Book Image

Mastering Kubernetes - Second Edition

By : Gigi Sayfan
Book Image

Mastering Kubernetes - Second Edition

By: Gigi Sayfan

Overview of this book

Kubernetes is an open source system that is used to automate the deployment, scaling, and management of containerized applications. If you are running more containers or want automated management of your containers, you need Kubernetes at your disposal. To put things into perspective, Mastering Kubernetes walks you through the advanced management of Kubernetes clusters. To start with, you will learn the fundamentals of both Kubernetes architecture and Kubernetes design in detail. You will discover how to run complex stateful microservices on Kubernetes including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backend. Using real-world use cases, you will explore the options for network configuration, and understand how to set up, operate, and troubleshoot various Kubernetes networking plugins. In addition to this, you will get to grips with custom resource development and utilization in automation and maintenance workflows. To scale up your knowledge of Kubernetes, you will encounter some additional concepts based on the Kubernetes 1.10 release, such as Promethus, Role-based access control, API aggregation, and more. By the end of this book, you’ll know everything you need to graduate from intermediate to advanced level of understanding Kubernetes.
Table of Contents (16 chapters)

Pushing the envelope with Kubernetes

In this section, we will see how the Kubernetes team pushes Kubernetes to its limit. The numbers are quite telling, but some of the tools and techniques, such as Kubemark, are ingenious, and you may even use them to test your clusters. In the wild, there are some Kubernetes clusters with 3,000 nodes. At CERN, the OpenStack team achieved 2 million requests per second:

Mirantis conducted a performance and scaling test in their scaling lab where they deployed 5,000 Kubernetes nodes (in VMs) on 500 physical servers.

For more detail on Mirantis, please refer to:

OpenAI scaled their machine learning Kubernetes cluster to 2,500 nodes and learned some valuable lessons, such as minding the query load of logging agents and storing...