chore(releasing): Automated changelog generation

[neuronull]

This change implements auto generated user-facing changelogs for OPW.

See the added changelog.d/README.md for details on the process.

DX things to note (apart from the README.md which you should read through):

• currently the existing deprecations/breaking changes process in Vector is unchanged. That is, in addition to editing the upgrade guide when introducing breaking changes, a changelog fragment file needs to be added. At a future time we will aim to generate the upgrade guide using the fragment files automatically. For the time being, it can be copy-pasted between the two since both need to be valid markdown.

Testing done

• tested the script used to validate fragments locally
• tested the generate release notes ruby script locally

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[datadog-vectordotdev]

Datadog Report

Branch report: neuronull/OPW-187_automated_changelog_generation
Commit report: e24e2d8

✅ vector: 0 Failed, 0 New Flaky, 2075 Passed, 0 Skipped, 1m 22.88s Wall Time

[jszwedko]

Very much looking forward to generating Vector changelogs!

[bruceg]

One required change below, otherwise just discussions and nit picking.

[github-actions]

Regression Detector Results

Run ID: 11deb0fb-b216-4e17-802d-55a3a824d396
Baseline: 9bdaaa2
Comparison: d115e26
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_log2metric_humio_metrics	ingress throughput	+2.95	[+2.81, +3.10]
➖	http_text_to_http_json	ingress throughput	+1.79	[+1.66, +1.92]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	+1.52	[+1.41, +1.63]
➖	splunk_hec_route_s3	ingress throughput	+1.30	[+0.78, +1.82]
➖	otlp_http_to_blackhole	ingress throughput	+0.60	[+0.43, +0.77]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	+0.50	[+0.39, +0.60]
➖	http_to_http_noack	ingress throughput	+0.16	[+0.06, +0.26]
➖	datadog_agent_remap_blackhole	ingress throughput	+0.14	[+0.05, +0.23]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	+0.13	[+0.01, +0.24]
➖	http_to_http_json	ingress throughput	+0.03	[-0.04, +0.11]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	+0.00	[-0.14, +0.14]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.00	[-0.14, +0.14]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	-0.00	[-0.14, +0.14]
➖	syslog_loki	ingress throughput	-0.02	[-0.07, +0.03]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.02	[-0.14, +0.09]
➖	enterprise_http_to_http	ingress throughput	-0.18	[-0.26, -0.09]
➖	http_to_s3	ingress throughput	-0.31	[-0.59, -0.04]
➖	socket_to_socket_blackhole	ingress throughput	-0.42	[-0.51, -0.34]
➖	http_to_http_acks	ingress throughput	-0.45	[-1.75, +0.85]
➖	otlp_grpc_to_blackhole	ingress throughput	-0.48	[-0.58, -0.38]
➖	http_elasticsearch	ingress throughput	-0.50	[-0.57, -0.44]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	-0.50	[-0.59, -0.42]
➖	datadog_agent_remap_datadog_logs	ingress throughput	-0.51	[-0.60, -0.41]
➖	syslog_splunk_hec_logs	ingress throughput	-0.55	[-0.62, -0.48]
➖	fluent_elasticsearch	ingress throughput	-0.63	[-1.11, -0.16]
➖	syslog_humio_logs	ingress throughput	-1.45	[-1.56, -1.35]
➖	file_to_blackhole	egress throughput	-1.56	[-4.02, +0.91]

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".