Ahmed Bebars, Principal Engineer at the New York Times and CNCF ambassador, recently shared his insights on effectively scaling Kubernetes clusters using KEDA and Karpenter. The discussion emphasized the importance of identifying the specific problem you're trying to solve before implementing any autoscaling solution.
Understanding the Tools: KEDA vs. Karpenter
Karpenter: This tool focuses on node-level scaling, providing significant granularity in managing your Kubernetes fleet. It abstracts away node groups and optimizes node scaling speed by relying on different mechanisms. Karpenter considers factors like pricing and support for spot instances to optimize cost-effectiveness. It also performs bin packing and consolidation to improve resource utilization. However, it's crucial to configure Karpenter correctly and monitor its logs to avoid unexpected node shutdowns.
KEDA: KEDA (Kubernetes Event-driven Autoscaling) is built on top of the Horizontal Pod Autoscaler (HPA) and expands its functionality by introducing more external metrics for scaling. KEDA focuses on scaling containers and pods based on various metrics, such as queue length or custom metrics. It allows scaling applications down to zero when there is no traffic. KEDA's strength lies in its ability to create custom scalers, enabling scaling based on specific application needs.
Key Considerations for Using Autoscalers
Define the problem: Before implementing any autoscaling solution, clearly identify the scaling problem. Determine whether the issue is related to node provisioning or application scaling.
Granularity and Speed: Karpenter offers both granularity and speed in scaling nodes. If your application requires rapid scaling and diverse workload types, Karpenter might be a suitable choice.
Cost Optimization: Karpenter helps in cost optimization by considering various factors such as node types, spot instances, and bin packing.
Metric Selection: Choose the right metrics for your application. While CPU and memory are common metrics, consider other factors like queue length, external metrics, or custom metrics.
Observability: Implement robust monitoring and logging to understand how Karpenter and KEDA are scaling your resources. This helps in identifying and resolving any issues that may arise.
Incremental Approach: Autoscaling is an iterative process. Start with basic scaling strategies and gradually introduce more advanced techniques as needed.
KEDA and Karpenter in Conjunction
KEDA and Karpenter work well together. KEDA manages the scaling of containers and pods, while Karpenter manages the underlying nodes. By combining these tools, you can achieve optimal scaling for both your applications and infrastructure.
Kubernetes vs. Serverless
Kubernetes and serverless architectures have different characteristics. Kubernetes is suitable for applications that require more control over the underlying infrastructure, while serverless is better suited for event-driven and highly fluctuating workloads. It is important to understand your traffic patterns. You can use both in conjunction.
Pitfalls to Avoid
Overly Specific Specifications: Avoid being too specific in Karpenter specifications, as this can lead to scaling failures if the requested resources are unavailable.
Controller Overload: Ensure that your autoscaling controllers have enough resources to handle the scaling demands of your cluster. Monitor the CPU and memory usage of your controllers to prevent them from crashing.
Using Karpenter and Cluster Autoscaler Together: Avoid running Karpenter and Cluster Autoscaler simultaneously, as they can conflict with each other.
Community and Further Learning
Karpenter and KEDA Documentation: Refer to the official documentation for Karpenter and KEDA for detailed information on their features and usage.
CNCF Slack: Join the CNCF Slack channels for Karpenter and KEDA to ask questions and engage with the community.
Kubernetes Community Days: Attend Kubernetes Community Days (KCD) events to connect with other Kubernetes enthusiasts and learn from experts.
KubeCon: Attend KubeCon to learn about the latest trends and technologies in the Kubernetes ecosystem.
By carefully considering these factors and leveraging the power of KEDA and Karpenter, you can effectively scale your Kubernetes clusters and optimize resource utilization.
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