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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,112 @@ | ||
| // Copyright The OpenTelemetry Authors | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
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| package runtime // import "go.opentelemetry.io/contrib/instrumentation/runtime" | ||
|
|
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| import ( | ||
| "context" | ||
| "fmt" | ||
| "math" | ||
| "runtime/metrics" | ||
| "time" | ||
|
|
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| "go.opentelemetry.io/otel/attribute" | ||
| "go.opentelemetry.io/otel/sdk/instrumentation" | ||
| "go.opentelemetry.io/otel/sdk/metric" | ||
| "go.opentelemetry.io/otel/sdk/metric/metricdata" | ||
| ) | ||
|
|
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| var startTime time.Time | ||
|
|
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| func init() { | ||
| startTime = time.Now() | ||
| } | ||
|
|
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| var histogramMetrics = []string{goSchedLatencies} | ||
|
|
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| type Producer struct { | ||
| collector *goCollector | ||
| } | ||
|
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| var _ metric.Producer = (*Producer)(nil) | ||
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| func NewProducer(opts ...ProducerOption) *Producer { | ||
| c := newProducerConfig(opts...) | ||
| return &Producer{ | ||
| collector: newCollector(c.MinimumReadMemStatsInterval, histogramMetrics), | ||
| } | ||
| } | ||
|
|
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| func (p *Producer) Produce(context.Context) ([]metricdata.ScopeMetrics, error) { | ||
| p.collector.refresh() | ||
| // Use the last collection time (which may or may not be now) for the timestamp. | ||
| histDp := convertRuntimeHistogram(p.collector.getHistogram(goSchedLatencies), p.collector.lastCollect) | ||
| if len(histDp) == 0 { | ||
| return nil, fmt.Errorf("unable to obtain go.schedule.duration metric from the runtime") | ||
| } | ||
| return []metricdata.ScopeMetrics{ | ||
| { | ||
| Scope: instrumentation.Scope{ | ||
| Name: ScopeName, | ||
| Version: Version(), | ||
| }, | ||
| Metrics: []metricdata.Metrics{ | ||
| { | ||
| Name: "go.schedule.duration", | ||
| Description: "The time goroutines have spent in the scheduler in a runnable state before actually running.", | ||
| Unit: "s", | ||
| Data: metricdata.Histogram[float64]{ | ||
| Temporality: metricdata.CumulativeTemporality, | ||
| DataPoints: histDp, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, nil | ||
| } | ||
|
|
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| var emptySet = attribute.EmptySet() | ||
|
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| func convertRuntimeHistogram(runtimeHist *metrics.Float64Histogram, ts time.Time) []metricdata.HistogramDataPoint[float64] { | ||
| if runtimeHist == nil { | ||
| return nil | ||
| } | ||
| bounds := runtimeHist.Buckets | ||
| counts := runtimeHist.Counts | ||
| if len(bounds) < 2 { | ||
| // runtime histograms are guaranteed to have at least two bucket boundaries. | ||
| return nil | ||
| } | ||
| // trim the first bucket since it is a lower bound. OTel histogram boundaries only have an upper bound. | ||
| bounds = bounds[1:] | ||
| if bounds[len(bounds)-1] == math.Inf(1) { | ||
| // trim the last bucket if it is +Inf, since the +Inf boundary is implicit in OTel. | ||
| bounds = bounds[:len(bounds)-1] | ||
| } else { | ||
| // if the last bucket is not +Inf, append an extra zero count since | ||
| // the implicit +Inf bucket won't have any observations. | ||
| counts = append(counts, 0) | ||
| } | ||
| count := uint64(0) | ||
| sum := float64(0) | ||
| for i, c := range counts { | ||
| count += c | ||
| // This computed sum is an underestimate, since it assumes each | ||
| // observation happens at the bucket's lower bound. | ||
| if i > 0 && count != 0 { | ||
| sum += bounds[i-1] * float64(count) | ||
| } | ||
| } | ||
|
|
||
| return []metricdata.HistogramDataPoint[float64]{ | ||
| { | ||
| StartTime: startTime, | ||
| Count: count, | ||
| Sum: sum, | ||
| Time: ts, | ||
| Bounds: bounds, | ||
| BucketCounts: counts, | ||
| Attributes: *emptySet, | ||
| }, | ||
| } | ||
| } |
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