Read and propagate helm config for security products

[robertpi]

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What does this PR do?

This PR is indent to allow ASM, IAST and SCA to be enabled (or disabled) by Helm Charts config on the controller pod. The cluster-agent, running on the controller pod, will read some environment variables and use this to mutate the configuration of other pod to set the environment variables that will activate products within the client libraries.

Motivation

This will make it easier for k8s clients (at least those using Helm Charts) to activate our products. Simplified installation is a common request.

Additional Notes

The PR will is designed to work with this PR: https://github.com/DataDog/helm-charts/pull/1337

Each product will have env var with the following format:

• DD_product_ENABLED_PROPAGATE

It will result in the a variable with the following format be propagated to all pods (or those conforming to the filters):

DD_product_ENABLED

Possible Drawbacks / Trade-offs

Adds more complexity to our config handling.

Describe how to test/QA your changes

• Unit tests have been added
• Manual end to end testing has been preformed

[bits-bot]

All committers have signed the CLA.

[pr-commenter[bot]]

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Regression Detector Results

Run ID: 5a24b2d6-2392-4ffc-9f6e-085606ec5843 Baseline: d6a7e7927b96c58b1ca37d5a45453a1a457e3958 Comparison: f16415e55c531017161e7ac498a923be8e5fef4a

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.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+0.27	[-6.25, +6.79]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+0.27	[-6.25, +6.79]
➖	process_agent_standard_check	memory utilization	+0.01	[-0.02, +0.05]
➖	trace_agent_msgpack	ingress throughput	+0.01	[-0.00, +0.02]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.01	[-0.04, +0.03]
➖	idle	memory utilization	-0.06	[-0.11, -0.00]
➖	otel_to_otel_logs	ingress throughput	-0.10	[-0.72, +0.51]
➖	process_agent_standard_check_with_stats	memory utilization	-0.11	[-0.14, -0.07]
➖	file_tree	memory utilization	-0.58	[-0.70, -0.46]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.79	[-0.84, -0.73]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.95	[-2.38, +0.47]
➖	process_agent_real_time_mode	memory utilization	-1.00	[-1.04, -0.96]
➖	basic_py_check	% cpu utilization	-1.87	[-4.00, +0.27]

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".

[kacper-murzyn]

/merge

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