feat(otel-metrics): share SDK trace stats mapping - #54906
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Static quality checks✅ Please find below the results from static quality gates Successful checksInfo
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Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: 3b2688f Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gate_security_mean_fs_load | memory utilization | +0.33 | [+0.25, +0.41] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_private_action_runner | memory utilization | +0.18 | [+0.07, +0.30] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_security_no_fs_load | memory utilization | +0.08 | [-0.06, +0.23] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_idle | memory utilization | -0.05 | [-0.18, +0.07] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_idle_all_features | memory utilization | -0.21 | [-0.26, -0.17] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_security_idle | memory utilization | -0.50 | [-0.60, -0.39] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_metrics_logs | memory utilization | -0.72 | [-0.96, -0.47] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_logs | % cpu utilization | -2.61 | [-3.50, -1.72] | 1 | Logs bounds checks dashboard |
Bounds Checks: ✅ Passed
| perf | experiment | bounds_check_name | replicates_passed | observed_value | links |
|---|---|---|---|---|---|
| ✅ | quality_gate_idle | intake_connections | 10/10 | 4 = 4 | bounds checks dashboard |
| ✅ | quality_gate_idle | memory_usage | 10/10 | 172.57MiB ≤ 178MiB | bounds checks dashboard |
| ✅ | quality_gate_idle | total_bytes_received | 10/10 | 741.34KiB ≤ 819.20KiB | bounds checks dashboard |
| ✅ | quality_gate_idle_all_features | intake_connections | 10/10 | 4 = 4 | bounds checks dashboard |
| ✅ | quality_gate_idle_all_features | memory_usage | 10/10 | 521.26MiB ≤ 538MiB | bounds checks dashboard |
| ✅ | quality_gate_idle_all_features | total_bytes_received | 10/10 | 1.13MiB ≤ 1.25MiB | bounds checks dashboard |
| ✅ | quality_gate_logs | intake_connections | 10/10 | 19 ≤ 40 | bounds checks dashboard |
| ✅ | quality_gate_logs | memory_usage | 10/10 | 203.88MiB ≤ 229MiB | bounds checks dashboard |
| ✅ | quality_gate_logs | missed_bytes | 10/10 | 0B = 0B | bounds checks dashboard |
| ✅ | quality_gate_logs | total_bytes_received | 10/10 | 263.81MiB ≤ 292MiB | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | cpu_usage | 10/10 | 361.38 ≤ 2000 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | intake_connections | 10/10 | 16 ≤ 40 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | memory_usage | 10/10 | 394.70MiB ≤ 439MiB | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | missed_bytes | 10/10 | 0B = 0B | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | total_bytes_received | 10/10 | 0.94GiB ≤ 1.04GiB | bounds checks dashboard |
| ✅ | quality_gate_private_action_runner | memory_usage | 10/10 | 71.95MiB ≤ 76MiB | bounds checks dashboard |
| ✅ | quality_gate_security_idle | cpu_usage | 10/10 | 32.54 ≤ 100 | bounds checks dashboard |
| ✅ | quality_gate_security_idle | memory_usage | 10/10 | 329.12MiB ≤ 335MiB | bounds checks dashboard |
| ✅ | quality_gate_security_mean_fs_load | cpu_usage | 10/10 | 66.13 ≤ 200 | bounds checks dashboard |
| ✅ | quality_gate_security_mean_fs_load | memory_usage | 10/10 | 305.29MiB ≤ 314MiB | bounds checks dashboard |
| ✅ | quality_gate_security_no_fs_load | cpu_usage | 10/10 | 20.69 ≤ 100 | bounds checks dashboard |
| ✅ | quality_gate_security_no_fs_load | memory_usage | 10/10 | 310.24MiB ≤ 343MiB | bounds checks dashboard |
Explanation
Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%
Performance changes are noted in the perf column of each table:
- ✅ = significantly better comparison variant performance
- ❌ = significantly worse comparison variant performance
- ➖ = no significant change in performance
A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".
For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.
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Its configuration does not mark it "erratic".
CI Pass/Fail Decision
✅ Passed. All Quality Gates passed.
- quality_gate_security_mean_fs_load, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_security_mean_fs_load, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check missed_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check missed_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_private_action_runner, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_security_idle, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
- quality_gate_security_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_security_no_fs_load, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
- quality_gate_security_no_fs_load, bounds check memory_usage: 10/10 replicas passed. Gate passed.
What does this PR do?
Adds a transport-neutral
sdktracestatspackage underopentelemetry-mapping-go.BuildSDKTraceStatsPayloadowns SDK metric recognition, field mapping, validation, normalized tags, time windows, per-datapoint error isolation, and lossless sparse-histogram construction.The caller supplies envelope metadata such as source and hostname. The package returns shared APM-stats fields without depending on the Agent or dd-source protobuf types.
Motivation
The Agent and public OTLP metrics intake need to convert
traces.span.sdk.metrics.durationinto the same APM trace-stats contract. Keeping two mappings would let tags, status handling, histogram validation, and operation naming drift. This PR makes one mapping implementation the source of truth while leaving transport adapters and delivery local to each consumer.Stack and shipping order
Scope
Validation
dda inv test --targets=./pkg/opentelemetry-mapping-go/otlp/metrics/...bazel test //pkg/opentelemetry-mapping-go/otlp/metrics/sdktracestats:sdktracestats_testBoth suites pass. Contract coverage includes field mapping, tag normalization, explicit histograms, invalid-datapoint isolation, temporality filtering, fallback windows, and large sparse-bin counts.
Review focus