[SVLS-5268] Disable flush ticker for AWS Lambda

[DylanLovesCoffee]

What does this PR do?

Adds back what was removed in #27722 but gates it to only AWS Lambda so that this doesn't affect serverless-init (GCP + Azure).

Motivation

We've been seeing a very infrequent panic in the Lambda Extension: panic: sync: WaitGroup is reused before previous Wait has returned. The panic appeared when we first introduced using WaitGroups to sync a flush trigger to the logs payload request to intake.

The current theory is that flush ticker will sometimes call WaitGroup.Add() after WaitGroup.Wait() is called, throwing a panic. A flush ticker is not necessary in Lambda where we'll always attempt to flush logs at some point during an invocation.

Additional Notes

Will need to also test this in self-monitoring for serverless-init to ensure it does not cause a regression.

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Changes are a re-implementation, but can be tested again. As the panic is very difficult to reproduce, I deployed the Extension to dogfood apps and monitored if a panic occurs over several hours/days.

[pr-commenter]

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=42283206 --os-family=ubuntu

Note: This applies to commit 8c9d1c3

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: b28866bd-b035-419f-9f64-bdde7baadb78 Metrics dashboard Target profiles

Baseline: a55c8cd
Comparison: 8c9d1c3

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	links
➖	tcp_syslog_to_blackhole	ingress throughput	+2.34	[-10.50, +15.18]	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.53	[-0.28, +1.34]	Logs
➖	idle	memory utilization	+0.15	[+0.12, +0.18]	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.04, +0.04]	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	Logs
➖	otel_to_otel_logs	ingress throughput	-0.35	[-1.16, +0.46]	Logs
➖	basic_py_check	% cpu utilization	-0.49	[-3.19, +2.21]	Logs
➖	file_tree	memory utilization	-0.79	[-0.86, -0.71]	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-1.48	[-3.95, +1.00]	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	idle	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".

[DylanLovesCoffee]

/merge

[dd-devflow]

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Use /merge -c to cancel this operation!