chore(websites): Setup preview site workflows

[devindford]

This PR adds the new workflows for building preview sites. There is a workflow for each specific deployment, and a specific github PR comment will show for each individual build. This also removes the old netlify files that are no longer needed. Worth noting the full workflow won't/cant run until the workflows exsist on main. The workflow_run will only trigger if the workflow it is calling exists on the master branch.

This will initially fail due to the app connection not being on so there is no branch sync

[bits-bot]

All committers have signed the CLA.

[netlify]

✅ Deploy Preview for vrl-playground canceled.

Name	Link
🔨 Latest commit	2a47dd9
🔍 Latest deploy log	https://app.netlify.com/sites/vrl-playground/deploys/654033549ca7c20008dbbc30

[netlify]

❌ Deploy Preview for vector-project failed.

Name	Link
🔨 Latest commit	2a47dd9
🔍 Latest deploy log	https://app.netlify.com/sites/vector-project/deploys/65403354869e6c000893d7f5

[github-advanced-security]

check-spelling found more than 10 potential problems in the proposed changes. Check the Files changed tab for more details.

[jszwedko]

Nice! I left a few comments but this approach generally seems sensible to me.

[jszwedko]

Given these workflows need access to secrets, I think they need to be on pull_request_target instead of pull_request.

[jszwedko]

Not a big deal, but given these workflows are almost identical, we could create just one parameterized workflow and call it from each of these site-specific workflows. See https://docs.github.com/en/actions/using-workflows/reusing-workflows

[jszwedko]

Nit: we could pull up the app_id into a workflow environment variable since we refer to it twice.

[datadog-vectordotdev]

Datadog Report

Branch report: devin.ford/WEB-4186
Commit report: bac4a8f

✅ vector: 0 Failed, 0 New Flaky, 2000 Passed, 0 Skipped, 1m 22.33s Wall Time

[devindford]

closing and reopening to test the workflow (it will still fail due to no github app connection right now)

[jszwedko]

Clever! This seems like it should work once my comments below are resolved.

[jszwedko]

Will github.head_ref work here? I'm not actually sure what ref it might refer to in this context (maybe master?). I would think you might need to thread the PR git ref through from the "Call Build Preview" via build artifacts (similar to what is described on https://securitylab.github.com/research/github-actions-preventing-pwn-requests/).

[jszwedko]

Same here, I don't know that github.event.pull_request.number will be populated. You may need to thread it through from the PR-triggered workflow as a build artifact.

[devindford]

closing and reopening to test the workflow (it will still fail due to no github app connection right now)

[devindford]

@jszwedko I think this is ready for a final look over, if there is no more feedback I'll reach out to security and have the app reconnected and then merge this in and open a follow up test PR to test the workflow since it won't run anything but the trigger until it's on master. github docs on workflow_run

[jszwedko]

Great! Thanks for refactoring the workflows.

[jszwedko]

Small nit that I might have used different files for these like:

Suggested change

	echo "PR Number: ${{ github.event.number }}" > ./pr/PR_INFO
	echo "Branch Name: ${{ github.head_ref }}" >> ./pr/PR_INFO
	echo "PR Number: ${{ github.event.number }}" > ./pr/number
	echo "Branch Name: ${{ github.head_ref }}" > ./pr/branch

Just to make extracting the info easier (no grep/sed'ing required).

[github-actions]

Regression Detector Results

Run ID: bd46b8f8-0766-478e-b8da-b7ab4b79f2d1
Baseline: 2bba40a
Comparison: 9d006c7
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
syslog_log2metric_humio_metrics	ingress throughput	+0.89	[+0.78, +1.01]	100.00%
datadog_agent_remap_datadog_logs_acks	ingress throughput	+0.89	[+0.80, +0.98]	100.00%
otlp_http_to_blackhole	ingress throughput	+0.86	[+0.71, +1.01]	100.00%
otlp_grpc_to_blackhole	ingress throughput	+0.71	[+0.61, +0.80]	100.00%
datadog_agent_remap_blackhole_acks	ingress throughput	+0.59	[+0.50, +0.68]	100.00%
syslog_humio_logs	ingress throughput	+0.53	[+0.44, +0.63]	100.00%
fluent_elasticsearch	ingress throughput	+0.49	[+0.04, +0.94]	92.88%
socket_to_socket_blackhole	ingress throughput	+0.48	[+0.39, +0.56]	100.00%
datadog_agent_remap_blackhole	ingress throughput	+0.32	[+0.24, +0.41]	100.00%
syslog_regex_logs2metric_ddmetrics	ingress throughput	+0.19	[+0.12, +0.27]	100.00%
syslog_loki	ingress throughput	+0.13	[+0.10, +0.17]	100.00%
http_to_http_noack	ingress throughput	+0.13	[+0.03, +0.24]	96.21%
http_text_to_http_json	ingress throughput	+0.11	[-0.01, +0.23]	86.91%
splunk_hec_route_s3	ingress throughput	+0.08	[-0.43, +0.59]	19.77%
file_to_blackhole	egress throughput	+0.08	[-2.54, +2.70]	3.86%
http_to_http_json	ingress throughput	+0.04	[-0.04, +0.11]	59.97%
splunk_hec_indexer_ack_blackhole	ingress throughput	+0.00	[-0.14, +0.14]	0.23%
splunk_hec_to_splunk_hec_logs_acks	ingress throughput	-0.00	[-0.14, +0.14]	1.69%
splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.03	[-0.15, +0.08]	38.00%
syslog_splunk_hec_logs	ingress throughput	-0.04	[-0.08, -0.00]	90.74%
enterprise_http_to_http	ingress throughput	-0.06	[-0.11, -0.01]	92.95%
http_to_s3	ingress throughput	-0.13	[-0.40, +0.15]	55.56%
syslog_log2metric_splunk_hec_metrics	ingress throughput	-0.19	[-0.32, -0.06]	98.05%
datadog_agent_remap_datadog_logs	ingress throughput	-0.35	[-0.44, -0.26]	100.00%
http_to_http_acks	ingress throughput	-1.40	[-2.70, -0.10]	92.44%