Book Image

Automating DevOps with GitLab CI/CD Pipelines

By : Christopher Cowell, Nicholas Lotz, Chris Timberlake
Book Image

Automating DevOps with GitLab CI/CD Pipelines

By: Christopher Cowell, Nicholas Lotz, Chris Timberlake

Overview of this book

Developers and release engineers understand the high stakes involved in building, packaging, and deploying code correctly. Ensuring that your code is functionally correct, fast, and secure is a time-consuming and complex task. Code implementation, development, and deployment can be conducted efficiently using GitLab CI/CD pipelines. Automating DevOps with GitLab CI/CD Pipelines begins with the basics of Git and GitLab, showing how to commit and review code. You’ll learn to set up GitLab Runners for executing and autoscaling CI/CD pipelines and creating and configuring pipelines for many software development lifecycle steps. You'll also discover where to find pipeline results in GitLab, and how to interpret those results. Through the course of the book, you’ll become well-equipped with deploying code to different environments, advancing CI/CD pipeline features such as connecting GitLab to a Kubernetes cluster and using GitLab with Terraform, triggering pipelines and improving pipeline performance and using best practices and troubleshooting tips for uncooperative pipelines. In-text examples, use cases, and self-assessments will reinforce the important CI/CD, GitLab, and Git concepts, and help you prepare for interviews and certification exams related to GitLab. By the end of this book, you'll be able to use GitLab to build CI/CD pipelines that automate all the DevOps steps needed to build and deploy high-quality, secure code.
Table of Contents (18 chapters)
1
Part 1 Getting Started with DevOps, Git, and GitLab
6
Part 2 Automating DevOps Stages with GitLab CI/CD Pipelines
11
Part 3 Next Steps for Improving Your Applications with GitLab

Improving your pipeline

You’ve set up a pipeline to make sure your code is of high quality and doesn’t have security vulnerabilities. In many cases, you can stop there. However, for this sample use case, you’ll go a step further and look into using a DAG to speed up the pipeline. You’ll also see whether it’s worth splitting the pipeline’s configuration code into multiple files to improve readability and maintainability.

Using a DAG to speed up the pipeline

Our pipeline isn’t complicated enough to justify converting it into a DAG quite yet, but if we continue to add more jobs, we’ll eventually want to use DAGs for some or all of it for performance reasons. Let’s preview this by using the needs keyword now to add some DAG elements to our pipeline.

First, let’s say that we want the code_quality job to run only after the unit-tests job passes. After all, we might think that our code needs to work correctly before...