[NDM] Fix for ignored_ip_addresses in autodiscovery

[a-rhodes]

What does this PR do?

Adds fix and unit test for a bug that currently exists in the NDM Autodiscovery Listener.

Motivation

When adding ignored_ip_addresses to the NDM Autodiscovery config in datadog.yaml, the SNMP Listener fails to start and no error is logged.

Describe how to test/QA your changes

Unit test included in PR

Possible Drawbacks / Trade-offs

None

Additional Notes

Related issue: #30181
Unit test showing original unmarshal bug: #30177

[dustmop]

Hello @a-rhodes! Thank you for tracking down the root cause of this issue and supplying this bug fix! It looks good to me, I'm going to reach out to the codeowners to get the necessary approvals to merge it.

I pushed a commit with a release note to the branch dustin.long/fix-config-autodiscovery. It's required for passing our CI. Would you mind either cherry-picking that commit and pushing it to your branch, or installing and running reno yourself? Thanks!

[a-rhodes]

Release note has been added and unit test has been updated to use require

[rtrieu]

LGTM

[dustmop]

/merge

[dd-devflow]

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[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 22m.

Use /merge -c to cancel this operation!

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: f24f24c4-f161-4884-b6cf-a072fbe41f97 Metrics dashboard Target profiles

Baseline: 86dab7c
Comparison: a5addb8

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	+0.68	[-2.14, +3.51]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+0.19	[-2.33, +2.71]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.08	[-0.42, +0.57]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.01	[-0.17, +0.19]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.01	[-0.24, +0.25]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.08, +0.08]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.01	[-0.24, +0.21]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.02	[-0.36, +0.32]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.16	[-0.26, -0.06]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.31	[-0.36, -0.25]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.31	[-1.11, +0.49]	1	Logs
➖	idle	memory utilization	-0.32	[-0.36, -0.28]	1	Logs bounds checks dashboard
➖	idle_all_features	memory utilization	-0.42	[-0.52, -0.32]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	-0.82	[-0.87, -0.78]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	-0.95	[-1.08, -0.82]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.72	[-2.43, -1.01]	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".