Merge pull request #14006 from aknuds1/arve/otlp-translator-direct
OTLP: Use PrometheusConverter directlypull/14010/head
Arve Knudsen
2 years ago
committed by
GitHub
commit
e491deba97
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// Copyright 2024 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// Provenance-includes-location: https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/95e8f8fdc2a9dc87230406c9a3cf02be4fd68bea/pkg/translator/prometheusremotewrite/metrics_to_prw_test.go
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// Provenance-includes-license: Apache-2.0
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// Provenance-includes-copyright: Copyright The OpenTelemetry Authors.
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package prometheusremotewrite |
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import ( |
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"fmt" |
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"testing" |
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"time" |
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"github.com/stretchr/testify/require" |
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"go.opentelemetry.io/collector/pdata/pcommon" |
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"go.opentelemetry.io/collector/pdata/pmetric" |
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"go.opentelemetry.io/collector/pdata/pmetric/pmetricotlp" |
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) |
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func BenchmarkPrometheusConverter_FromMetrics(b *testing.B) { |
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for _, resourceAttributeCount := range []int{0, 5, 50} { |
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b.Run(fmt.Sprintf("resource attribute count: %v", resourceAttributeCount), func(b *testing.B) { |
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for _, histogramCount := range []int{0, 1000} { |
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b.Run(fmt.Sprintf("histogram count: %v", histogramCount), func(b *testing.B) { |
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nonHistogramCounts := []int{0, 1000} |
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if resourceAttributeCount == 0 && histogramCount == 0 { |
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// Don't bother running a scenario where we'll generate no series.
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nonHistogramCounts = []int{1000} |
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} |
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for _, nonHistogramCount := range nonHistogramCounts { |
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b.Run(fmt.Sprintf("non-histogram count: %v", nonHistogramCount), func(b *testing.B) { |
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for _, labelsPerMetric := range []int{2, 20} { |
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b.Run(fmt.Sprintf("labels per metric: %v", labelsPerMetric), func(b *testing.B) { |
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for _, exemplarsPerSeries := range []int{0, 5, 10} { |
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b.Run(fmt.Sprintf("exemplars per series: %v", exemplarsPerSeries), func(b *testing.B) { |
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payload := createExportRequest(resourceAttributeCount, histogramCount, nonHistogramCount, labelsPerMetric, exemplarsPerSeries) |
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for i := 0; i < b.N; i++ { |
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converter := NewPrometheusConverter() |
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require.NoError(b, converter.FromMetrics(payload.Metrics(), Settings{})) |
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require.NotNil(b, converter.TimeSeries()) |
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} |
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}) |
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} |
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}) |
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} |
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}) |
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} |
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}) |
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} |
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}) |
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} |
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} |
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func createExportRequest(resourceAttributeCount int, histogramCount int, nonHistogramCount int, labelsPerMetric int, exemplarsPerSeries int) pmetricotlp.ExportRequest { |
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request := pmetricotlp.NewExportRequest() |
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rm := request.Metrics().ResourceMetrics().AppendEmpty() |
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generateAttributes(rm.Resource().Attributes(), "resource", resourceAttributeCount) |
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metrics := rm.ScopeMetrics().AppendEmpty().Metrics() |
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ts := pcommon.NewTimestampFromTime(time.Now()) |
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for i := 1; i <= histogramCount; i++ { |
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m := metrics.AppendEmpty() |
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m.SetEmptyHistogram() |
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m.SetName(fmt.Sprintf("histogram-%v", i)) |
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m.Histogram().SetAggregationTemporality(pmetric.AggregationTemporalityCumulative) |
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h := m.Histogram().DataPoints().AppendEmpty() |
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h.SetTimestamp(ts) |
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// Set 50 samples, 10 each with values 0.5, 1, 2, 4, and 8
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h.SetCount(50) |
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h.SetSum(155) |
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h.BucketCounts().FromRaw([]uint64{10, 10, 10, 10, 10, 0}) |
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h.ExplicitBounds().FromRaw([]float64{.5, 1, 2, 4, 8, 16}) // Bucket boundaries include the upper limit (ie. each sample is on the upper limit of its bucket)
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generateAttributes(h.Attributes(), "series", labelsPerMetric) |
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generateExemplars(h.Exemplars(), exemplarsPerSeries, ts) |
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} |
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for i := 1; i <= nonHistogramCount; i++ { |
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m := metrics.AppendEmpty() |
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m.SetEmptySum() |
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m.SetName(fmt.Sprintf("sum-%v", i)) |
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m.Sum().SetAggregationTemporality(pmetric.AggregationTemporalityCumulative) |
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point := m.Sum().DataPoints().AppendEmpty() |
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point.SetTimestamp(ts) |
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point.SetDoubleValue(1.23) |
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generateAttributes(point.Attributes(), "series", labelsPerMetric) |
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generateExemplars(point.Exemplars(), exemplarsPerSeries, ts) |
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} |
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for i := 1; i <= nonHistogramCount; i++ { |
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m := metrics.AppendEmpty() |
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m.SetEmptyGauge() |
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m.SetName(fmt.Sprintf("gauge-%v", i)) |
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point := m.Gauge().DataPoints().AppendEmpty() |
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point.SetTimestamp(ts) |
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point.SetDoubleValue(1.23) |
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generateAttributes(point.Attributes(), "series", labelsPerMetric) |
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generateExemplars(point.Exemplars(), exemplarsPerSeries, ts) |
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} |
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return request |
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} |
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func generateAttributes(m pcommon.Map, prefix string, count int) { |
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for i := 1; i <= count; i++ { |
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m.PutStr(fmt.Sprintf("%v-name-%v", prefix, i), fmt.Sprintf("value-%v", i)) |
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} |
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} |
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func generateExemplars(exemplars pmetric.ExemplarSlice, count int, ts pcommon.Timestamp) { |
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for i := 1; i <= count; i++ { |
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e := exemplars.AppendEmpty() |
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e.SetTimestamp(ts) |
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e.SetDoubleValue(2.22) |
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e.SetSpanID(pcommon.SpanID{0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08}) |
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e.SetTraceID(pcommon.TraceID{0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f}) |
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} |
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} |
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