trace-agent: add config for delta profiles in internal profiling

[paulcacheux]

What does this PR do?

All other agents have a config to enable/disable delta profiles (and it's actually turned on by default for other agents and when deployed internally).

This PR plugs the already available config (shared with the core agent) to the profiling config used by the trace agent.

The main goal with this PR is to bring consistency around profiling of Datadog agents.

You can read more about delta profiles here.

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=48051423 --os-family=ubuntu

Note: This applies to commit 8e9c246

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: eeb9e972-6f5a-4a62-bce0-d5fee280a099 Metrics dashboard Target profiles

Baseline: 295783a
Comparison: 8e9c246

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	trials	links
➖	basic_py_check	% cpu utilization	+2.21	[-0.46, +4.88]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.80	[+0.07, +1.54]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.79	[+0.68, +0.91]	1	Logs bounds checks dashboard
➖	idle	memory utilization	+0.68	[+0.63, +0.73]	1	Logs bounds checks dashboard
➖	file_to_blackhole_300ms_latency	egress throughput	+0.05	[-0.13, +0.23]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.01	[-0.21, +0.23]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.01	[-0.32, +0.34]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.01	[-0.26, +0.24]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.02	[-0.11, +0.08]	1	Logs
➖	file_tree	memory utilization	-0.08	[-0.22, +0.07]	1	Logs
➖	quality_gate_idle	memory utilization	-0.10	[-0.15, -0.05]	1	Logs bounds checks dashboard
➖	otel_to_otel_logs	ingress throughput	-0.13	[-0.94, +0.68]	1	Logs
➖	idle_all_features	memory utilization	-0.20	[-0.30, -0.11]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.36	[-0.41, -0.30]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.46	[-0.95, +0.02]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-0.62	[-3.13, +1.88]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	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
✅	idle	memory_usage	10/10
✅	idle_all_features	memory_usage	10/10
✅	quality_gate_idle	memory_usage	10/10
✅	quality_gate_idle_all_features	memory_usage	10/10

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".

[paulcacheux]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 23m.

Use /merge -c to cancel this operation!