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Diffstat (limited to 'vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go')
-rw-r--r-- | vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go | 231 |
1 files changed, 231 insertions, 0 deletions
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go new file mode 100644 index 000000000..62ec51e1f --- /dev/null +++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go @@ -0,0 +1,231 @@ +// Copyright The OpenTelemetry Authors +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate" + +import ( + "context" + "sort" + "sync" + "time" + + "go.opentelemetry.io/otel/attribute" + "go.opentelemetry.io/otel/sdk/metric/metricdata" +) + +type buckets[N int64 | float64] struct { + counts []uint64 + count uint64 + total N + min, max N +} + +// newBuckets returns buckets with n bins. +func newBuckets[N int64 | float64](n int) *buckets[N] { + return &buckets[N]{counts: make([]uint64, n)} +} + +func (b *buckets[N]) sum(value N) { b.total += value } + +func (b *buckets[N]) bin(idx int, value N) { + b.counts[idx]++ + b.count++ + 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 histValues[N int64 | float64] struct { + noSum bool + bounds []float64 + + values map[attribute.Set]*buckets[N] + valuesMu sync.Mutex +} + +func newHistValues[N int64 | float64](bounds []float64, noSum bool) *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]{ + noSum: noSum, + bounds: b, + values: make(map[attribute.Set]*buckets[N]), + } +} + +// Aggregate records the measurement value, scoped by attr, and aggregates it +// into a histogram. +func (s *histValues[N]) measure(_ context.Context, value N, attr attribute.Set) { + // 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, float64(value)) + + 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[N](len(s.bounds) + 1) + // Ensure min and max are recorded values (not zero), for new buckets. + b.min, b.max = value, value + s.values[attr] = b + } + b.bin(idx, value) + if !s.noSum { + b.sum(value) + } +} + +// 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] { + return &histogram[N]{ + histValues: newHistValues[N](boundaries, noSum), + noMinMax: noMinMax, + start: now(), + } +} + +// histogram summarizes a set of measurements as an histogram with explicitly +// defined buckets. +type histogram[N int64 | float64] struct { + *histValues[N] + + noMinMax bool + start time.Time +} + +func (s *histogram[N]) delta(dest *metricdata.Aggregation) int { + t := now() + + // If *dest is not a metricdata.Histogram, memory reuse is missed. In that + // case, use the zero-value h and hope for better alignment next cycle. + h, _ := (*dest).(metricdata.Histogram[N]) + h.Temporality = metricdata.DeltaTemporality + + s.valuesMu.Lock() + defer s.valuesMu.Unlock() + + // Do not allow modification of our copy of bounds. + bounds := make([]float64, len(s.bounds)) + copy(bounds, s.bounds) + + n := len(s.values) + hDPts := reset(h.DataPoints, n, n) + + var i int + for a, b := range s.values { + hDPts[i].Attributes = a + hDPts[i].StartTime = s.start + hDPts[i].Time = t + hDPts[i].Count = b.count + hDPts[i].Bounds = bounds + hDPts[i].BucketCounts = b.counts + + if !s.noSum { + hDPts[i].Sum = b.total + } + + if !s.noMinMax { + hDPts[i].Min = metricdata.NewExtrema(b.min) + hDPts[i].Max = metricdata.NewExtrema(b.max) + } + + // Unused attribute sets do not report. + delete(s.values, a) + i++ + } + // The delta collection cycle resets. + s.start = t + + h.DataPoints = hDPts + *dest = h + + return n +} + +func (s *histogram[N]) cumulative(dest *metricdata.Aggregation) int { + t := now() + + // If *dest is not a metricdata.Histogram, memory reuse is missed. In that + // case, use the zero-value h and hope for better alignment next cycle. + h, _ := (*dest).(metricdata.Histogram[N]) + h.Temporality = metricdata.CumulativeTemporality + + s.valuesMu.Lock() + defer s.valuesMu.Unlock() + + // Do not allow modification of our copy of bounds. + bounds := make([]float64, len(s.bounds)) + copy(bounds, s.bounds) + + n := len(s.values) + hDPts := reset(h.DataPoints, n, n) + + var i int + 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) + + hDPts[i].Attributes = a + hDPts[i].StartTime = s.start + hDPts[i].Time = t + hDPts[i].Count = b.count + hDPts[i].Bounds = bounds + hDPts[i].BucketCounts = counts + + if !s.noSum { + hDPts[i].Sum = b.total + } + + if !s.noMinMax { + hDPts[i].Min = metricdata.NewExtrema(b.min) + hDPts[i].Max = metricdata.NewExtrema(b.max) + } + i++ + // 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. + } + + h.DataPoints = hDPts + *dest = h + + return n +} |