Add clarifying unit of measurement

[estherk15]

What does this PR do?

• Add unit of measurement for Logs batch wait time.

Motivation

Customer feedback on documentation is that the lack of unit of measurement is unclear https://datadoghq.atlassian.net/browse/DOCS-8347 Confirmation slack

Additional Notes

Possible Drawbacks / Trade-offs

This only adds a unit of measurement for the one parameter, not all parameters state the unit of measurement which looks inconsistent.

[bits-bot]

All committers have signed the CLA.

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: a9cedfda-3109-48c9-9d2b-aea2615f8e0a Metrics dashboard Target profiles

Baseline: 3407a2ca3fa6ef26838157b2cefa0db7877dba40 Comparison: ac74ac95006a391ef9bae833faaf709512cd4293

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
➖	file_tree	memory utilization	+2.77	[+2.67, +2.86]	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+2.09	[+1.19, +2.99]	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+1.78	[-11.42, +14.98]	Logs
➖	basic_py_check	% cpu utilization	+1.69	[-0.88, +4.26]	Logs
➖	otel_to_otel_logs	ingress throughput	+1.15	[+0.33, +1.96]	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.00, +0.00]	Logs
➖	idle	memory utilization	-0.11	[-0.15, -0.08]	Logs
➖	pycheck_1000_100byte_tags	% cpu utilization	-2.34	[-7.12, +2.43]	Logs

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".

[pgimalac]

/merge

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[estherk15]

/merge

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