diff options
Diffstat (limited to 'vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate')
6 files changed, 77 insertions, 15 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 fde219333..0321da681 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 @@ -121,7 +121,10 @@ 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) { +func (b Builder[N]) ExplicitBucketHistogram( + boundaries []float64, + noMinMax, noSum bool, +) (Measure[N], ComputeAggregation) { h := newHistogram[N](boundaries, noMinMax, noSum, b.AggregationLimit, b.resFunc()) switch b.Temporality { case metricdata.DeltaTemporality: @@ -133,7 +136,10 @@ 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) { +func (b Builder[N]) ExponentialBucketHistogram( + maxSize, maxScale int32, + noMinMax, noSum bool, +) (Measure[N], ComputeAggregation) { h := newExponentialHistogram[N](maxSize, maxScale, noMinMax, noSum, b.AggregationLimit, b.resFunc()) switch b.Temporality { case metricdata.DeltaTemporality: diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go index 32a62e1b8..ae1f59344 100644 --- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go +++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go @@ -48,7 +48,12 @@ type expoHistogramDataPoint[N int64 | float64] struct { zeroCount uint64 } -func newExpoHistogramDataPoint[N int64 | float64](attrs attribute.Set, maxSize int, maxScale int32, noMinMax, noSum bool) *expoHistogramDataPoint[N] { // nolint:revive // we need this control flag +func newExpoHistogramDataPoint[N int64 | float64]( + attrs attribute.Set, + maxSize int, + maxScale int32, + noMinMax, noSum bool, +) *expoHistogramDataPoint[N] { // nolint:revive // we need this control flag f := math.MaxFloat64 ma := N(f) // if N is int64, max will overflow to -9223372036854775808 mi := N(-f) @@ -283,7 +288,12 @@ func (b *expoBuckets) downscale(delta int32) { // 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(attribute.Set) FilteredExemplarReservoir[N]) *expoHistogram[N] { +func newExponentialHistogram[N int64 | float64]( + maxSize, maxScale int32, + noMinMax, noSum bool, + limit int, + r func(attribute.Set) FilteredExemplarReservoir[N], +) *expoHistogram[N] { return &expoHistogram[N]{ noSum: noSum, noMinMax: noMinMax, @@ -314,7 +324,12 @@ type expoHistogram[N int64 | float64] struct { start time.Time } -func (e *expoHistogram[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) { +func (e *expoHistogram[N]) measure( + ctx context.Context, + value N, + fltrAttr attribute.Set, + droppedAttr []attribute.KeyValue, +) { // Ignore NaN and infinity. if math.IsInf(float64(value), 0) || math.IsNaN(float64(value)) { return @@ -360,11 +375,19 @@ func (e *expoHistogram[N]) delta(dest *metricdata.Aggregation) int { hDPts[i].ZeroThreshold = 0.0 hDPts[i].PositiveBucket.Offset = val.posBuckets.startBin - hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(val.posBuckets.counts), len(val.posBuckets.counts)) + hDPts[i].PositiveBucket.Counts = reset( + hDPts[i].PositiveBucket.Counts, + len(val.posBuckets.counts), + len(val.posBuckets.counts), + ) copy(hDPts[i].PositiveBucket.Counts, val.posBuckets.counts) hDPts[i].NegativeBucket.Offset = val.negBuckets.startBin - hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(val.negBuckets.counts), len(val.negBuckets.counts)) + hDPts[i].NegativeBucket.Counts = reset( + hDPts[i].NegativeBucket.Counts, + len(val.negBuckets.counts), + len(val.negBuckets.counts), + ) copy(hDPts[i].NegativeBucket.Counts, val.negBuckets.counts) if !e.noSum { @@ -413,11 +436,19 @@ func (e *expoHistogram[N]) cumulative(dest *metricdata.Aggregation) int { hDPts[i].ZeroThreshold = 0.0 hDPts[i].PositiveBucket.Offset = val.posBuckets.startBin - hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(val.posBuckets.counts), len(val.posBuckets.counts)) + hDPts[i].PositiveBucket.Counts = reset( + hDPts[i].PositiveBucket.Counts, + len(val.posBuckets.counts), + len(val.posBuckets.counts), + ) copy(hDPts[i].PositiveBucket.Counts, val.posBuckets.counts) hDPts[i].NegativeBucket.Offset = val.negBuckets.startBin - hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(val.negBuckets.counts), len(val.negBuckets.counts)) + hDPts[i].NegativeBucket.Counts = reset( + hDPts[i].NegativeBucket.Counts, + len(val.negBuckets.counts), + len(val.negBuckets.counts), + ) copy(hDPts[i].NegativeBucket.Counts, val.negBuckets.counts) if !e.noSum { diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/filtered_reservoir.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/filtered_reservoir.go index 691a91060..d4c41642d 100644 --- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/filtered_reservoir.go +++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/filtered_reservoir.go @@ -33,7 +33,10 @@ type filteredExemplarReservoir[N int64 | float64] struct { // NewFilteredExemplarReservoir creates a [FilteredExemplarReservoir] which only offers values // that are allowed by the filter. -func NewFilteredExemplarReservoir[N int64 | float64](f exemplar.Filter, r exemplar.Reservoir) FilteredExemplarReservoir[N] { +func NewFilteredExemplarReservoir[N int64 | float64]( + f exemplar.Filter, + r exemplar.Reservoir, +) FilteredExemplarReservoir[N] { return &filteredExemplarReservoir[N]{ filter: f, reservoir: r, 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 index d577ae2c1..d3068484c 100644 --- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go +++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go @@ -53,7 +53,12 @@ type histValues[N int64 | float64] struct { valuesMu sync.Mutex } -func newHistValues[N int64 | float64](bounds []float64, noSum bool, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *histValues[N] { +func newHistValues[N int64 | float64]( + bounds []float64, + noSum bool, + limit int, + r func(attribute.Set) FilteredExemplarReservoir[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 @@ -71,7 +76,12 @@ func newHistValues[N int64 | float64](bounds []float64, noSum bool, limit int, r // Aggregate records the measurement value, scoped by attr, and aggregates it // into a histogram. -func (s *histValues[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) { +func (s *histValues[N]) measure( + ctx context.Context, + value N, + fltrAttr attribute.Set, + droppedAttr []attribute.KeyValue, +) { // 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 @@ -108,7 +118,12 @@ func (s *histValues[N]) measure(ctx context.Context, value N, fltrAttr attribute // 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(attribute.Set) FilteredExemplarReservoir[N]) *histogram[N] { +func newHistogram[N int64 | float64]( + boundaries []float64, + noMinMax, noSum bool, + limit int, + r func(attribute.Set) FilteredExemplarReservoir[N], +) *histogram[N] { return &histogram[N]{ histValues: newHistValues[N](boundaries, noSum, limit, r), noMinMax: noMinMax, diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go index d3a93f085..350ccebdc 100644 --- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go +++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go @@ -114,7 +114,10 @@ func (s *lastValue[N]) copyDpts(dest *[]metricdata.DataPoint[N], t time.Time) in // newPrecomputedLastValue returns an aggregator that summarizes a set of // observations as the last one made. -func newPrecomputedLastValue[N int64 | float64](limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *precomputedLastValue[N] { +func newPrecomputedLastValue[N int64 | float64]( + limit int, + r func(attribute.Set) FilteredExemplarReservoir[N], +) *precomputedLastValue[N] { return &precomputedLastValue[N]{lastValue: newLastValue[N](limit, r)} } diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go index 8e132ad61..612cde432 100644 --- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go +++ b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go @@ -143,7 +143,11 @@ func (s *sum[N]) cumulative(dest *metricdata.Aggregation) int { // newPrecomputedSum returns an aggregator that summarizes a set of // observations as their arithmetic sum. Each sum is scoped by attributes and // the aggregation cycle the measurements were made in. -func newPrecomputedSum[N int64 | float64](monotonic bool, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *precomputedSum[N] { +func newPrecomputedSum[N int64 | float64]( + monotonic bool, + limit int, + r func(attribute.Set) FilteredExemplarReservoir[N], +) *precomputedSum[N] { return &precomputedSum[N]{ valueMap: newValueMap[N](limit, r), monotonic: monotonic, |