Book Image

The Complete Kubernetes Guide

By : Jonathan Baier, Gigi Sayfan, Jesse White
Book Image

The Complete Kubernetes Guide

By: Jonathan Baier, Gigi Sayfan, Jesse White

Overview of this book

If you are running a number of containers and want to be able to automate the way they’re managed, it can be helpful to have Kubernetes at your disposal. This Learning Path guides you through core Kubernetes constructs, such as pods, services, replica sets, replication controllers, and labels. You'll get started by learning how to integrate your build pipeline and deployments in a Kubernetes cluster. As you cover more chapters in the Learning Path, you'll get up to speed with orchestrating updates behind the scenes, avoiding downtime on your cluster, and dealing with underlying cloud provider instability in your cluster. With the help of real-world use cases, you'll also explore options for network configuration, and understand how to set up, operate, and troubleshoot various Kubernetes networking plugins. In addition to this, you'll gain insights into custom resource development and utilization in automation and maintenance workflows. By the end of this Learning Path, you'll have the expertise you need to progress from an intermediate to an advanced level of understanding Kubernetes. This Learning Path includes content from the following Packt products: • Getting Started with Kubernetes - Third Edition by Jonathan Baier and Jesse White • Mastering Kubernetes - Second Edition by Gigi Sayfan
Table of Contents (26 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Launching jobs


Hue 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 through the job resource. A Kubernetes job manages one or more pods and ensures that they run until success. If one of the pods managed by the job fails or is deleted, then the job will run a new pod until it succeeds.

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: python:3.6
        command: ["python", 
                  "-c", 
                  "import math; print(math.factorial(5))"]
      restartPolicy: Never      

Note that the restartPolicy must be either Never or OnFailure. The default Always value is invalid because a job shouldn't restart after successful completion.

Let...