[PROCS-1451]Improving stripArguments functions for windows

[ccdaniele]

What does this PR do?

• Updates the function stripArguments() currently on pkg/process/procutil/data_scrubber.go to allow expand this function to be implemented on windows.
• Removes stripArguments() from data_scrubber.go and add two different variations of the function to the new files data_scrubber_fallback.go and data_scrubber_windows.go allowing to include a corresponding version of stripArguments() while the process agent is build in different OS.
• Creates the new files data_scrubber_fallback_test.go and data_scrubber_windows_test.go to testing the new functions.

Motivation

• The current behavior of the stripArguments() while the process agent is running on Windows OS is limited when a command line contains an empty space as a character in its structure. The new functionality will provide the function with more parameters to retrieve the command without its arguments even in those cases.

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

1. Windows VM

You will need to provision a Microsoft Windows Virtual Machine to run its build of the Datadog agent

2. Prepare the test

For doing this you will need to do the following:

Datadog.yaml file:

• Create a datadog.yaml file including the following parameters:

...
process_config:
  process_collection:
    enabled: true
  strip_proc_arguments: true
...

Example process (optional)

• Although your windows VM should be already running several process, you can also create your own executable script for your test. This post will give you several options, however the easiest way should be creating a notepad file using the format below:

#!/bin/bash

SECONDS=500

sleep $SECONDS

Click the File menu, Select the Save As option, and confirm a descriptive name for the script.

After that you can open powershell and execute the script including the file with its path and some arguments, for example:

& "C:\PATH\TO\SCRIPT\first_script.ps1 -argument1 argument2"

3. Run the process check

With the configuration file and the your script ready now is time to use the command below to run the process check manually:

/opt/datadog-agent/embedded/bin/process-agent check process  -config <file with updated custom datadog.yaml file>.yaml

4. Check the results.

Finally, it's time to confirm if the function is new function is working. With the command above executed, you should see the check output printed in the console. You can search in the console for your command ( CTR + f ).

The test should pass if the command appears without its arguments:

C:\PATH\TO\SCRIPT\first_script.ps1 

Reviewer's Checklist

• [ ] If known, an appropriate milestone has been selected; otherwise the Triage milestone is set.
• [ ] Use the major_change label if your change either has a major impact on the code base, is impacting multiple teams or is changing important well-established internals of the Agent. This label will be use during QA to make sure each team pay extra attention to the changed behavior. For any customer facing change use a releasenote.
• [ ] A release note has been added or the changelog/no-changelog label has been applied.
• [ ] Changed code has automated tests for its functionality.
• [ ] Adequate QA/testing plan information is provided. Except if the qa/skip-qa label, with required either qa/done or qa/no-code-change labels, are applied.
• [ ] At least one team/.. label has been applied, indicating the team(s) that should QA this change.
• [ ] If applicable, docs team has been notified or an issue has been opened on the documentation repo.
• [ ] If applicable, the need-change/operator and need-change/helm labels have been applied.
• [ ] If applicable, the k8s/<min-version> label, indicating the lowest Kubernetes version compatible with this feature.
• [ ] If applicable, the config template has been updated.

[pr-commenter[bot]]

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Regression Detector Results

Run ID: 548ae8e4-9469-4f8f-a20b-97475ac61070 Baseline: aa19c3ff0eaa21704341ddb694dfb1d12d62fabd Comparison: e22dec71a4b4a14cba132b85fd1d6630ebf296cb Total CPUs: 7

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	+1.27	[-5.32, +7.86]
➖	file_tree	memory utilization	+0.32	[+0.25, +0.39]
➖	idle	memory utilization	+0.27	[+0.25, +0.30]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+1.27	[-5.32, +7.86]
➖	process_agent_standard_check	memory utilization	+0.54	[+0.49, +0.58]
➖	process_agent_real_time_mode	memory utilization	+0.49	[+0.47, +0.52]
➖	process_agent_standard_check_with_stats	memory utilization	+0.49	[+0.45, +0.53]
➖	file_tree	memory utilization	+0.32	[+0.25, +0.39]
➖	idle	memory utilization	+0.27	[+0.25, +0.30]
➖	trace_agent_json	ingress throughput	+0.03	[+0.01, +0.06]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.03, +0.03]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.06, +0.06]
➖	trace_agent_msgpack	ingress throughput	+0.00	[-0.01, +0.01]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.17	[-0.23, -0.11]
➖	otel_to_otel_logs	ingress throughput	-1.17	[-1.86, -0.47]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.18	[-2.60, +0.25]

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".