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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Handling scarce resources with limits and quotas

With the horizontal pod autoscaler creating pods on the fly, we need to think about managing our resources. Scheduling can easily get out of control, and inefficient use of resources is a real concern. There are several factors, which can interact with each other in subtle ways:

  • Overall cluster capacity
  • Resource granularity per node
  • Division of workloads per namespace
  • Daemon sets
  • Stateful sets
  • Affinity, anti-affinity, taints, and tolerations

First, let’s understand the core issue. The Kubernetes scheduler has to take into account all these factors when it schedules pods. If there are conflicts or a lot of overlapping requirements, then Kubernetes may have a problem finding room to schedule new pods. For example, a very extreme yet simple scenario is that a daemon set runs a pod on every node that requires 50% of the available memory. Now, Kubernetes can’t schedule any other...