[CWS] windows: do not send events for rename pre args

[paulcacheux]

What does this PR do?

Some of the rename ETW events are not generating probe events but just filling an LRU waiting for further events to actually create the final probe events. As such we definitely should not be creating a probe event with an unknown event type. This is seemingly not causing issues further down the road but could in the future and it's clearly wasting some CPU cycles.

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=48848184 --os-family=ubuntu

Note: This applies to commit b9faada

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: d99d2b31-e08b-4afc-8571-df8b53f0cedc

Baseline: 5ed352e
Comparison: b9faada
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	pycheck_lots_of_tags	% cpu utilization	+4.26	[+0.78, +7.74]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+3.62	[+3.49, +3.75]	1	Logs bounds checks dashboard
➖	basic_py_check	% cpu utilization	+1.68	[-2.10, +5.47]	1	Logs
➖	quality_gate_idle	memory utilization	+0.23	[+0.17, +0.29]	1	Logs bounds checks dashboard
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.18	[-0.30, +0.66]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.06	[-0.14, +0.25]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.05	[-0.44, +0.54]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.00	[-0.49, +0.49]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.01	[-0.33, +0.31]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.02	[-0.11, +0.07]	1	Logs
➖	file_tree	memory utilization	-0.06	[-0.19, +0.06]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.10	[-0.34, +0.14]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.30	[-0.35, -0.24]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.42	[-1.14, +0.30]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.59	[-1.31, +0.14]	1	Logs

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	quality_gate_idle	memory_usage	4/10	bounds checks dashboard
❌	quality_gate_idle_all_features	memory_usage	6/10	bounds checks dashboard
❌	file_to_blackhole_100ms_latency	lost_bytes	8/10
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	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

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

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

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".

[paulcacheux]

/merge

[dd-devflow]

Devflow running: /merge

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2024-11-13 08:48:49 UTC ℹ️ MergeQueue: pull request added to the queue

The median merge time in main is 23m.