Book Image

Mastering Kubernetes - Third Edition

By : Gigi Sayfan
Book Image

Mastering Kubernetes - Third Edition

By: Gigi Sayfan

Overview of this book

The third edition of Mastering Kubernetes is updated with the latest tools and code enabling you to learn Kubernetes 1.18’s latest features. This book primarily concentrates on diving deeply into complex concepts and Kubernetes best practices to help you master the skills of designing and deploying large clusters on various cloud platforms. The book trains you to run complex stateful microservices on Kubernetes including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backend. With the two new chapters, you will gain expertise in serverless computing and utilizing service meshes. As you proceed through the chapters, you will explore different options for network configuration and learn to set up, operate, and troubleshoot Kubernetes networking plugins through real-world use cases. Furthermore, you will understand the mechanisms of custom resource development and its utilization in automation and maintenance workflows. By the end of this Kubernetes book, you will graduate from an intermediate to advanced Kubernetes professional.
Table of Contents (19 chapters)
17
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18
Index

Launching jobs

Hue has evolved and has a lot of long-running processes deployed as microservices, but it also has a lot of tasks that run, accomplish some goal, and exit. Kubernetes supports this functionality via the Job resource. A Kubernetes job manages one or more pods and ensures that they run until they are successful. If one of the pods managed by the job fails or is deleted, then the job will run a new pod until it succeeds.

There are also many serverless or function-as-a-service solutions for Kubernetes, but they are built on top of native Kubernetes. We will dedicate a whole chapter to serverless computing.

Here is a job that runs a Python process to compute the factorial of 5 (hint: it's 120):

apiVersion: batch/v1
kind: Job
metadata:
  name: factorial5
spec:
  template:
    metadata:
      name: factorial5
    spec:
      containers:
      - name: factorial5
        image: g1g1/py-kube:0.2
        command: ["python",
                  "-c...