Run Flux CD E2E with the Kubernetes Agent backend

[vitkyrka]

What does this PR do?

Runs the Flux CD Kind E2E with the Kubernetes Agent backend introduced by #24639, following the pattern established for Argo Rollouts in #24674.

The dd_environment fixture no longer port-forwards from the host; instead it builds endpoints that are reachable from inside the Kind cluster:

• source-controller and notification-controller do have a Kubernetes Service, but that Service only forwards its port 80 to the controller's API port (9090), not to the Prometheus metrics port (8080) — confirmed by inspecting the Endpoints object and by a manual in-cluster wget against the Service DNS name, which times out on :8080. Service DNS therefore cannot be used to reach /metrics for these two controllers.
• helm-controller and kustomize-controller have no Service at all.

Because none of the four controllers' metrics ports are reachable via a Service, all four use their pod's IP directly on port 8080 (the port the shipped allow-scraping NetworkPolicy explicitly opens to cross-namespace ingress, confirming it's the intended scrape path). Each controller is a single-replica Deployment, so the pod IP is unambiguous. The pod IP is looked up once inside setup_fluxcd() — while the Kind cluster is guaranteed to still be up — and cached with datadog_checks.dev._env.save_state/get_state (mirroring get_node_agent() in #24645 for Velero), so ddev env test and ddev env stop, which re-invoke dd_environment in a fresh process, don't need to re-run kubectl against a cluster that may already be gone.

Validation:

• ddev test -fs fluxcd
• ddev --no-interactive test fluxcd (4 passed, 1 E2E skipped)
• ddev env show fluxcd (py3.13)
• ddev env start --dev fluxcd py3.13
• ddev env test --dev fluxcd py3.13 (1 passed, 4 deselected)
• ddev env stop fluxcd py3.13

Motivation

This PR migrates Flux CD to the new backend. Running the Agent inside the same Kind cluster removes the long-lived host-side kubectl port-forward processes without changing the E2E setup, waits, instance shape, assertions, or timeout behavior.

Review checklist (to be filled by reviewers)

• Feature or bugfix MUST have appropriate tests (unit, integration, e2e)
• Add qa/required if this PR needs QA validation, or qa/skip-qa if it does not. Exactly one of the two is required.
• If you need to backport this PR to another branch, you can add the backport/<branch-name> label to the PR and it will automatically open a backport PR once this one is merged

[cit-pr-commenter-54b7da]

evalya-impact-summary

evalya impact analysis

Impact analysis: 0 selected, 0 skipped (of 0 test tasks)
Publish tasks:   0 (always emitted)
Diff (1 file):
  fluxcd/tests/conftest.py

Debug a specific task: evalya plan impact --path <path> --task <task>

Learn more about CI impact filtering

[datadog-datadog-prod-us1-2]

 

🎉 All green!

🧪 All tests passed
❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 37.50%
• Overall Coverage: 87.85% (-0.56%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: b8cbbc9 | Docs | Datadog PR Page | Give us feedback!}

[vitkyrka]

@codex review

[chatgpt-codex-connector]

💡 Codex Review

Here are some automated review suggestions for this pull request.

Reviewed commit: bcda1e9390

ℹ️ About Codex in GitHub

Your team has set up Codex to review pull requests in this repo. Reviews are triggered when you

• Open a pull request for review
• Mark a draft as ready
• Comment "@codex review".

If Codex has suggestions, it will comment; otherwise it will react with 👍.

Codex can also answer questions or update the PR. Try commenting "@codex address that feedback".

[dd-octo-sts]

Validation Report

All 21 validations passed.

Show details

Validation	Description	Status
agent-reqs	Verify check versions match the Agent requirements file	✅
ci	Validate CI configuration and code coverage settings	✅
codeowners	Validate every integration has a CODEOWNERS entry	✅
config	Validate default configuration files against spec.yaml	✅
dep	Verify dependency pins are consistent and Agent-compatible	✅
http	Validate integrations use the HTTP wrapper correctly	✅
imports	Validate check imports do not use deprecated modules	✅
integration-style	Validate check code style conventions	✅
jmx-metrics	Validate JMX metrics definition files and config	✅
labeler	Validate PR labeler config matches integration directories	✅
legacy-signature	Validate no integration uses the legacy Agent check signature	✅
license-headers	Validate Python files have proper license headers	✅
licenses	Validate third-party license attribution list	✅
metadata	Validate metadata.csv metric definitions	✅
models	Validate configuration data models match spec.yaml	✅
openmetrics	Validate OpenMetrics integrations disable the metric limit	✅
package	Validate Python package metadata and naming	✅
qa-label	Validate the pull request declares whether it needs QA for the next Agent release	✅
readmes	Validate README files have required sections	✅
saved-views	Validate saved view JSON file structure and fields	✅
version	Validate version consistency between package and changelog	✅

View full run