Book Image

The KCNA Book

By : Nigel Poulton
Book Image

The KCNA Book

By: Nigel Poulton

Overview of this book

There is a huge benefit in building small, specialized, single-purpose apps that can self-heal, auto scale, and update regularly without needing downtime. Kubernetes and cloud-native technologies come in handy in building such apps. Possessing the knowledge and skills to leverage Kubernetes can positively enhance possibilities in favor of architects who specialize in cloud-native microservices applications. ‘The KCNA Book’ is designed to help those working in technology with a passion to become certified in the Kubernetes and Cloud-Native Associate Exam. You will learn about containerization, microservices, and cloud-native architecture. You will learn about Kubernetes fundamentals and container orchestration. The book also sheds light on cloud-native application delivery and observability. It focuses on the KCNA exam domains and competencies, which can be applied to the sample test included in the book. Put your knowledge to the test and enhance your skills with the all-encompassing topic coverage. Upon completion, you will begin your journey to get the best roles, projects, and organizations with this exam-oriented book.
Table of Contents (12 chapters)
9
8: Sample test
Appendix B: Sample Test answers

Scheduling

Kubernetes has a built-in scheduler that runs as part of the control plane. It uses advanced logic to schedule pods to the right worker nodes.

Scheduling starts when Kubernetes is asked to run a new pod. This might be you sending a new manifest file to the API server that asks for a new pod, it might be the result of an autoscaling event, or it might even be a self-healing action replacing a failed pod. Either way, as soon as a new pod is requested, it goes into the pending state while the scheduler picks the best node to run it. As soon as a node is identified the pod is scheduled. However, if the scheduler can’t find a suitable node, the pod will stay pending.

Consider the following example. You’re running a Kubernetes cluster with 6 worker nodes and all of your pods have been configured with resource requests and resource limits – resource requests tell the scheduler the minimum amount of CPU and memory a container needs in order to run, whereas...