mirror of
https://github.com/open-telemetry/opentelemetry-go.git
synced 2024-12-10 09:50:58 +02:00
Refactor exponential histogram tests to use existing fixtures (#4747)
* Refactor expo hist test to use existing fixtures The tests for the exponential histogram create their own testing fixtures. There is nothing these new fixtures do that cannot already be done with the existing testing fixtures used by all the other aggregate functions. Unify the exponential histogram testing to use the existing fixtures. * Add alt input for cumulative test
This commit is contained in:
Tyler Yahn
GitHub
parent
b5afa704f1
commit
215eae31c7
@ -23,10 +23,8 @@ import (
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"github.com/stretchr/testify/assert"
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"github.com/stretchr/testify/require"
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"go.opentelemetry.io/otel/attribute"
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"go.opentelemetry.io/otel/internal/global"
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"go.opentelemetry.io/otel/sdk/metric/metricdata"
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"go.opentelemetry.io/otel/sdk/metric/metricdata/metricdatatest"
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)
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type noErrorHandler struct{ t *testing.T }
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@ -739,161 +737,181 @@ func TestSubNormal(t *testing.T) {
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}
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func TestExponentialHistogramAggregation(t *testing.T) {
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t.Run("Int64", testExponentialHistogramAggregation[int64])
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t.Run("Float64", testExponentialHistogramAggregation[float64])
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t.Cleanup(mockTime(now))
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t.Run("Int64/Delta", testDeltaExpoHist[int64]())
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t.Run("Float64/Delta", testDeltaExpoHist[float64]())
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t.Run("Int64/Cumulative", testCumulativeExpoHist[int64]())
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t.Run("Float64/Cumulative", testCumulativeExpoHist[float64]())
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}
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func testExponentialHistogramAggregation[N int64 | float64](t *testing.T) {
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const (
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maxSize = 4
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maxScale = 20
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noMinMax = false
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noSum = false
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)
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tests := []struct {
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name string
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build func() (Measure[N], ComputeAggregation)
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input [][]N
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want metricdata.ExponentialHistogram[N]
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wantCount int
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}{
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func testDeltaExpoHist[N int64 | float64]() func(t *testing.T) {
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in, out := Builder[N]{
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Temporality: metricdata.DeltaTemporality,
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Filter: attrFltr,
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}.ExponentialBucketHistogram(4, 20, false, false)
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ctx := context.Background()
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return test[N](in, out, []teststep[N]{
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{
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name: "Delta Single",
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build: func() (Measure[N], ComputeAggregation) {
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return Builder[N]{
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input: []arg[N]{},
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expect: output{
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n: 0,
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agg: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.DeltaTemporality,
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}.ExponentialBucketHistogram(maxSize, maxScale, noMinMax, noSum)
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},
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input: [][]N{
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{4, 4, 4, 2, 16, 1},
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},
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want: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.DeltaTemporality,
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
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{
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Count: 6,
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Min: metricdata.NewExtrema[N](1),
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Max: metricdata.NewExtrema[N](16),
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Sum: 31,
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Scale: -1,
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PositiveBucket: metricdata.ExponentialBucket{
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Offset: -1,
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Counts: []uint64{1, 4, 1},
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},
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},
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{},
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},
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},
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wantCount: 1,
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},
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{
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name: "Cumulative Single",
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build: func() (Measure[N], ComputeAggregation) {
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return Builder[N]{
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Temporality: metricdata.CumulativeTemporality,
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}.ExponentialBucketHistogram(maxSize, maxScale, noMinMax, noSum)
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input: []arg[N]{
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{ctx, 4, alice},
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{ctx, 4, alice},
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{ctx, 4, alice},
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{ctx, 2, alice},
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{ctx, 16, alice},
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{ctx, 1, alice},
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},
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input: [][]N{
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{4, 4, 4, 2, 16, 1},
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},
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want: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.CumulativeTemporality,
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
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{
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Count: 6,
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Min: metricdata.NewExtrema[N](1),
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Max: metricdata.NewExtrema[N](16),
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Sum: 31,
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Scale: -1,
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PositiveBucket: metricdata.ExponentialBucket{
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Offset: -1,
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Counts: []uint64{1, 4, 1},
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},
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},
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},
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},
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wantCount: 1,
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},
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{
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name: "Delta Multiple",
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build: func() (Measure[N], ComputeAggregation) {
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return Builder[N]{
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expect: output{
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n: 1,
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agg: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.DeltaTemporality,
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}.ExponentialBucketHistogram(maxSize, maxScale, noMinMax, noSum)
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},
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input: [][]N{
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{2, 3, 8},
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{4, 4, 4, 2, 16, 1},
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},
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want: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.DeltaTemporality,
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
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{
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Count: 6,
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Min: metricdata.NewExtrema[N](1),
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Max: metricdata.NewExtrema[N](16),
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Sum: 31,
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Scale: -1,
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PositiveBucket: metricdata.ExponentialBucket{
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Offset: -1,
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Counts: []uint64{1, 4, 1},
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
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{
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Attributes: fltrAlice,
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StartTime: staticTime,
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Time: staticTime,
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Count: 6,
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Min: metricdata.NewExtrema[N](1),
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Max: metricdata.NewExtrema[N](16),
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Sum: 31,
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Scale: -1,
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PositiveBucket: metricdata.ExponentialBucket{
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Offset: -1,
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Counts: []uint64{1, 4, 1},
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},
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},
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},
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},
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},
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wantCount: 1,
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},
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{
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name: "Cumulative Multiple ",
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build: func() (Measure[N], ComputeAggregation) {
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return Builder[N]{
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// Delta sums are expected to reset.
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input: []arg[N]{},
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expect: output{
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n: 0,
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agg: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.DeltaTemporality,
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{},
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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 testCumulativeExpoHist[N int64 | float64]() func(t *testing.T) {
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in, out := Builder[N]{
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Temporality: metricdata.CumulativeTemporality,
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Filter: attrFltr,
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}.ExponentialBucketHistogram(4, 20, false, false)
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ctx := context.Background()
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return test[N](in, out, []teststep[N]{
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{
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input: []arg[N]{},
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expect: output{
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n: 0,
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agg: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.CumulativeTemporality,
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}.ExponentialBucketHistogram(maxSize, maxScale, noMinMax, noSum)
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{},
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},
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},
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input: [][]N{
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{2, 3, 8},
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{4, 4, 4, 2, 16, 1},
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},
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{
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input: []arg[N]{
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{ctx, 4, alice},
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{ctx, 4, alice},
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{ctx, 4, alice},
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{ctx, 2, alice},
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{ctx, 16, alice},
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{ctx, 1, alice},
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},
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want: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.CumulativeTemporality,
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
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{
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Count: 9,
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Min: metricdata.NewExtrema[N](1),
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Max: metricdata.NewExtrema[N](16),
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Sum: 44,
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Scale: -1,
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PositiveBucket: metricdata.ExponentialBucket{
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Offset: -1,
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Counts: []uint64{1, 6, 2},
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expect: output{
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n: 1,
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agg: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.CumulativeTemporality,
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
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{
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Attributes: fltrAlice,
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StartTime: staticTime,
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Time: staticTime,
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Count: 6,
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Min: metricdata.NewExtrema[N](1),
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Max: metricdata.NewExtrema[N](16),
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Sum: 31,
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Scale: -1,
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PositiveBucket: metricdata.ExponentialBucket{
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Offset: -1,
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Counts: []uint64{1, 4, 1},
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},
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},
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},
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},
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},
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wantCount: 1,
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},
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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restore := withHandler(t)
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defer restore()
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in, out := tt.build()
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ctx := context.Background()
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var got metricdata.Aggregation
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var count int
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for _, n := range tt.input {
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for _, v := range n {
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in(ctx, v, *attribute.EmptySet())
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}
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count = out(&got)
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}
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metricdatatest.AssertAggregationsEqual(t, tt.want, got, metricdatatest.IgnoreTimestamp())
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assert.Equal(t, tt.wantCount, count)
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})
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}
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{
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input: []arg[N]{
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{ctx, 2, alice},
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{ctx, 3, alice},
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{ctx, 8, alice},
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},
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expect: output{
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n: 1,
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agg: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.CumulativeTemporality,
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
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{
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Attributes: fltrAlice,
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StartTime: staticTime,
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Time: staticTime,
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Count: 9,
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Min: metricdata.NewExtrema[N](1),
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Max: metricdata.NewExtrema[N](16),
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Sum: 44,
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Scale: -1,
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PositiveBucket: metricdata.ExponentialBucket{
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Offset: -1,
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Counts: []uint64{1, 6, 2},
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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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input: []arg[N]{},
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expect: output{
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n: 1,
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agg: metricdata.ExponentialHistogram[N]{
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Temporality: metricdata.CumulativeTemporality,
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DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
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{
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Attributes: fltrAlice,
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StartTime: staticTime,
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Time: staticTime,
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Count: 9,
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Min: metricdata.NewExtrema[N](1),
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Max: metricdata.NewExtrema[N](16),
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Sum: 44,
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Scale: -1,
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PositiveBucket: metricdata.ExponentialBucket{
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Offset: -1,
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Counts: []uint64{1, 6, 2},
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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 FuzzGetBin(f *testing.F) {
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