Running Kubernetes in production, especially Amazon EKS, is more than just deploying applications and scaling nodes. A production-ready Kubernetes setup requires careful thought about reliability, scalability, security, cost optimization, and resilience to failures.

In this article, we explore must-have configurations for a production-grade EKS cluster. I'll incorporate critical best practices, including cluster autoscaling, standardized instances, pod disruption budgets, and more, to ensure your cluster runs efficiently and reliably.

1. Use Cluster Autoscaler with Priority Expander

The Cluster Autoscaler helps dynamically scale your Kubernetes nodes based on pod demand. However, its default behavior may not always choose the ideal node groups. The Priority Expander solves this by allowing you to assign priority to different node groups explicitly.

Why is this essential?

• Prioritize cheaper spot instances for workloads that can handle interruptions.
• Reserve on-demand instances for critical workloads requiring stability.
• This setup achieves both cost efficiency and high availability.

2. Standardize Instance Sizes for Workloads

Always choose standardized instance sizes for similar workloads. Avoid mixing drastically different instance types (small and extra-large instances) for similar workloads.

Why is this important?

• Reduces complexity in scheduling and autoscaling decisions.
• Ensures efficient utilization of resources.
• Simplifies operational overhead, cost tracking, and budgeting.

Recommended approach: Use a balanced instance type like 2xlarge—not too small (avoiding frequent node churn) and not too large (avoiding wastage from idle resources).

3. Implement Pod Disruption Budgets (PDB) Everywhere

Every workload, especially mission-critical deployments, should have clearly defined Pod Disruption Budgets. PDBs define the minimum number of pods required to be running during disruptions such as node upgrades or scaling activities.

Example:

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

Benefits:

• Ensures high availability of your services.
• Prevents downtime during node maintenance and cluster scaling.

4. Protect Critical Workloads on Dedicated On-Demand Nodes

Some workloads simply cannot afford interruptions or instability (like databases or critical middleware). To address this, deploy a dedicated on-demand node group with a label and taint:

kubectl taint nodes critical-node dedicated=critical:NoSchedule

Then configure critical workload pods to tolerate this taint:

nodeSelector:
  node-role.kubernetes.io/critical: "true"
tolerations:
- key: "dedicated"
  operator: "Equal"
  value: "critical"
  effect: "NoSchedule"

Why does this matter?

• Guarantees that critical workloads always run on stable, highly available infrastructure.
• Reduces the risk associated with spot instance interruptions.

5. Set Horizontal Pod Autoscaler (HPA) for Every Workload

Automating workload scaling based on demand is crucial. Horizontal Pod Autoscaler (HPA) ensures workloads scale up or down based on real-time metrics, such as CPU, memory, or custom metrics from Prometheus.

Always define HPAs for all significant workloads:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: web-app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

Benefits:

• Cost efficiency through automated scaling.
• Reduces downtime risks by dynamically adjusting capacity.
• Simplifies resource management at scale.

6. Plan for Pod Disruptions Explicitly

Beyond defining Pod Disruption Budgets, it's essential to make disruption planning a core operational practice:

• Document workloads sensitive to disruptions.
• Regularly review and test pod eviction and replacement processes.
• Include disruption handling as a part of operational playbooks and cluster maintenance SOPs.

This ensures your team is proactive rather than reactive, avoiding outages due to routine maintenance or unexpected disruptions.

7. Use a Cluster Overprovisioner for Faster Scaling

Cluster Autoscaler might take up to a minute to provision a new node. To handle sudden spikes efficiently, deploy an overprovisioner. This involves deploying small pause pods at low resource usage, spread evenly across nodes.

Example Configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: cluster-overprovisioner
spec:
  replicas: 10
  selector:
    matchLabels:
      app: pause-pod
  template:
    metadata:
      labels:
        app: pause-pod
    spec:
      priorityClassName: overprovisioning
      containers:
      - name: pause
        image: k8s.gcr.io/pause
        resources:
          requests:
            cpu: "100m"
            memory: "100Mi"
      topologySpreadConstraints:
      - maxSkew: 1
        topologyKey: topology.kubernetes.io/zone
        whenUnsatisfiable: ScheduleAnyway
        labelSelector:
          matchLabels:
            app: pause-pod

Benefits:

• Ensures quick scheduling of new pods, reducing delays during load spikes.
• Improves responsiveness and stability under sudden traffic increases.

Additional Production Best Practices

Beyond the critical points listed above, here are additional essential practices to consider for your production EKS environment:

• Resource Requests and Limits: Avoid resource starvation and unexpected pod evictions by clearly defining CPU and memory requests and limits.
• Readiness and Liveness Probes: Ensure workloads restart gracefully and are reliably marked ready.
• RBAC and IAM Integration: Enforce strict role-based access controls (RBAC) and IAM policies to enhance security.
• Logging and Monitoring: Integrate comprehensive monitoring and alerting solutions (Prometheus, Loki, Grafana, Alertmanager).
• Network Optimization: Optimize your CNI settings, particularly if you're using the Amazon VPC CNI, to avoid IP exhaustion and enhance network performance.

Conclusion

Establishing a production-grade EKS cluster involves careful planning and disciplined implementation of these best practices. Each configuration, from autoscaling strategies and node management to disruption planning and overprovisioning, contributes directly to the resilience, efficiency, and reliability of your Kubernetes workloads.

Follow these guidelines to confidently run robust, efficient, and scalable Kubernetes infrastructure in production environments.