Book Image

Mastering Kubernetes - Fourth Edition

By : Gigi Sayfan
3.3 (3)
Book Image

Mastering Kubernetes - Fourth Edition

3.3 (3)
By: Gigi Sayfan

Overview of this book

The fourth edition of the bestseller Mastering Kubernetes includes the most recent tools and code to enable you to learn the latest features of Kubernetes 1.25. This book contains a thorough exploration of complex concepts and best practices to help you master the skills of designing and deploying large-scale distributed systems on Kubernetes clusters. You’ll learn how to run complex stateless and stateful microservices on Kubernetes, including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backends. In addition, you’ll understand how to utilize serverless computing and service meshes. Further, two new chapters have been added. “Governing Kubernetes” covers the problem of policy management, how admission control addresses it, and how policy engines provide a powerful governance solution. “Running Kubernetes in Production” shows you what it takes to run Kubernetes at scale across multiple cloud providers, multiple geographical regions, and multiple clusters, and it also explains how to handle topics such as upgrades, capacity planning, dealing with cloud provider limits/quotas, and cost management. By the end of this Kubernetes book, you’ll have a strong understanding of, and hands-on experience with, a wide range of Kubernetes capabilities.
Table of Contents (21 chapters)
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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 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

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

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

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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.