feat(tools/k8s-discovery): complete resource discovery coverage, multi-container workload preservation, and generic cluster support

[utkarsh-devops]

PR Title

feat(tools/k8s-discovery): complete resource discovery coverage, multi-container workload preservation, and generic cluster support

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PR Description

Summary of Changes

This Pull Request enhances tools/k8s-discovery to eliminate data truncation, expand Kubernetes resource inventory coverage, and add generic cluster support across AWS EKS, Azure AKS, Google GKE, and local/on-premise environments.

Key Enhancements:

1. Expanded Resource Inventory (k8s_resources.py):

   • StorageClasses: Added get_storageclass_details() to collect provisioners, reclaim policies, volume binding modes, expansion flags, parameters, and annotations.
   • Cluster-Wide RBAC: Added get_cluster_role_details() and get_cluster_role_binding_details() for cluster-scoped access review.
   • CRDs & Custom Resources: Added get_crd_details() and get_custom_resource_details() to automatically inspect installed CRDs (e.g., Karpenter, cert-manager, Istio, AWS LB Controller) and list custom resource instances.
   • Identity & Annotations: Preserved metadata.annotations on ServiceAccounts (enabling EKS IRSA eks.amazonaws.com/role-arn tracking), Services (AWS Load Balancer controller annotations), Namespaces, and RBAC roles.
   • Workload & Security Contexts: Extracted container privileged flags, pod service_account_name, host_network, and owner_references.
   • Persistent Volumes: Extracted volume_handle, ebs_volume_id, csi_driver, volume_mode, and node_affinity.

2. Data Preservation & Relational CSV Export (common.py):

   • Multi-Container Workload Flattening: Refactored _flatten_workloads() to iterate over 100% of workload containers, preventing multi-container pod specs from being truncated.
   • DaemonSet Replicas: Extracted desired_scheduled / current_scheduled metrics when standard replicas count is unpopulated.
   • Header Retention for Empty CSVs: Implemented DEFAULT_CSV_HEADERS mapping to ensure all 26 CSV exports write complete header rows even when resource counts are zero.

3. Cloud & Generic Cluster Enhancements (eks_discovery.py, generic_discovery.py, main.py):

   • EKS Managed Add-ons: Added _get_addons() to inspect EKS add-ons, versions, health status, and IAM roles.
   • Generic K8s Support: Enabled standard kubeconfig scanning via python main.py generic for MicroK8s, Minikube, K3s, Rancher, and on-prem clusters.

4. Testing & Documentation (test_k8s_discovery.py, README.md):

   • Added unit tests verifying multi-container preservation, DaemonSet replica handling, IRSA annotation output, and deterministic empty-file CSV header generation.
   • Updated README.md with complete documentation for all 26 CSV export files, setup prerequisites, and CLI sub-commands (aws, azure, gke, generic).

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Verification & Testing

• Syntax & Compilation: Verified code syntax and AST parsing (python3 -m py_compile).
• Unit Tests: Executed unit test suite covering container flattening and CSV exporter logic:

  python3 test_k8s_discovery.py

  Output:

  SUCCESS: test_flatten_workloads_multi_container passed!
  SUCCESS: test_save_to_csvs_and_empty_headers passed!
  

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PR Checklist

• PR is not a duplicate.
• Code conforms to Google Python Style Guide.
• Unit tests added and passing cleanly.
• Accompanying README.md updated with full usage instructions and output schema tables.
• License headers and files verified in accordance with repository standards.