Use more restrictive interface when initializing the config

[hush-hush]

What does this PR do?

Use more restrictive interface when initializing the config.This makes sure we only initialize the configuration within those function.

Describe how to test/QA your changes

Running the CI is enough.

[dustmop]

LGTM

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 0971136a-9bc0-4e1b-b97c-2597647456b2 Metrics dashboard Target profiles

Baseline: fbe15f7
Comparison: 8db129a

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

No significant changes in experiment optimization goals

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

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	idle	memory utilization	+0.31	[+0.26, +0.35]	1	Logs
➖	file_tree	memory utilization	+0.24	[+0.14, +0.35]	1	Logs
➖	idle_all_features	memory utilization	+0.24	[+0.16, +0.32]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.18	[-0.63, +0.99]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.06	[-0.67, +0.79]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.10, +0.12]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.53	[-0.59, -0.48]	1	Logs
➖	basic_py_check	% cpu utilization	-0.74	[-3.39, +1.91]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.96	[-3.38, +1.46]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	idle	memory_usage	10/10

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".