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Tyler Yahn
dcfec0c2fa
* Add the random fixed size exemplar reservoir * Rename fixed.go to storage.go * Update sdk/metric/internal/exemplar/rand.go Co-authored-by: David Ashpole <dashpole@google.com> * Remove stale ref to spec recommendation * Add comments to clarify the reset/advance/Collect methods * Apply comment from feedback * Add random func to gen rand float64 on (0,1) * Use random in TestFixedSizeSamplingCorrectness * Add clarifying algorithm comments Include a high-level overview of the algorithm implemented and clarify parameter names to be consistent. * Fix duplicate word * Update sdk/metric/internal/exemplar/rand.go * Comment TestFixedSizeSamplingCorrectness * Update test delta * Test collect less than cap * Remove measurement.Valid method --------- Co-authored-by: David Ashpole <dashpole@google.com> Co-authored-by: Chester Cheung <cheung.zhy.csu@gmail.com>
63 lines
1.7 KiB
Go
63 lines
1.7 KiB
Go
// Copyright The OpenTelemetry Authors
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//
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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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package exemplar
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import (
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"context"
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"math"
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"sort"
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"testing"
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"github.com/stretchr/testify/assert"
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)
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func TestFixedSize(t *testing.T) {
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t.Run("Int64", ReservoirTest[int64](func(n int) (Reservoir[int64], int) {
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return FixedSize[int64](n), n
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}))
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t.Run("Float64", ReservoirTest[float64](func(n int) (Reservoir[float64], int) {
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return FixedSize[float64](n), n
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}))
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}
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func TestFixedSizeSamplingCorrectness(t *testing.T) {
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intensity := 0.1
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sampleSize := 1000
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data := make([]float64, sampleSize*1000)
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for i := range data {
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// Generate exponentially distributed data.
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data[i] = (-1.0 / intensity) * math.Log(random())
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}
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// Sort to test position bias.
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sort.Float64s(data)
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r := FixedSize[float64](sampleSize)
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for _, value := range data {
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r.Offer(context.Background(), staticTime, value, nil)
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}
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var sum float64
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for _, m := range r.(*randRes[float64]).store {
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sum += m.Value
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}
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mean := sum / float64(sampleSize)
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// Check the intensity/rate of the sampled distribution is preserved
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// ensuring no bias in our random sampling algorithm.
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assert.InDelta(t, 1/mean, intensity, 0.02) // Within 5σ.
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}
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