feat(configurable shutdown duration): make shutdown duration configurable

[DominicBurkart]

We want to make the graceful shutdown period configurable instead of hardcoding it to sixty seconds.

Issues:
#9042 Remove 60s hard cutoff period for graceful shutdowns
#12831 Want to adjust graceful shutdown time

This is my first PR in vector, not sure if this is the correct approach:

• are the ergonomics (-1 for no timeout, 0+ for timeout durations) good?
• any test recommendations beyond manual testing?

[bits-bot]

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[datadog-vectordotdev]

Datadog Report

Branch report: dominic.burkart/configurable-shutdown-duration
Commit report: 33c9059

✅ vector: 0 Failed, 0 New Flaky, 1897 Passed, 0 Skipped, 1m 33.55s Wall Time

[neuronull]

any test recommendations beyond manual testing?

Note in the below comments, I'm intentionally adding a space between shut <> down to not trigger the CI 😆

We have an integration test for shut down, that would be good to run.

Locally it can be run from cargo vdev int test shut down

It can also be triggered manually in CI on a PR by adding a PR comment with /ci-run-integration <integration_name> , though I'd recommend for integration tests, running them locally is preferred so as to not use CI resources... the integration tests all run in containers so as long as you have docker/podman I think should be g2g.

[neuronull]

Note in the below comments, I'm intentionally adding a space between shut <> down to not trigger the CI 😆

heh, it ended up triggering all of the workflows... I see why it did that and I think I can improve the detection logic, so this was a useful exercise... plus you'll get the results of the shutdown int test.

[lukesteensen]

Well done, this looks good! I like where we ended up with the arg group and no magic values.

[jszwedko]

One thing I just realized is that we need to add this to the docs 😄

I believe it should go in https://github.com/vectordotdev/vector/blob/master/website/cue/reference/cli.cue . You can see the other options there as a reference. cue has a bit of a learning curve so let me know if you have trouble with it.

You can also see the README in https://github.com/vectordotdev/vector/tree/master/website for how to run the docs site locally if you want to see it rendered. Netlify will also build a branch preview for the PR.

[DominicBurkart]

One thing I just realized is that we need to add this to the docs 😄

Ah thanks! Added docs (preview link). This is how the changes are displayed:

[datadog-vectordotdev]

Datadog Report

Branch report: dominic.burkart/configurable-shutdown-duration
Commit report: f33638c

✅ vector: 0 Failed, 0 New Flaky, 1897 Passed, 0 Skipped, 1m 20.35s Wall Time

[jszwedko]

Looks great!

[jszwedko]

I think this is ready to merge whenever you are ready 🎉 Feel free to click the button.

[github-actions]

Regression Detector Results

Run ID: 096ef1d2-1ec4-444d-a699-c25ad42b05ff
Baseline: 7a4f1f7
Comparison: 23ed0e3
Total vector CPUs: 7

Explanation

A regression test is an integrated performance test for vector in a repeatable rig, with varying configuration for vector. What follows is a statistical summary of a brief vector run for each configuration across SHAs given above. The goal of these tests are to determine quickly if vector performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

1. The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.

2. Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
file_to_blackhole	egress throughput	+2.48	[-1.43, +6.39]	58.31%
http_text_to_http_json	ingress throughput	+2.46	[+2.37, +2.54]	100.00%
otlp_grpc_to_blackhole	ingress throughput	+1.89	[+1.78, +2.01]	100.00%
http_to_http_json	ingress throughput	+0.74	[+0.68, +0.80]	100.00%
syslog_splunk_hec_logs	ingress throughput	+0.58	[+0.52, +0.64]	100.00%
http_to_http_acks	ingress throughput	+0.55	[-0.67, +1.78]	43.57%
datadog_agent_remap_datadog_logs_acks	ingress throughput	+0.22	[+0.13, +0.30]	99.81%
datadog_agent_remap_blackhole_acks	ingress throughput	+0.13	[+0.06, +0.20]	97.83%
enterprise_http_to_http	ingress throughput	+0.03	[-0.00, +0.06]	77.65%
splunk_hec_indexer_ack_blackhole	ingress throughput	+0.02	[-0.03, +0.06]	38.98%
splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.01	[-0.06, +0.08]	12.17%
fluent_elasticsearch	ingress throughput	-0.00	[-0.00, +0.00]	9.85%
splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.00	[-0.05, +0.04]	3.64%
http_to_http_noack	ingress throughput	-0.04	[-0.10, +0.01]	65.98%
socket_to_socket_blackhole	ingress throughput	-0.33	[-0.38, -0.27]	100.00%
splunk_hec_route_s3	ingress throughput	-0.55	[-0.69, -0.42]	100.00%
otlp_http_to_blackhole	ingress throughput	-0.60	[-0.79, -0.41]	99.99%
syslog_log2metric_splunk_hec_metrics	ingress throughput	-0.81	[-0.89, -0.73]	100.00%
datadog_agent_remap_blackhole	ingress throughput	-1.12	[-1.22, -1.02]	100.00%
syslog_humio_logs	ingress throughput	-1.39	[-1.46, -1.31]	100.00%
syslog_loki	ingress throughput	-1.50	[-1.60, -1.40]	100.00%
syslog_log2metric_humio_metrics	ingress throughput	-1.97	[-2.05, -1.89]	100.00%
datadog_agent_remap_datadog_logs	ingress throughput	-2.23	[-2.35, -2.10]	100.00%
syslog_regex_logs2metric_ddmetrics	ingress throughput	-4.90	[-5.08, -4.72]	100.00%