[SVLS-5205] Fix SIGSEGV for Serverless container environments using serverless-init and DogStatsD in multiple containers

[jcstorms1]

What does this PR do?

Adds a nil pointer check to fix a signal segmentation violation in GCP and Azure Serverless container environments using serverless-init and DogStatsD in multiple containers. Previously, if a DogStatsD port was in use by another container, the metric agent would not start and return nil. When creating the cold start metric we did not check for this edge case and would pass a nil pointer to the AddColdStartMetric. This led to a panic and segmentation fault.

Motivation

Issue 26599

Describe how you validated your changes

• Unit Tests
• Tested in GCP and Azure container environments

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 aws.create-vm --pipeline-id=52551736 --os-family=ubuntu

Note: This applies to commit 95b721f

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor b97c90616b68239053e33f46f4db6900f2c59f4a

Diff per package

package	diff	status	size	ancestor	threshold
datadog-dogstatsd-arm64-deb	2.29MB	❌	56.14MB	53.85MB	0.50MB
datadog-iot-agent-x86_64-rpm	0.92MB	❌	113.89MB	112.97MB	0.50MB
datadog-iot-agent-x86_64-suse	0.92MB	❌	113.89MB	112.97MB	0.50MB
datadog-iot-agent-amd64-deb	0.92MB	❌	113.82MB	112.90MB	0.50MB
datadog-iot-agent-aarch64-rpm	0.72MB	❌	109.33MB	108.61MB	0.50MB
datadog-iot-agent-arm64-deb	0.72MB	❌	109.26MB	108.54MB	0.50MB
datadog-heroku-agent-amd64-deb	-0.38MB	✅	506.18MB	506.56MB	0.50MB
datadog-dogstatsd-x86_64-rpm	-17.52MB	✅	58.71MB	76.22MB	0.50MB
datadog-dogstatsd-x86_64-suse	-17.52MB	✅	58.71MB	76.22MB	0.50MB
datadog-dogstatsd-amd64-deb	-17.52MB	✅	58.63MB	76.15MB	0.50MB
datadog-agent-aarch64-rpm	-56.58MB	✅	950.25MB	1006.83MB	0.50MB
datadog-agent-arm64-deb	-56.71MB	✅	940.95MB	997.66MB	0.50MB
datadog-agent-x86_64-rpm	-261.65MB	✅	1020.94MB	1282.59MB	0.50MB
datadog-agent-x86_64-suse	-261.65MB	✅	1020.94MB	1282.59MB	0.50MB
datadog-agent-amd64-deb	-261.78MB	✅	1011.62MB	1273.40MB	0.50MB

Decision

❌ Failed

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: e9c91b94-da21-48ff-acbe-4d9aeea1791f

Baseline: 832149a
Comparison: 95b721f
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_logs	% cpu utilization	+1.78	[-1.50, +5.06]	1	Logs
➖	quality_gate_idle	memory utilization	+0.50	[+0.46, +0.54]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.49	[-0.18, +1.16]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.05	[-0.87, +0.96]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	+0.04	[-0.75, +0.83]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.04	[-0.75, +0.82]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.02	[-0.62, +0.66]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	+0.01	[-0.83, +0.85]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.11, +0.13]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.00	[-0.70, +0.70]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.00	[-0.47, +0.46]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.25	[-0.34, -0.16]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.34	[-0.40, -0.28]	1	Logs
➖	file_tree	memory utilization	-0.44	[-0.57, -0.31]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.62	[-1.40, +0.16]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http1	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http1	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http2	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http2	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	lost_bytes	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10
✅	quality_gate_logs	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".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.