Benefits of Karpenter: Simplifying Kubernetes Cluster Autoscaling

Karpenter is an open-source project that simplifies cluster autoscaling for Kubernetes. It automatically provisions new nodes in response to unschedulable pods, eliminating the need for manual configuration of cluster size. This blog post explores the benefits of Karpenter and dives into best practices for using it effectively.

Why Use Karpenter?

Traditionally, Kubernetes users relied on tools like AWS EC2 Auto Scaling groups and the Cluster Autoscaler to manage cluster size. Karpenter offers several advantages over these solutions:

• Simplified Node Management: Karpenter eliminates the need for managing dozens of individual node groups, offering a more centralized approach to provisioning diverse node configurations.
• Improved Scheduling: With quicker node provisioning and pod scheduling, Karpenter enhances cluster responsiveness.
• Reduced Complexity: Karpenter decouples cluster scaling from specific cloud provider abstractions, making it more flexible and Kubernetes-centric.

Karpenter Best Practices

Here are some key recommendations to optimize your use of Karpenter:

• Target Karpenter for Workloads with Dynamic Needs: Leverage Karpenter for clusters with workloads experiencing fluctuating resource demands or diverse compute requirements. For static workloads, consider Managed Node Groups or Autoscaling Groups.
• Alternatives for Immature Features: If specific features you require are still under development in Karpenter, explore other autoscaling projects as a temporary solution.

Karpenter Controller Deployment

• Deployment Options: Deploy the Karpenter controller using a Helm chart. The chart installs the controller and a webhook pod as a Deployment.
• Minimum Requirements: Run the controller on EKS Fargate or a worker node belonging to a node group. A minimum of one small node group with at least one worker node is recommended. Alternatively, deploy the pods on EKS Fargate by creating a dedicated Fargate profile for the karpenter namespace.

NodePool Best Practices

• Multiple NodePools: Create separate NodePools for different teams or workloads requiring distinct OS versions, instance types, or taints.
• NodePool Configuration:
  • Mutual Exclusion or Weighting: Configure NodePools to be either mutually exclusive or weighted for consistent scheduling behavior.
  • Timers for Node Deletion: Utilize timers (TTL) to automatically delete idle nodes or those exceeding a set expiration time.
  • Instance Type Constraints: When using Spot Instances, avoid overly restricting the instance types Karpenter can provision. This allows Karpenter to optimize EC2’s Price-Capacity Optimized allocation strategy and secure the most cost-effective instances.

Pod Scheduling Best Practices

• High Availability: Adhere to EKS best practices for high availability. Utilize Topology Spread within Karpenter to distribute pods across nodes and availability zones. Implement Disruption Budgets to define minimum available pods during eviction or deletion events.
• Layered Constraints: Leverage Karpenter’s layered constraint model to create complex NodePool and pod deployment constraints for optimal pod scheduling. Consider factors like:
  • Availability Zone targeting using node selectors for applications requiring communication with specific EC2 instances.
  • Hardware requirements by specifying pod resource requests for features like GPUs.
• Resource Limits and Billing Alarms:
  • Set resource limits within Karpenter to define the maximum compute resources a NodePool can provision. This helps manage costs alongside billing alarms that trigger notifications when spend exceeds defined thresholds.
  • Explore Cost Anomaly Detection, a feature within AWS Cost Management, to identify unusual spending patterns.
• Disruption Prevention: Use the karpenter.sh/do-not-disrupt annotation on pods to prevent Karpenter from deprovisioning nodes hosting critical applications or long-running jobs.
• Resource Requests and Limits:
  • Configure requests and limits for all non-CPU resources, particularly when using consolidation. This ensures accurate resource allocation and prevents OOM (Out-of-Memory) situations.
  • Utilize LimitRanges to establish default resource requests and limits for namespaces where pods might not specify them explicitly.
• Accurate Resource Requests: Provide accurate resource requests for all workloads to optimize node provisioning based on actual requirements. This is especially crucial for the consolidation feature.

CoreDNS Considerations

For reliable CoreDNS operation with Karpenter’s dynamic node provisioning, configure CoreDNS pods with:

• coredns-lameduck-duration to prevent directing queries to terminated pods.
• Readiness probes to ensure pods are healthy before receiving traffic.

Karpenter Blueprints

Karpenter Blueprints is a valuable resource repository showcasing common workload scenarios configured with best practices. It offers pre-built configurations for creating an EKS cluster with Karpenter and testing various blueprints. You can even combine these blueprints to tailor a solution for your specific needs.

Advanced Considerations

• Node Drain and Eviction:
  • Utilize Karpenter’s drain command to gracefully evict pods before draining a node for maintenance or updates.
  • Consider deploying a drain controller alongside Karpenter to automate the draining process based on specific conditions.
• Custom User Data and AMIs:
  • While Karpenter generally discourages custom launch templates, you can incorporate custom user data scripts for specific node configuration needs.
  • For complex configurations beyond user data, leverage AWS CloudFormation or Terraform to provision custom AMIs (Amazon Machine Images) and reference them within your Karpenter provisioner configuration.
• Integration with GitOps Tools:
  • Karpenter integrates seamlessly with GitOps tools like ArgoCD and Flux for infrastructure as code (IaC) deployments.
  • Define your cluster configuration and NodePool specifications in Git repositories, ensuring version control and easy rollback capabilities.

Monitoring and Observability

• Node Health and Resource Utilization: Employ a monitoring solution like Prometheus and Grafana to track node health, resource utilization, and Karpenter events.
• Karpenter Metrics: Utilize the built-in Karpenter metrics endpoint to gather detailed information on provisioning activities, node status, and errors.

Conclusion

Karpenter is a powerful tool for automating Kubernetes cluster scaling. By following these best practices and exploring advanced configurations, you can optimize your cluster for cost, performance, and operational efficiency. Remember, Karpenter is an evolving project, so stay up-to-date with the latest features and best practices for optimal utilization.