[ASCII-2336] Build the Default source tree once config is Ready for usage

[dustmop]

What does this PR do?

Build the default source of the config model by inserting all keys from BindEnv, SetKnown and SetDefault. Insert values from SetDefault during this process. Once the config is built, these three methods should not be called again.

Motivation

Removing our dependency on viper.

Describe how to test/QA your changes

Run with envvar DD_CONF_NODETREEMODEL=enable. Config will be built, but the agent won't sucessfully run, as this feature is a work in progress.

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=47090222 --os-family=ubuntu

Note: This applies to commit d1c3487

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 78865564-0652-48f8-9ab7-b5f3cf1cd081 Metrics dashboard Target profiles

Baseline: 3270886
Comparison: d1c3487

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
➖	quality_gate_idle_all_features	memory utilization	+1.73	[+1.59, +1.87]	1	Logs bounds checks dashboard
➖	idle_all_features	memory utilization	+1.11	[+0.97, +1.25]	1	Logs bounds checks dashboard
➖	otel_to_otel_logs	ingress throughput	+1.11	[+0.30, +1.91]	1	Logs
➖	quality_gate_idle	memory utilization	+0.85	[+0.79, +0.91]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	+0.79	[-1.91, +3.49]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.23	[-0.25, +0.72]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+0.13	[-2.39, +2.64]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.04	[-0.21, +0.29]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.20, +0.24]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.12, +0.10]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.01	[-0.35, +0.33]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.17	[-0.35, +0.01]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.24	[-0.95, +0.48]	1	Logs
➖	file_tree	memory utilization	-0.54	[-0.69, -0.39]	1	Logs
➖	idle	memory utilization	-0.67	[-0.72, -0.61]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.75	[-0.80, -0.69]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	idle	memory_usage	10/10
✅	idle_all_features	memory_usage	10/10
✅	quality_gate_idle	memory_usage	10/10
✅	quality_gate_idle_all_features	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".