Kubernetes Best Practices for Production

Deploying applications to Kubernetes requires careful planning and adherence to best practices. In this post, we’ll explore the key strategies that will help you run production-ready Kubernetes clusters.

Resource Management

One of the most critical aspects of running Kubernetes in production is proper resource management. Always define resource requests and limits for your containers.

resources:
  requests:
    memory: "256Mi"
    cpu: "250m"
  limits:
    memory: "512Mi"
    cpu: "500m"

Setting appropriate resource limits prevents a single pod from consuming all available resources and ensures fair scheduling across your cluster.

High Availability Strategies

For production workloads, you should always deploy across multiple availability zones. Use node pools with auto-scaling enabled and configure pod disruption budgets to ensure application availability during maintenance events.

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: app-pdb
spec:
  minAvailable: 2
  selector:
    matchLabels:
      app: myapp

Security Best Practices

Security should be a top priority. Implement the principle of least privilege by using RBAC (Role-Based Access Control), run containers as non-root users, and enable network policies to restrict pod-to-pod communication.

Always scan your container images for vulnerabilities and use secrets management solutions for sensitive data. Remember: defense in depth is crucial for production environments.

Health Checks and Monitoring

Configure liveness and readiness probes for your containers. This allows Kubernetes to automatically restart unhealthy pods and route traffic only to ready instances.

livenessProbe:
  httpGet:
    path: /health
    port: 8080
  initialDelaySeconds: 30
  periodSeconds: 10

Combine this with a robust monitoring stack like Prometheus and Grafana to get visibility into cluster performance and application metrics.