You've already forked opentelemetry-go
mirror of
https://github.com/open-telemetry/opentelemetry-go.git
synced 2025-10-31 00:07:40 +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
@@ -23,10 +23,8 @@ import (
|
||||
"github.com/stretchr/testify/assert"
|
||||
"github.com/stretchr/testify/require"
|
||||
|
||||
"go.opentelemetry.io/otel/attribute"
|
||||
"go.opentelemetry.io/otel/internal/global"
|
||||
"go.opentelemetry.io/otel/sdk/metric/metricdata"
|
||||
"go.opentelemetry.io/otel/sdk/metric/metricdata/metricdatatest"
|
||||
)
|
||||
|
||||
type noErrorHandler struct{ t *testing.T }
|
||||
@@ -739,161 +737,181 @@ func TestSubNormal(t *testing.T) {
|
||||
}
|
||||
|
||||
func TestExponentialHistogramAggregation(t *testing.T) {
|
||||
t.Run("Int64", testExponentialHistogramAggregation[int64])
|
||||
t.Run("Float64", testExponentialHistogramAggregation[float64])
|
||||
t.Cleanup(mockTime(now))
|
||||
|
||||
t.Run("Int64/Delta", testDeltaExpoHist[int64]())
|
||||
t.Run("Float64/Delta", testDeltaExpoHist[float64]())
|
||||
t.Run("Int64/Cumulative", testCumulativeExpoHist[int64]())
|
||||
t.Run("Float64/Cumulative", testCumulativeExpoHist[float64]())
|
||||
}
|
||||
|
||||
func testExponentialHistogramAggregation[N int64 | float64](t *testing.T) {
|
||||
const (
|
||||
maxSize = 4
|
||||
maxScale = 20
|
||||
noMinMax = false
|
||||
noSum = false
|
||||
)
|
||||
|
||||
tests := []struct {
|
||||
name string
|
||||
build func() (Measure[N], ComputeAggregation)
|
||||
input [][]N
|
||||
want metricdata.ExponentialHistogram[N]
|
||||
wantCount int
|
||||
}{
|
||||
func testDeltaExpoHist[N int64 | float64]() func(t *testing.T) {
|
||||
in, out := Builder[N]{
|
||||
Temporality: metricdata.DeltaTemporality,
|
||||
Filter: attrFltr,
|
||||
}.ExponentialBucketHistogram(4, 20, false, false)
|
||||
ctx := context.Background()
|
||||
return test[N](in, out, []teststep[N]{
|
||||
{
|
||||
name: "Delta Single",
|
||||
build: func() (Measure[N], ComputeAggregation) {
|
||||
return Builder[N]{
|
||||
input: []arg[N]{},
|
||||
expect: output{
|
||||
n: 0,
|
||||
agg: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.DeltaTemporality,
|
||||
}.ExponentialBucketHistogram(maxSize, maxScale, noMinMax, noSum)
|
||||
},
|
||||
input: [][]N{
|
||||
{4, 4, 4, 2, 16, 1},
|
||||
},
|
||||
want: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.DeltaTemporality,
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
|
||||
{
|
||||
Count: 6,
|
||||
Min: metricdata.NewExtrema[N](1),
|
||||
Max: metricdata.NewExtrema[N](16),
|
||||
Sum: 31,
|
||||
Scale: -1,
|
||||
PositiveBucket: metricdata.ExponentialBucket{
|
||||
Offset: -1,
|
||||
Counts: []uint64{1, 4, 1},
|
||||
},
|
||||
},
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{},
|
||||
},
|
||||
},
|
||||
wantCount: 1,
|
||||
},
|
||||
{
|
||||
name: "Cumulative Single",
|
||||
build: func() (Measure[N], ComputeAggregation) {
|
||||
return Builder[N]{
|
||||
Temporality: metricdata.CumulativeTemporality,
|
||||
}.ExponentialBucketHistogram(maxSize, maxScale, noMinMax, noSum)
|
||||
input: []arg[N]{
|
||||
{ctx, 4, alice},
|
||||
{ctx, 4, alice},
|
||||
{ctx, 4, alice},
|
||||
{ctx, 2, alice},
|
||||
{ctx, 16, alice},
|
||||
{ctx, 1, alice},
|
||||
},
|
||||
input: [][]N{
|
||||
{4, 4, 4, 2, 16, 1},
|
||||
},
|
||||
want: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.CumulativeTemporality,
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
|
||||
{
|
||||
Count: 6,
|
||||
Min: metricdata.NewExtrema[N](1),
|
||||
Max: metricdata.NewExtrema[N](16),
|
||||
Sum: 31,
|
||||
Scale: -1,
|
||||
PositiveBucket: metricdata.ExponentialBucket{
|
||||
Offset: -1,
|
||||
Counts: []uint64{1, 4, 1},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
wantCount: 1,
|
||||
},
|
||||
{
|
||||
name: "Delta Multiple",
|
||||
build: func() (Measure[N], ComputeAggregation) {
|
||||
return Builder[N]{
|
||||
expect: output{
|
||||
n: 1,
|
||||
agg: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.DeltaTemporality,
|
||||
}.ExponentialBucketHistogram(maxSize, maxScale, noMinMax, noSum)
|
||||
},
|
||||
input: [][]N{
|
||||
{2, 3, 8},
|
||||
{4, 4, 4, 2, 16, 1},
|
||||
},
|
||||
want: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.DeltaTemporality,
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
|
||||
{
|
||||
Count: 6,
|
||||
Min: metricdata.NewExtrema[N](1),
|
||||
Max: metricdata.NewExtrema[N](16),
|
||||
Sum: 31,
|
||||
Scale: -1,
|
||||
PositiveBucket: metricdata.ExponentialBucket{
|
||||
Offset: -1,
|
||||
Counts: []uint64{1, 4, 1},
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
|
||||
{
|
||||
Attributes: fltrAlice,
|
||||
StartTime: staticTime,
|
||||
Time: staticTime,
|
||||
Count: 6,
|
||||
Min: metricdata.NewExtrema[N](1),
|
||||
Max: metricdata.NewExtrema[N](16),
|
||||
Sum: 31,
|
||||
Scale: -1,
|
||||
PositiveBucket: metricdata.ExponentialBucket{
|
||||
Offset: -1,
|
||||
Counts: []uint64{1, 4, 1},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
wantCount: 1,
|
||||
},
|
||||
{
|
||||
name: "Cumulative Multiple ",
|
||||
build: func() (Measure[N], ComputeAggregation) {
|
||||
return Builder[N]{
|
||||
// Delta sums are expected to reset.
|
||||
input: []arg[N]{},
|
||||
expect: output{
|
||||
n: 0,
|
||||
agg: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.DeltaTemporality,
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{},
|
||||
},
|
||||
},
|
||||
},
|
||||
})
|
||||
}
|
||||
|
||||
func testCumulativeExpoHist[N int64 | float64]() func(t *testing.T) {
|
||||
in, out := Builder[N]{
|
||||
Temporality: metricdata.CumulativeTemporality,
|
||||
Filter: attrFltr,
|
||||
}.ExponentialBucketHistogram(4, 20, false, false)
|
||||
ctx := context.Background()
|
||||
return test[N](in, out, []teststep[N]{
|
||||
{
|
||||
input: []arg[N]{},
|
||||
expect: output{
|
||||
n: 0,
|
||||
agg: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.CumulativeTemporality,
|
||||
}.ExponentialBucketHistogram(maxSize, maxScale, noMinMax, noSum)
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{},
|
||||
},
|
||||
},
|
||||
input: [][]N{
|
||||
{2, 3, 8},
|
||||
{4, 4, 4, 2, 16, 1},
|
||||
},
|
||||
{
|
||||
input: []arg[N]{
|
||||
{ctx, 4, alice},
|
||||
{ctx, 4, alice},
|
||||
{ctx, 4, alice},
|
||||
{ctx, 2, alice},
|
||||
{ctx, 16, alice},
|
||||
{ctx, 1, alice},
|
||||
},
|
||||
want: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.CumulativeTemporality,
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
|
||||
{
|
||||
Count: 9,
|
||||
Min: metricdata.NewExtrema[N](1),
|
||||
Max: metricdata.NewExtrema[N](16),
|
||||
Sum: 44,
|
||||
Scale: -1,
|
||||
PositiveBucket: metricdata.ExponentialBucket{
|
||||
Offset: -1,
|
||||
Counts: []uint64{1, 6, 2},
|
||||
expect: output{
|
||||
n: 1,
|
||||
agg: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.CumulativeTemporality,
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
|
||||
{
|
||||
Attributes: fltrAlice,
|
||||
StartTime: staticTime,
|
||||
Time: staticTime,
|
||||
Count: 6,
|
||||
Min: metricdata.NewExtrema[N](1),
|
||||
Max: metricdata.NewExtrema[N](16),
|
||||
Sum: 31,
|
||||
Scale: -1,
|
||||
PositiveBucket: metricdata.ExponentialBucket{
|
||||
Offset: -1,
|
||||
Counts: []uint64{1, 4, 1},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
wantCount: 1,
|
||||
},
|
||||
}
|
||||
|
||||
for _, tt := range tests {
|
||||
t.Run(tt.name, func(t *testing.T) {
|
||||
restore := withHandler(t)
|
||||
defer restore()
|
||||
in, out := tt.build()
|
||||
ctx := context.Background()
|
||||
|
||||
var got metricdata.Aggregation
|
||||
var count int
|
||||
for _, n := range tt.input {
|
||||
for _, v := range n {
|
||||
in(ctx, v, *attribute.EmptySet())
|
||||
}
|
||||
count = out(&got)
|
||||
}
|
||||
|
||||
metricdatatest.AssertAggregationsEqual(t, tt.want, got, metricdatatest.IgnoreTimestamp())
|
||||
assert.Equal(t, tt.wantCount, count)
|
||||
})
|
||||
}
|
||||
{
|
||||
input: []arg[N]{
|
||||
{ctx, 2, alice},
|
||||
{ctx, 3, alice},
|
||||
{ctx, 8, alice},
|
||||
},
|
||||
expect: output{
|
||||
n: 1,
|
||||
agg: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.CumulativeTemporality,
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
|
||||
{
|
||||
Attributes: fltrAlice,
|
||||
StartTime: staticTime,
|
||||
Time: staticTime,
|
||||
Count: 9,
|
||||
Min: metricdata.NewExtrema[N](1),
|
||||
Max: metricdata.NewExtrema[N](16),
|
||||
Sum: 44,
|
||||
Scale: -1,
|
||||
PositiveBucket: metricdata.ExponentialBucket{
|
||||
Offset: -1,
|
||||
Counts: []uint64{1, 6, 2},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
{
|
||||
input: []arg[N]{},
|
||||
expect: output{
|
||||
n: 1,
|
||||
agg: metricdata.ExponentialHistogram[N]{
|
||||
Temporality: metricdata.CumulativeTemporality,
|
||||
DataPoints: []metricdata.ExponentialHistogramDataPoint[N]{
|
||||
{
|
||||
Attributes: fltrAlice,
|
||||
StartTime: staticTime,
|
||||
Time: staticTime,
|
||||
Count: 9,
|
||||
Min: metricdata.NewExtrema[N](1),
|
||||
Max: metricdata.NewExtrema[N](16),
|
||||
Sum: 44,
|
||||
Scale: -1,
|
||||
PositiveBucket: metricdata.ExponentialBucket{
|
||||
Offset: -1,
|
||||
Counts: []uint64{1, 6, 2},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
})
|
||||
}
|
||||
|
||||
func FuzzGetBin(f *testing.F) {
|
||||
|
||||
Reference in New Issue
Block a user