[ASCII-1487] wrap template's funcMap function to cast or fail softly

[misteriaud]

What does this PR do?

This PR introduces the funcMapDynTypeChecker function, which enhances the robustness of template rendering by adding dynamic type-checking to function maps. The new function ensures that type mismatches during template execution result in informative error messages rather than causing the entire template rendering process to fail.

Motivation

Function calls in template execution don't have type safety check at compile time, so it could happen that the rendering of an all template fail because of type mismatch between parameter provided to template.Execute() and the function called. In Agent context, it can leads to an entire status with multiple function calls ends like:

executing "generalStatus.tmpl" at <.memstats.Alloc>: invalid value; expected float64 

Describe how to test/QA your changes

QA is covered by unit tests.

[pr-commenter[bot]]

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=38118657 --os-family=ubuntu

Note: This applies to commit f1ebf669

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: c0eb8777-80ac-438c-a8a5-7dfd4a9b33ba Metrics dashboard Target profiles

Baseline: 671f875b21bf84ec8b0d44cdd8cf1d450d808dc4 Comparison: f1ebf669523ff2db780f05c4f31fbbe930eb39c7

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	links
➖	basic_py_check	% cpu utilization	+0.20	[-2.39, +2.78]	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	Logs
➖	idle	memory utilization	-0.09	[-0.13, -0.06]	Logs
➖	file_tree	memory utilization	-0.12	[-0.25, +0.01]	Logs
➖	otel_to_otel_logs	ingress throughput	-0.30	[-1.10, +0.51]	Logs
➖	pycheck_1000_100byte_tags	% cpu utilization	-0.63	[-5.44, +4.18]	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.69	[-1.58, +0.20]	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-7.06	[-19.49, +5.37]	Logs

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".