[CWS] extract process.ancestors from the accessors generation

[paulcacheux]

What does this PR do?

Extract ancestors iteration into a non-generated function, uses it to simplify code and cache the amount of a ancestors for pre-allocating the next ancestors run.

The main goal of this PR is to prepare the field for potential improvements skipping completely the slice creation and doing the evaluation directly while iterating.

Example profile showing this bad pattern

Motivation

Describe how to test/QA your changes

This code is covered by functional tests.

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=48814142 --os-family=ubuntu

Note: This applies to commit af2ff24

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 7a955c6e-88e9-45f8-88a2-5707a15b3044

Baseline: e899559
Comparison: af2ff24
Diff

Optimization Goals: ❌ Significant changes detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	basic_py_check	% cpu utilization	+7.14	[+3.14, +11.13]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	basic_py_check	% cpu utilization	+7.14	[+3.14, +11.13]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+1.50	[+1.41, +1.59]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+1.46	[-1.95, +4.87]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.16	[-0.32, +0.64]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.11	[-0.37, +0.60]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.05	[-0.20, +0.29]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.03	[-0.69, +0.76]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.01	[-0.17, +0.20]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.01	[-0.48, +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	[-0.34, +0.33]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.09, +0.07]	1	Logs
➖	quality_gate_idle	memory utilization	-0.81	[-0.86, -0.77]	1	Logs bounds checks dashboard
➖	otel_to_otel_logs	ingress throughput	-1.26	[-1.98, -0.54]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-2.97	[-3.11, -2.83]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	-3.06	[-3.21, -2.91]	1	Logs

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	quality_gate_idle	memory_usage	8/10	bounds checks dashboard
❌	quality_gate_idle_all_features	memory_usage	8/10	bounds checks dashboard
✅	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	lost_bytes	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

View all feedbacks in Devflow UI.

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2024-11-15 08:25:16 UTC ℹ️ MergeQueue: waiting for PR to be ready

This merge request is not mergeable yet, because of pending checks/missing approvals. It will be added to the queue as soon as checks pass and/or get approvals.
Note: if you pushed new commits since the last approval, you may need additional approval.
You can remove it from the waiting list with /remove command.

---

2024-11-15 08:25:18 UTC ℹ️ MergeQueue: waiting for PR to be ready

This merge request is not mergeable yet, because of pending checks/missing approvals. It will be added to the queue as soon as checks pass and/or get approvals.
Note: if you pushed new commits since the last approval, you may need additional approval.
You can remove it from the waiting list with /remove command.

---

2024-11-15 08:26:20 UTC ℹ️ MergeQueue: merge request added to the queue

The median merge time in main is 24m.