Book Image

Continuous Delivery with Docker and Jenkins - Second Edition

By : Rafał Leszko
Book Image

Continuous Delivery with Docker and Jenkins - Second Edition

By: Rafał Leszko

Overview of this book

Continuous Delivery with Docker and Jenkins, Second Edition will explain the advantages of combining Jenkins and Docker to improve the continuous integration and delivery process of an app development. It will start with setting up a Docker server and configuring Jenkins on it. It will then provide steps to build applications on Docker files and integrate them with Jenkins using continuous delivery processes such as continuous integration, automated acceptance testing, and configuration management. Moving on, you will learn how to ensure quick application deployment with Docker containers along with scaling Jenkins using Kubernetes. Next, you will get to know how to deploy applications using Docker images and testing them with Jenkins. Towards the end, the book will touch base with missing parts of the CD pipeline, which are the environments and infrastructure, application versioning, and nonfunctional testing. By the end of the book, you will be enhancing the DevOps workflow by integrating the functionalities of Docker and Jenkins.
Table of Contents (18 chapters)
Title Page
Dedication
About Packt
Contributors
Preface
Index

Exercises


You've learned a lot about how to configure the Continuous Integration process. Since practice makes perfect, I recommend doing the following exercises:

  1. Create a Python program that multiplies two numbers passed as the command-line parameters. Add unit tests and publish the project on GitHub:
    1. Create two files: calculator.py and test_calculator.py
    2. You can use the unittest library at https://docs.python.org/library/unittest.html
    3. Run the program and the unit test
  1. Build the Continuous Integration pipeline for the Python calculator project:
    1. Use Jenkinsfile to specify the pipeline
    2. Configure the trigger so that the pipeline runs automatically in case of any commit to the repository
    3. The pipeline doesn't need the Compile step since Python is an interpretable language
    4. Run the pipeline and observe the results
    5. Try to commit the code that breaks each stage of the pipeline and observe how it is visualized in Jenkins