kubernetes-operator-dev

name	kubernetes-operator-dev
description	Expert in building Kubernetes Operators using Python (Kopf) and Go (Kubebuilder). Covers CRD design, reconciliation loops, event handling, and production deployment patterns.
version	1.0.0
tags	["kubernetes","operator","python","go","kopf","kubebuilder","devops"]

Kubernetes Operator Dev

Overview

This skill provides expert guidance on designing, building, and deploying Kubernetes Operators. It focuses on the Operator Pattern to automate complex application lifecycle management.

Primary Frameworks:

• Python: Kopf (Kubernetes Operator Pythonic Framework) - Best for data science, AI/ML workloads, and Python teams.
• Go: Kubebuilder / Operator SDK - Best for high-performance, core infrastructure operators.

Core Capabilities

1. CRD Design: Modeling custom resources (APIs) with OpenAPI v3 schemas.
2. Reconciliation: Implementing idempotent loops to bring current state to desired state.
3. Event Handling: Reacting to Create/Update/Delete events on resources.
4. Finalizers: Ensuring clean resource cleanup and deletion logic.
5. Testing: Unit testing handlers and end-to-end testing with Kind/Minikube.

Python Operator Workflow (Kopf)

1. Project Structure

my-operator/
├── deploy/
│   ├── crd.yaml          # CustomResourceDefinition
│   ├── rbac.yaml         # ServiceAccount, Role, RoleBinding
│   └── operator.yaml     # Deployment
├── src/
│   └── handlers.py       # Python logic
├── Dockerfile
└── requirements.txt      # kopf, kubernetes

2. Basic Handler Pattern

import kopf
import kubernetes.client as k8s

@kopf.on.create('my-group', 'v1', 'mycustomresources')
def create_fn(spec, name, namespace, logger, **kwargs):
    """
    Called when a new MyCustomResource is created.
    """
    size = spec.get('size', 1)
    logger.info(f"Creating resource {name} with size {size}")

    # Logic to create child resources (e.g., Pods, Services)
    api = k8s.CoreV1Api()
    # ... implementation ...

    return {'message': 'created', 'time': kopf.now()}

3. Idempotency & Error Handling

• Idempotency: Handlers must be safe to run multiple times. Check if a child resource exists before creating it.
• Retries: Raise kopf.TemporaryError for transient failures (e.g., network issues) to trigger a retry with backoff.
• Permanent Errors: Raise kopf.PermanentError to stop retries for invalid configuration.

Best Practices

• Status Subresource: Always use the status subresource to report observation (e.g., Ready, Phase, Conditions). Do not update spec.
• Owner References: Set ownerReferences on child resources so they are garbage collected when the parent CR is deleted.
• RBAC: Grant only the necessary permissions (Least Privilege).
• Structured Logging: Use the provided logger to ensure logs are JSON-formatted and traceable.
• Namespace Scoping: Decide if your operator is namespaced or cluster-wide.

Deployment Checklist

1. CRD Registered: Apply crd.yaml first.
2. RBAC Configured: Ensure the ServiceAccount has permissions to watch/list/update the CRs and manage child resources.
3. Image Built: Build multi-arch image (amd64/arm64) for production.
4. Liveness/Readiness: Configure probes in the Deployment.
5. Monitoring: Expose metrics (Prometheus) if needed.

Common Commands

• Run locally (dev): kopf run src/handlers.py --verbose
• Build image: docker build -t my-operator:v1 .
• Apply manifest: kubectl apply -f deploy/
• View logs: kubectl logs -l app=my-operator -f