Convert kyverno tests jobs in pytest

What does this MR do and why?

Convert our existing kyverno test into pytest.

When a failure is detected, it will create a failed subtest to have a cleaner output than a giant failure

eg:

SUBFAILED(resource='default/fake-failing-report') tests/security/test_kyverno.py::test_policy_reports - Failed: PolicyReport 'default/fake-failing-report' — fail=1 warn=0 error=0; see output/kyverno/policy_reports/default_fake-failing-report.json                                                                                                                          
SUBFAILED(resource='default/fake-failing-report-1') tests/security/test_kyverno.py::test_policy_reports - Failed: PolicyReport 'default/fake-failing-report-1' — fail=1 warn=0 error=0; 
see output/kyverno/policy_reports/default_fake-failing-report-1.json                                                                                                                    
SUBFAILED(resource='default/fake-failing-report-10') tests/security/test_kyverno.py::test_policy_reports - Failed: PolicyReport 'default/fake-failing-report-10' — fail=10 warn=0 error=
0; see output/kyverno/policy_reports/default_fake-failing-report-10.json                                                                                                                

I haven't mark it as "management" cluster only, so we can check at each step of the deployment that we don't have report failure. The test are using mgmt_cluster fixture to always talk to the management.

Test coverage

CI configuration

Below you can choose test deployment variants to run in this MR's CI.

Click to open to CI configuration

Legend:

Icon Meaning Available values
☁️ Infra Provider capd, capo, capm3
🚀 Bootstrap Provider kubeadm (alias kadm), rke2, okd, ck8s
🐧 Node OS ubuntu, suse, na, leapmicro
🛠️ Deployment Options Deployment option list and description
🎬 Pipeline Scenarios Available scenario list and description
🟢 Enabled units Any available units name, by default apply to management and workload cluster. Can be prefixed by mgmt: or wkld: to be applied only to a specific cluster type
🔴 Disabled units Any available units name, by default apply to management and workload cluster. Can be prefixed by mgmt: or wkld: to be applied only to a specific cluster type
🏗️ Target platform Can be used to select specific deployment environment Available platform list and description
⚡ Pipeline control autorun, manual or blocking. Can be used to override global config and start a deployment pipeline the required way
  • 🎬 preview ☁️ capd 🚀 kadm 🐧 ubuntu

  • 🎬 preview ☁️ capo 🚀 rke2 🐧 suse

  • 🎬 preview ☁️ capm3 🚀 rke2 🐧 ubuntu

  • ☁️ capd 🚀 kadm 🛠️ light-deploy 🐧 ubuntu

  • ☁️ capd 🚀 rke2 🛠️ light-deploy 🐧 suse

  • ☁️ capo 🚀 rke2 🐧 suse

  • ☁️ capo 🚀 rke2 🐧 leapmicro

  • ☁️ capo 🚀 kadm 🐧 ubuntu

  • ☁️ capo 🚀 kadm 🐧 ubuntu 🟢 neuvector,mgmt:harbor

  • ☁️ capo 🚀 rke2 🎬 rolling-update 🛠️ ha 🐧 ubuntu

  • ☁️ capo 🚀 kadm 🎬 wkld-k8s-upgrade 🐧 ubuntu

  • ☁️ capo 🚀 rke2 🎬 rolling-update-no-wkld 🛠️ ha 🐧 suse

  • ☁️ capo 🚀 rke2 🎬 sylva-upgrade 🛠️ ha 🐧 ubuntu

  • ☁️ capo 🚀 rke2 🎬 sylva-upgrade-from-1.6.x 🛠️ ha,misc 🐧 ubuntu

  • ☁️ capo 🚀 rke2 🛠️ ha,misc 🐧 ubuntu

  • ☁️ capo 🚀 rke2 🛠️ misc 🐧 ubuntu 🟢 mgmt:harbor 🔴 neuvector

  • ☁️ capo 🚀 rke2 🛠️ ha,misc,openbao 🐧 suse

  • ☁️ capo 🚀 rke2 🐧 suse 🎬 upgrade-from-prev-tag

  • ☁️ capm3 🚀 rke2 🐧 suse

  • ☁️ capm3 🚀 kadm 🐧 ubuntu

  • ☁️ capm3 🚀 ck8s 🐧 ubuntu

  • ☁️ capm3 🚀 kadm 🎬 rolling-update-no-wkld 🛠️ ha,misc 🐧 ubuntu

  • ☁️ capm3 🚀 rke2 🎬 wkld-k8s-upgrade 🛠️ ha 🐧 suse

  • ☁️ capm3 🚀 kadm 🎬 rolling-update 🛠️ ha 🐧 ubuntu

  • ☁️ capm3 🚀 rke2 🎬 upgrade-from-prev-release-branch 🛠️ ha 🐧 suse

  • ☁️ capm3 🚀 rke2 🛠️ misc,ha 🐧 suse

  • ☁️ capm3 🚀 rke2 🎬 sylva-upgrade 🛠️ ha,misc 🐧 suse

  • ☁️ capm3 🚀 kadm 🎬 rolling-update 🛠️ ha 🐧 suse

  • ☁️ capm3 🚀 ck8s 🎬 rolling-update 🛠️ ha 🐧 ubuntu

  • ☁️ capm3 🚀 rke2|okd 🎬 no-update 🐧 ubuntu|na

  • ☁️ capm3 🚀 rke2 🐧 suse 🎬 upgrade-from-release-1.5

  • ☁️ capm3 🚀 rke2 🐧 suse 🎬 upgrade-to-main

Global config for deployment pipelines

  • autorun pipelines
  • allow failure on pipelines
  • record sylvactl events

Notes:

  • Enabling autorun will make deployment pipelines to be run automatically without human interaction
  • Disabling allow failure will make deployment pipelines mandatory for pipeline success.
  • if both autorun and allow failure are disabled, deployment pipelines will need manual triggering but will be blocking the pipeline

Be aware: after configuration change, pipeline is not triggered automatically. Please run it manually (by clicking the run pipeline button in Pipelines tab) or push new code.

Edited by Loic Nicolle

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