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Mastering Kubernetes

You're reading from   Mastering Kubernetes Level up your container orchestration skills with Kubernetes to build, run, secure, and observe large-scale distributed apps

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781839211256
Length 642 pages
Edition 3rd Edition
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Author (1):
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Gigi Sayfan Gigi Sayfan
Author Profile Icon Gigi Sayfan
Gigi Sayfan
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Table of Contents (19) Chapters Close

Preface 1. Understanding Kubernetes Architecture FREE CHAPTER 2. Creating Kubernetes Clusters 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 Advanced Networking 11. Running Kubernetes on Multiple Clouds and Cluster Federation 12. Serverless Computing on Kubernetes 13. Monitoring Kubernetes Clusters 14. Utilizing Service Meshes 15. Extending Kubernetes 16. The Future of Kubernetes 17. Other Books You May Enjoy
18. Index

Comparing Minikube, KinD, and k3d

Minikube is the official local Kubernetes release. It is part of Kubernetes; it's very mature and very full featured. That said, it requires a VM and is both slow to install and to start. It can also 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 feature that you need that is not available in either KinD or k3d.

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 HA clusters with multiple control-plane nodes. It is also designed to be used as a library, which I don't find as 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.

k3d is the clear winner for me. It's lightning fast, supports multiple clusters, and supports multiple worker nodes per cluster. It's also easy to stop and start clusters without losing state.

Alright. Let's take a look at the cloud.

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