Add an OpenTelemetry receiver source.

[tobz]

Context

Adds a new source for receiving OpenTelemetry data.

Super WIP.

[pr-commenter]

Regression Detector (DogStatsD)

Regression Detector Results

Run ID: 615439a7-e6ce-4ab9-861b-9a5441444744

Baseline: 7.55.2
Comparison: 7.55.3

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

Significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

perf	experiment	goal	Δ mean %	Δ mean % CI	links
✅	dsd_uds_100mb_3k_contexts_distributions_only	memory utilization	-5.34	[-5.57, -5.12]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	links
➖	dsd_uds_1mb_50k_contexts_memlimit	ingress throughput	+0.03	[-0.01, +0.08]
➖	dsd_uds_10mb_3k_contexts	ingress throughput	+0.03	[-0.00, +0.06]
➖	dsd_uds_512kb_3k_contexts	ingress throughput	+0.02	[-0.03, +0.07]
➖	dsd_uds_1mb_50k_contexts	ingress throughput	+0.02	[-0.00, +0.04]
➖	dsd_uds_100mb_3k_contexts	ingress throughput	+0.01	[+0.00, +0.03]
➖	dsd_uds_100mb_250k_contexts	ingress throughput	+0.01	[-0.01, +0.02]
➖	dsd_uds_500mb_3k_contexts	ingress throughput	+0.00	[+0.00, +0.00]
➖	dsd_uds_1mb_3k_contexts	ingress throughput	-0.04	[-0.11, +0.02]
✅	dsd_uds_100mb_3k_contexts_distributions_only	memory utilization	-5.34	[-5.57, -5.12]

Explanation

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:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. 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.

3. Its configuration does not mark it "erratic".