You're reading from Mastering Kubernetes Dive into Kubernetes and learn how to create and operate world-class cloud-native systems

Product type Paperback

Published in Jun 2023

Publisher Packt

ISBN-13 9781804611395

Length 746 pages

Edition 4th Edition

Tools

Kubernetes

Concepts

Containerization

Author (1):

Gigi Sayfan

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Table of Contents (21) Chapters

Preface

1. Understanding Kubernetes Architecture

2. Creating Kubernetes Clusters FREE CHAPTER

3. High Availability and Reliability

4. Securing Kubernetes

5. Using Kubernetes Resources in Practice

6. Managing Storage

7. Running Stateful Applications with Kubernetes

8. Deploying and Updating Applications

9. Packaging Applications

10. Exploring Kubernetes Networking

11. Running Kubernetes on Multiple Clusters

12. Serverless Computing on Kubernetes

13. Monitoring Kubernetes Clusters

14. Utilizing Service Meshes

15. Extending Kubernetes

16. Governing Kubernetes

17. Running Kubernetes in Production

18. The Future of Kubernetes

19. Other Books You May Enjoy

20. Index

Comparing Minikube, KinD, and k3d

Minikube is an official local Kubernetes release. It’s very mature and very full-featured. That said, it requires a VM and is both slow to install and to start. It also can get into trouble with networking at arbitrary times, and sometimes the only remedy is deleting the cluster and rebooting. Also, minikube supports a single node only. I suggest using Minikube only if it supports some features that you need that are not available in either KinD or k3d. See https://minikube.sigs.k8s.io/ for more info.

KinD is much faster than Minikube and is used for Kubernetes conformance tests, so by definition, it is a conformant Kubernetes distribution. It is the only local cluster solution that provides an HA cluster with multiple control plane nodes. It is also designed to be used as a library, which I don’t find a big attraction because it is very easy to automate CLIs from code. The main downside of KinD for local development is that it is ephemeral. I recommend using KinD if you contribute to Kubernetes itself and want to test against it. See https://kind.sigs.k8s.io/.

K3d is the clear winner for me. Lightning fast, and supports multiple clusters and multiple worker nodes per cluster. Easy to stop and start clusters without losing state. See https://k3d.io/.

Honorable mention – Rancher Desktop Kubernetes cluster

I use Rancher Desktop as my Docker Engine provider, but it also comes with a built-in Kubernetes cluster. You don’t get to customize it and you can’t have multiple clusters or even multiple nodes in the same cluster. But, if all you need is a local single-node Kubernetes cluster to play with, then the rancher-desktop cluster is there for you.

To use this cluster, type:

$ kubectl config use-context rancher-desktop
Switched to context "rancher-desktop".

You can decide how many resources you allocate to its node, which is important if you try to deploy a lot of workloads on it because you get just the one node.

Graphical user interface, application

Description automatically generated

Figure 2.6: Rancher Desktop – Kubernetes settings

In this section, we covered creating Kubernetes clusters locally using Minikube, KinD, and K3d. In the next section, we will look at creating clusters in the cloud.