Add delta/cumulative histogram implementation

sdk/metric/internal/histogram.go

@@ -18,19 +18,94 @@
 package internal // import "go.opentelemetry.io/otel/sdk/metric/internal"
 
 import (
+	"sort"
+	"sync"
+	"time"
+
 	"go.opentelemetry.io/otel/attribute"
 	"go.opentelemetry.io/otel/sdk/metric/aggregation"
 	"go.opentelemetry.io/otel/sdk/metric/metricdata"
 )
 
-// histogram summarizes a set of measurements as an histogram with
+type buckets struct {
+	counts   []uint64
+	count    uint64
+	sum      float64
+	min, max float64
+}
+
+// newBuckets returns buckets with n bins.
+func newBuckets(n int) *buckets {
+	return &buckets{counts: make([]uint64, n)}
+}
+
+func (b *buckets) bin(idx int, value float64) {
+	b.counts[idx]++
+	b.count++
+	b.sum += value
+	if value < b.min {
+		b.min = value
+	} else if value > b.max {
+		b.max = value
+	}
+}
+
+// histValues summarizes a set of measurements as an histValues with
 // explicitly defined buckets.
-type histogram[N int64 | float64] struct {
-	// TODO(#2970): implement.
+type histValues[N int64 | float64] struct {
+	bounds []float64
+
+	values   map[attribute.Set]*buckets
+	valuesMu sync.Mutex
 }
 
-func (s *histogram[N]) Aggregate(value N, attr attribute.Set) {
-	// TODO(#2970): implement.
+func newHistValues[N int64 | float64](bounds []float64) *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)
+	return &histValues[N]{
+		bounds: b,
+		values: make(map[attribute.Set]*buckets),
+	}
+}
+
+// Aggregate records the measurement value, scoped by attr, and aggregates it
+// into a histogram.
+func (s *histValues[N]) Aggregate(value N, attr attribute.Set) {
+	// Accept all types to satisfy the Aggregator interface. However, since
+	// the Aggregation produced by this Aggregator is only float64, convert
+	// here to only use this type.
+	v := float64(value)
+
+	// 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
+	// is len(s.bounds)+1, with the last bucket representing:
+	// (s.bounds[len(s.bounds)-1], +∞).
+	idx := sort.SearchFloat64s(s.bounds, v)
+
+	s.valuesMu.Lock()
+	defer s.valuesMu.Unlock()
+
+	b, ok := s.values[attr]
+	if !ok {
+		// N+1 buckets. For example:
+		//
+		//   bounds = [0, 5, 10]
+		//
+		// Then,
+		//
+		//   buckets = (-∞, 0], (0, 5.0], (5.0, 10.0], (10.0, +∞)
+		b = newBuckets(len(s.bounds) + 1)
+		// Ensure min and max are recorded values (not zero), for new buckets.
+		b.min, b.max = v, v
+		s.values[attr] = b
+	}
+	b.bin(idx, v)
 }
 
 // NewDeltaHistogram returns an Aggregator that summarizes a set of
@@ -41,20 +116,59 @@ func (s *histogram[N]) Aggregate(value N, attr attribute.Set) {
 // Aggregator's Aggregations method is called it will reset all histogram
 // counts to zero.
 func NewDeltaHistogram[N int64 | float64](cfg aggregation.ExplicitBucketHistogram) Aggregator[N] {
-	return &deltaHistogram[N]{}
+	return &deltaHistogram[N]{
+		histValues: newHistValues[N](cfg.Boundaries),
+		noMinMax:   cfg.NoMinMax,
+		start:      now(),
+	}
 }
 
 // deltaHistogram summarizes a set of measurements made in a single
 // aggregation cycle as an histogram with explicitly defined buckets.
 type deltaHistogram[N int64 | float64] struct {
-	histogram[N]
+	*histValues[N]
 
-	// TODO(#2970): implement.
+	noMinMax bool
+	start    time.Time
 }
 
 func (s *deltaHistogram[N]) Aggregation() metricdata.Aggregation {
-	// TODO(#2970): implement.
-	return nil
+	h := metricdata.Histogram{Temporality: metricdata.DeltaTemporality}
+
+	s.valuesMu.Lock()
+	defer s.valuesMu.Unlock()
+
+	if len(s.values) == 0 {
+		return h
+	}
+
+	// Do not allow modification of our copy of bounds.
+	bounds := make([]float64, len(s.bounds))
+	copy(bounds, s.bounds)
+	t := now()
+	h.DataPoints = make([]metricdata.HistogramDataPoint, 0, len(s.values))
+	for a, b := range s.values {
+		hdp := metricdata.HistogramDataPoint{
+			Attributes:   a,
+			StartTime:    s.start,
+			Time:         t,
+			Count:        b.count,
+			Bounds:       bounds,
+			BucketCounts: b.counts,
+			Sum:          b.sum,
+		}
+		if !s.noMinMax {
+			hdp.Min = &b.min
+			hdp.Max = &b.max
+		}
+		h.DataPoints = append(h.DataPoints, hdp)
+
+		// Unused attribute sets do not report.
+		delete(s.values, a)
+	}
+	// The delta collection cycle resets.
+	s.start = t
+	return h
 }
 
 // NewCumulativeHistogram returns an Aggregator that summarizes a set of
@@ -64,18 +178,66 @@ func (s *deltaHistogram[N]) Aggregation() metricdata.Aggregation {
 // the bucketed counts of all values aggregated since the returned Aggregator
 // was created.
 func NewCumulativeHistogram[N int64 | float64](cfg aggregation.ExplicitBucketHistogram) Aggregator[N] {
-	return &cumulativeHistogram[N]{}
+	return &cumulativeHistogram[N]{
+		histValues: newHistValues[N](cfg.Boundaries),
+		noMinMax:   cfg.NoMinMax,
+		start:      now(),
+	}
 }
 
 // cumulativeHistogram summarizes a set of measurements made over all
 // aggregation cycles as an histogram with explicitly defined buckets.
 type cumulativeHistogram[N int64 | float64] struct {
-	histogram[N]
+	*histValues[N]
 
-	// TODO(#2970): implement.
+	noMinMax bool
+	start    time.Time
 }
 
 func (s *cumulativeHistogram[N]) Aggregation() metricdata.Aggregation {
-	// TODO(#2970): implement.
-	return nil
+	h := metricdata.Histogram{Temporality: metricdata.CumulativeTemporality}
+
+	s.valuesMu.Lock()
+	defer s.valuesMu.Unlock()
+
+	if len(s.values) == 0 {
+		return h
+	}
+
+	// Do not allow modification of our copy of bounds.
+	bounds := make([]float64, len(s.bounds))
+	copy(bounds, s.bounds)
+	t := now()
+	h.DataPoints = make([]metricdata.HistogramDataPoint, 0, len(s.values))
+	for a, b := range s.values {
+		// 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)
+
+		hdp := metricdata.HistogramDataPoint{
+			Attributes:   a,
+			StartTime:    s.start,
+			Time:         t,
+			Count:        b.count,
+			Bounds:       bounds,
+			BucketCounts: counts,
+			Sum:          b.sum,
+		}
+		if !s.noMinMax {
+			// Similar to counts, make a copy.
+			min, max := b.min, b.max
+			hdp.Min = &min
+			hdp.Max = &max
+		}
+		h.DataPoints = append(h.DataPoints, hdp)
+		// 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.
+	}
+	return h
 }