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[exporter/elasticsearch] Add exponential histogram support (open-tele…
…metry#34818) **Description:** <Describe what has changed.> <!--Ex. Fixing a bug - Describe the bug and how this fixes the issue. Ex. Adding a feature - Explain what this achieves.--> Convert exponential histograms to T-digest histograms supported by Elasticsearch **Link to tracking Issue:** <Issue number if applicable> Fixes open-telemetry#34813 **Testing:** <Describe what testing was performed and which tests were added.> Unit tests and exporter test **Documentation:** <Describe the documentation added.>
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27 changes: 27 additions & 0 deletions
27
.chloggen/elasticsearchexporter_exponential-histogram.yaml
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| # Use this changelog template to create an entry for release notes. | ||
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| # One of 'breaking', 'deprecation', 'new_component', 'enhancement', 'bug_fix' | ||
| change_type: enhancement | ||
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| # The name of the component, or a single word describing the area of concern, (e.g. filelogreceiver) | ||
| component: elasticsearchexporter | ||
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| # A brief description of the change. Surround your text with quotes ("") if it needs to start with a backtick (`). | ||
| note: Add exponential histogram support | ||
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| # Mandatory: One or more tracking issues related to the change. You can use the PR number here if no issue exists. | ||
| issues: [34813] | ||
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| # (Optional) One or more lines of additional information to render under the primary note. | ||
| # These lines will be padded with 2 spaces and then inserted directly into the document. | ||
| # Use pipe (|) for multiline entries. | ||
| subtext: | ||
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| # If your change doesn't affect end users or the exported elements of any package, | ||
| # you should instead start your pull request title with [chore] or use the "Skip Changelog" label. | ||
| # Optional: The change log or logs in which this entry should be included. | ||
| # e.g. '[user]' or '[user, api]' | ||
| # Include 'user' if the change is relevant to end users. | ||
| # Include 'api' if there is a change to a library API. | ||
| # Default: '[user]' | ||
| change_logs: [] |
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66 changes: 66 additions & 0 deletions
66
exporter/elasticsearchexporter/internal/exphistogram/exphistogram.go
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| // Copyright The OpenTelemetry Authors | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
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| // Package exphistogram contains utility functions for exponential histogram conversions. | ||
| package exphistogram // import "github.com/open-telemetry/opentelemetry-collector-contrib/exporter/elasticsearchexporter/internal/exphistogram" | ||
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| import ( | ||
| "math" | ||
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| "go.opentelemetry.io/collector/pdata/pmetric" | ||
| ) | ||
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| // LowerBoundary calculates the lower boundary given index and scale. | ||
| // Adopted from https://opentelemetry.io/docs/specs/otel/metrics/data-model/#producer-expectations | ||
| func LowerBoundary(index, scale int) float64 { | ||
| if scale <= 0 { | ||
| return LowerBoundaryNegativeScale(index, scale) | ||
| } | ||
| // Use this form in case the equation above computes +Inf | ||
| // as the lower boundary of a valid bucket. | ||
| inverseFactor := math.Ldexp(math.Ln2, -scale) | ||
| return 2.0 * math.Exp(float64(index-(1<<scale))*inverseFactor) | ||
| } | ||
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| // LowerBoundaryNegativeScale calculates the lower boundary for scale <= 0. | ||
| // Adopted from https://opentelemetry.io/docs/specs/otel/metrics/data-model/#producer-expectations | ||
| func LowerBoundaryNegativeScale(index, scale int) float64 { | ||
| return math.Ldexp(1, index<<-scale) | ||
| } | ||
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| // ToTDigest converts an OTLP exponential histogram data point to T-Digest counts and mean centroid values. | ||
| func ToTDigest(dp pmetric.ExponentialHistogramDataPoint) (counts []int64, values []float64) { | ||
| scale := int(dp.Scale()) | ||
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| offset := int(dp.Negative().Offset()) | ||
| bucketCounts := dp.Negative().BucketCounts() | ||
| for i := bucketCounts.Len() - 1; i >= 0; i-- { | ||
| count := bucketCounts.At(i) | ||
| if count == 0 { | ||
| continue | ||
| } | ||
| lb := -LowerBoundary(offset+i+1, scale) | ||
| ub := -LowerBoundary(offset+i, scale) | ||
| counts = append(counts, int64(count)) | ||
| values = append(values, lb+(ub-lb)/2) | ||
| } | ||
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| if zeroCount := dp.ZeroCount(); zeroCount != 0 { | ||
| counts = append(counts, int64(zeroCount)) | ||
| values = append(values, 0) | ||
| } | ||
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| offset = int(dp.Positive().Offset()) | ||
| bucketCounts = dp.Positive().BucketCounts() | ||
| for i := 0; i < bucketCounts.Len(); i++ { | ||
| count := bucketCounts.At(i) | ||
| if count == 0 { | ||
| continue | ||
| } | ||
| lb := LowerBoundary(offset+i, scale) | ||
| ub := LowerBoundary(offset+i+1, scale) | ||
| counts = append(counts, int64(count)) | ||
| values = append(values, lb+(ub-lb)/2) | ||
| } | ||
| return | ||
| } |
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135
exporter/elasticsearchexporter/internal/exphistogram/exphistogram_test.go
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| // Copyright The OpenTelemetry Authors | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
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| package exphistogram | ||
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| import ( | ||
| "testing" | ||
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| "github.com/stretchr/testify/assert" | ||
| "go.opentelemetry.io/collector/pdata/pmetric" | ||
| ) | ||
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| func TestToTDigest(t *testing.T) { | ||
| for _, tc := range []struct { | ||
| name string | ||
| scale int32 | ||
| zeroCount uint64 | ||
| positiveOffset int32 | ||
| positiveBuckets []uint64 | ||
| negativeOffset int32 | ||
| negativeBuckets []uint64 | ||
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| expectedCounts []int64 | ||
| expectedValues []float64 | ||
| }{ | ||
| { | ||
| name: "empty", | ||
| scale: 0, | ||
| expectedCounts: nil, | ||
| expectedValues: nil, | ||
| }, | ||
| { | ||
| name: "empty, scale=1", | ||
| scale: 1, | ||
| expectedCounts: nil, | ||
| expectedValues: nil, | ||
| }, | ||
| { | ||
| name: "empty, scale=-1", | ||
| scale: -1, | ||
| expectedCounts: nil, | ||
| expectedValues: nil, | ||
| }, | ||
| { | ||
| name: "zeros", | ||
| scale: 0, | ||
| zeroCount: 1, | ||
| expectedCounts: []int64{1}, | ||
| expectedValues: []float64{0}, | ||
| }, | ||
| { | ||
| name: "scale=0", | ||
| scale: 0, | ||
| zeroCount: 1, | ||
| positiveBuckets: []uint64{1, 1}, | ||
| negativeBuckets: []uint64{1, 1}, | ||
| expectedCounts: []int64{1, 1, 1, 1, 1}, | ||
| expectedValues: []float64{-3, -1.5, 0, 1.5, 3}, | ||
| }, | ||
| { | ||
| name: "scale=0, no zeros", | ||
| scale: 0, | ||
| zeroCount: 0, | ||
| positiveBuckets: []uint64{1, 1}, | ||
| negativeBuckets: []uint64{1, 1}, | ||
| expectedCounts: []int64{1, 1, 1, 1}, | ||
| expectedValues: []float64{-3, -1.5, 1.5, 3}, | ||
| }, | ||
| { | ||
| name: "scale=0, offset=1", | ||
| scale: 0, | ||
| zeroCount: 1, | ||
| positiveOffset: 1, | ||
| positiveBuckets: []uint64{1, 1}, | ||
| negativeOffset: 1, | ||
| negativeBuckets: []uint64{1, 1}, | ||
| expectedCounts: []int64{1, 1, 1, 1, 1}, | ||
| expectedValues: []float64{-6, -3, 0, 3, 6}, | ||
| }, | ||
| { | ||
| name: "scale=0, offset=-1", | ||
| scale: 0, | ||
| zeroCount: 1, | ||
| positiveOffset: -1, | ||
| positiveBuckets: []uint64{1, 1}, | ||
| negativeOffset: -1, | ||
| negativeBuckets: []uint64{1, 1}, | ||
| expectedCounts: []int64{1, 1, 1, 1, 1}, | ||
| expectedValues: []float64{-1.5, -0.75, 0, 0.75, 1.5}, | ||
| }, | ||
| { | ||
| name: "scale=0, different offsets", | ||
| scale: 0, | ||
| zeroCount: 1, | ||
| positiveOffset: -1, | ||
| positiveBuckets: []uint64{1, 1}, | ||
| negativeOffset: 1, | ||
| negativeBuckets: []uint64{1, 1}, | ||
| expectedCounts: []int64{1, 1, 1, 1, 1}, | ||
| expectedValues: []float64{-6, -3, 0, 0.75, 1.5}, | ||
| }, | ||
| { | ||
| name: "scale=-1", | ||
| scale: -1, | ||
| zeroCount: 1, | ||
| positiveBuckets: []uint64{1, 1}, | ||
| negativeBuckets: []uint64{1, 1}, | ||
| expectedCounts: []int64{1, 1, 1, 1, 1}, | ||
| expectedValues: []float64{-10, -2.5, 0, 2.5, 10}, | ||
| }, | ||
| { | ||
| name: "scale=1", | ||
| scale: 1, | ||
| zeroCount: 1, | ||
| positiveBuckets: []uint64{1, 1}, | ||
| negativeBuckets: []uint64{1, 1}, | ||
| expectedCounts: []int64{1, 1, 1, 1, 1}, | ||
| expectedValues: []float64{-1.7071067811865475, -1.2071067811865475, 0, 1.2071067811865475, 1.7071067811865475}, | ||
| }, | ||
| } { | ||
| t.Run(tc.name, func(t *testing.T) { | ||
| dp := pmetric.NewExponentialHistogramDataPoint() | ||
| dp.SetScale(tc.scale) | ||
| dp.SetZeroCount(tc.zeroCount) | ||
| dp.Positive().SetOffset(tc.positiveOffset) | ||
| dp.Positive().BucketCounts().FromRaw(tc.positiveBuckets) | ||
| dp.Negative().SetOffset(tc.negativeOffset) | ||
| dp.Negative().BucketCounts().FromRaw(tc.negativeBuckets) | ||
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| counts, values := ToTDigest(dp) | ||
| assert.Equal(t, tc.expectedCounts, counts) | ||
| assert.Equal(t, tc.expectedValues, values) | ||
| }) | ||
| } | ||
| } |
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