @@ func newHistValues[N int64 | float64](bounds []float64, noSum bool) *histValues[
 	return &histValues[N]{
 		noSum:  noSum,
 		bounds: b,
+		limit:  newLimiter[*buckets[N]](limit),
 		values: make(map[attribute.Set]*buckets[N]),
 	}
 }
@@ -86,6 +88,7 @@ func (s *histValues[N]) measure(_ context.Context, value N, attr attribute.Set)
 	s.valuesMu.Lock()
 	defer s.valuesMu.Unlock()
 
+	attr = s.limit.Attributes(attr, s.values)
 	b, ok := s.values[attr]
 	if !ok {
 		// N+1 buckets. For example:
@@ -108,9 +111,9 @@ func (s *histValues[N]) measure(_ context.Context, value N, attr attribute.Set)
 
 // 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) *histogram[N] {
 	return &histogram[N]{
-		histValues: newHistValues[N](boundaries, noSum),
+		histValues: newHistValues[N](boundaries, noSum, limit),
 		noMinMax:   noMinMax,
 		start:      now(),
 	}