[CWS-3196] allow disabling env vars resolution when using event process stream

[paulcacheux]

What does this PR do?

This PR allows to disable env vars resolution when using the event process data stream. This will allow USM and NPM team to receive a process stream without env variables. Something that some clients might want/require.

New config:

event_monitoring_config:
  env_vars_resolution:
    enabled: true|false

Motivation

Describe how to test/QA your changes

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=47060030 --os-family=ubuntu

Note: This applies to commit 848d0ab

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: ea1440de-9618-466a-8c5a-ef6f3a204235 Metrics dashboard Target profiles

Baseline: 5c501ac
Comparison: 848d0ab

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
➖	file_tree	memory utilization	+3.69	[+3.54, +3.84]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.85	[+1.12, +2.57]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+1.64	[+1.52, +1.76]	1	Logs bounds checks dashboard
➖	pycheck_lots_of_tags	% cpu utilization	+1.22	[-1.37, +3.80]	1	Logs
➖	quality_gate_idle	memory utilization	+0.63	[+0.58, +0.68]	1	Logs bounds checks dashboard
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.13	[-0.35, +0.61]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.02	[-0.07, +0.12]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.01	[-0.18, +0.19]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.01	[-0.33, +0.34]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.00	[-0.24, +0.25]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.04	[-0.27, +0.18]	1	Logs
➖	idle	memory utilization	-0.15	[-0.20, -0.09]	1	Logs bounds checks dashboard
➖	otel_to_otel_logs	ingress throughput	-0.22	[-1.02, +0.59]	1	Logs
➖	idle_all_features	memory utilization	-0.48	[-0.58, -0.38]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	-0.57	[-3.34, +2.20]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.61	[-0.69, -0.54]	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".