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)
19
Other Books You May Enjoy
20
Index

Additional extension points

There are some additional extension points that don’t fit into the categories we have discussed so far.

Providing custom metrics for horizontal pod autoscaling

Prior to Kubernetes 1.6, custom metrics were implemented as a Heapster model. In Kubernetes 1.6, new custom metrics APIs landed and matured gradually. As of Kubernetes 1.9, they are enabled by default. As you may recall, Keda (https://keda.sh) is a project that focuses on custom metrics for autoscaling. However, if for some reason Keda doesn’t meet your needs, you can implement your own custom metrics. Custom metrics rely on API aggregation. The recommended path is to start with the custom metrics API server boilerplate, available here: https://github.com/kubernetes-sigs/custom-metrics-apiserver.

Then, you can implement the CustomMetricsProvider interface:

type CustomMetricsProvider interface {
    // GetRootScopedMetricByName fetches a particular metric for a particular...