[SVLS-5506] Update serverless-init Cloud Run service to support cloud run functions

[nina9753]

What does this PR do?

Update serverless-init Cloud Run service to support Cloud Run functions This adds the other environmental variables returned by cloud run and cloud run functions into the tag array. K_CONFIGURATION, FUNCTION_SIGNATURE_TYPE, FUNCTION_TARGET This also updates the metric prefix to be gcp.runfunction and the origin is cloudrunfunction

Motivation

Google Cloud Run Function now supports sidecars under cloud run

Additional Notes

currently blocked by #29307

Possible Drawbacks / Trade-offs

Currently, this approach will work for all runtimes except Go. Which does not have any out of the box environmental variables we could use to mark a service as a cloud function source deploy. We will need to force the customer to add FUNCTION_TARGET to the variables during setup so that in datadog everything is tagged correctly

Describe how to test/QA your changes

added a new test case TestGetCloudRunFunctionTagsWithEnvironmentVariables you can also run all test locally by running go test -tags "test" -v ./cmd/serverless-init/...

[pr-commenter[bot]]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=43552879 --os-family=ubuntu

Note: This applies to commit 14a83075

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: b04c291b-9787-409a-a28d-fba1c0c2ad47 Metrics dashboard Target profiles

Baseline: d89c5c44d6de36137772d80dd97d18bf0f076cfc Comparison: 14a830755c6fb77321ad7d59af9b2cae4dbbe392

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	trials	links
➖	tcp_syslog_to_blackhole	ingress throughput	+3.15	[-9.84, +16.15]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.78	[+0.98, +2.57]	1	Logs
➖	file_tree	memory utilization	+0.09	[+0.04, +0.14]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]	1	Logs
➖	idle	memory utilization	-0.12	[-0.15, -0.08]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.56	[-1.37, +0.26]	1	Logs
➖	basic_py_check	% cpu utilization	-1.04	[-3.92, +1.84]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-1.60	[-3.85, +0.64]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
❌	idle	memory_usage	1/10

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".