enhancement(kafka sink): Make healthcheck topic configurable

[yalinglee]

This PR proposes a solution for #10465.

It adds a new healthcheck_topic option so when the topic value is templated, healthchecks won't fail.

Closes: #10465

[bits-bot]

All committers have signed the CLA.

[yalinglee]

Please let me know if the proposed approach makes sense or if you have other suggestions!

This is also my first time contributing and writing Rust code, so any feedback would be appreciated!

[jszwedko]

Thanks for this @yalinglee ! I think ideally this option would live under the existing healthcheck option (e.g. as healthcheck.topic but I recognize that that would be a fairly large refactoring of the config structs and, likely, the documentation so I'm ok with having this be a top-level option for now.

[yalinglee]

@jszwedko Apologies for the late reply! I was hesitant to move this forward for fear of introducing a less-than-ideal solution to the repo, but I will defer to your judgement on this. Thank you!

[github-actions]

Regression Detector Results

Run ID: f86a3777-16a6-4e01-8062-6396b8fd61e7
Baseline: 48a29b3
Comparison: c2fc5ef
Total CPUs: 7

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
➖	syslog_loki	ingress throughput	+1.69	[+1.61, +1.76]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	+1.38	[+1.25, +1.51]
➖	syslog_log2metric_humio_metrics	ingress throughput	+1.30	[+1.11, +1.48]
➖	datadog_agent_remap_datadog_logs	ingress throughput	+0.77	[+0.64, +0.90]
➖	file_to_blackhole	egress throughput	+0.73	[-1.78, +3.25]
➖	splunk_hec_route_s3	ingress throughput	+0.52	[+0.03, +1.00]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	+0.44	[+0.36, +0.53]
➖	http_to_s3	ingress throughput	+0.25	[-0.03, +0.52]
➖	otlp_grpc_to_blackhole	ingress throughput	+0.21	[+0.11, +0.30]
➖	http_to_http_noack	ingress throughput	+0.20	[+0.09, +0.31]
➖	http_text_to_http_json	ingress throughput	+0.18	[+0.06, +0.29]
➖	http_to_http_json	ingress throughput	+0.04	[-0.04, +0.12]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	+0.00	[-0.14, +0.15]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	-0.00	[-0.14, +0.14]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.02	[-0.13, +0.10]
➖	http_to_http_acks	ingress throughput	-0.08	[-1.43, +1.28]
➖	enterprise_http_to_http	ingress throughput	-0.10	[-0.17, -0.02]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	-0.10	[-0.21, +0.00]
➖	http_elasticsearch	ingress throughput	-0.30	[-0.37, -0.23]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	-0.59	[-0.69, -0.49]
➖	fluent_elasticsearch	ingress throughput	-0.74	[-1.21, -0.27]
➖	syslog_splunk_hec_logs	ingress throughput	-0.89	[-0.97, -0.82]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	-0.90	[-1.05, -0.75]
➖	otlp_http_to_blackhole	ingress throughput	-1.01	[-1.15, -0.87]
➖	socket_to_socket_blackhole	ingress throughput	-1.77	[-1.84, -1.69]
➖	syslog_humio_logs	ingress throughput	-1.96	[-2.09, -1.83]
➖	datadog_agent_remap_blackhole	ingress throughput	-3.80	[-3.93, -3.66]

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".