Kubernetes CPU Requests and Limits: A Deep Dive

Understanding CPU requests and limits is essential for managing Kubernetes workloads effectively. This guide covers everything you need to know about CPU resource management.

What are CPU Requests and Limits?

CPU Requests

CPU requests specify the minimum amount of CPU resources a container is guaranteed to receive. The scheduler uses requests to decide which node to place the pod on.

CPU Limits

CPU limits specify the maximum amount of CPU resources a container can use. If a container exceeds its limit, it will be throttled.

How CPU Resources Work

Requests and Scheduling

The Kubernetes scheduler uses CPU requests to determine if a node has enough resources to schedule a pod. It ensures that the sum of all CPU requests on a node doesn't exceed the node's capacity.

Limits and Throttling

When a container exceeds its CPU limit, the Linux kernel throttles the process by limiting its CPU time. This can cause performance degradation but prevents the container from consuming all available CPU.

Best Practices

1. Set Appropriate Requests

Set CPU requests based on your application's baseline CPU usage. This helps the scheduler make better placement decisions.

2. Set Reasonable Limits

Set CPU limits that allow for peak usage while preventing any single container from consuming all available CPU.

3. Monitor CPU Usage

Use monitoring tools to track CPU usage and adjust requests and limits based on observed patterns.

4. Use Horizontal Pod Autoscaling

HPA can scale your pods based on CPU usage, helping you handle varying loads efficiently.

Common Mistakes

1. Not Setting Requests

Without requests, the scheduler can't make informed decisions about pod placement.

2. Setting Requests Equal to Limits

This prevents your application from taking advantage of available CPU resources during low-load periods.

3. Setting Limits Too Low

Limits that are too low can cause performance issues and throttling.

4. Ignoring CPU Throttling

CPU throttling can cause significant performance degradation, so it's important to monitor and address it.

Conclusion

Proper CPU resource management is crucial for Kubernetes workloads. By understanding requests and limits and following best practices, you can ensure your applications have the resources they need while maintaining cluster efficiency.