Kubernetes API Priority and Fairness

Kubernetes API Priority and Fairness

This post is about the new Kubernetes API Priority and Fairness (APF) feature. I will like to share with you what I have learned and show you how to define policies to prioritize and throttle inbound requests to the Kubernetes API server. Then I will also go over some metrics and debugging endpoints that you can use to determine if APF is affecting your controllers.

This post is about the new Kubernetes API Priority and Fairness (APF) feature. I will like to share with you what I have learned and show you how to define policies to prioritize and throttle inbound requests to the Kubernetes API server. Then I will also go over some metrics and debugging endpoints that you can use to determine if APF is affecting your controllers.

🆕 APF is enabled by default in Kubernetes 1.20, as a beta feature. For earlier versions of Kubernetes, it can be [enabled_](https://kubernetes.io/docs/concepts/cluster-administration/flow-control/#enabling-disabling-api-priority-and-fairness) via the APIPriorityAndFairness _feature gate.

What is APF

Prior to APF, the API server uses the--max-requests-inflight and --max-mutating-requests-inflight command-line flags to regulate the volume of inbound requests. The only distinction that this implementation can make on the requests is whether the requests are mutating ones or not. They can’t, for example, ensure that lower priority traffic doesn’t overwhelm the critical ones as described in this example issue.

APF provides a flow control mechanism to ensure that the API server can throttle requests while ensuring fairness. It allows platform owners to define API-level policies to classify inbound requests into different priorities and flows.

rate-limiting throttling software-engineering kubernetes k8s

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