How to Set Up Continuous Integration Pipelines with GitLab
By
Samantha Cox
•
Nov 21, 2025
Remember that time you pushed code on a Friday afternoon and everything broke? Continuous Integration Pipelines with GitLab catch those problems before they hit production. Here's how to set up automated testing and deployment that actually makes your life easier.
Key Takeaways
Continuous Integration (CI) automates code changes merging and enhances collaboration, providing greater visibility and faster iterations.
Setting up GitLab for CI involves creating a .gitlab-ci.yml file to define jobs and stages, with GitLab Runners executing the configurations.
Effective pipeline optimization includes using cache, artifacts, and testing strategies, while allowing for troubleshooting to address common issues.
What is Continuous Integration?
Continuous integration is a practice that automates the merging of code changes from various contributors into a single project. This method allows developers to work on features simultaneously and integrate them independently, reducing the risk of integration conflicts. Continuous merging and testing of code changes in CI helps prevent high failure rates in releases, catching bugs early in the development process.
One of the key benefits of implementing CI is enhanced communication and collaboration between development and operations teams. This practice provides:
Better visibility into the software development process, making it easier for other organizational departments to stay informed and contribute effectively.
A significant impact on the speed of product iterations and bug fixes.
The ability for organizations to scale their engineering teams and codebases more effectively through a robust CI pipeline.
CI relies heavily on a source code version control system, which tracks changes and ensures that all new code passes automated tests before integration. This process prevents regressions and maintains the stability of the codebase. Adopting CI practices fosters a culture of continuous improvement and progressive delivery, keeping software always in a releasable state through iterative code changes and the latest version of the code.
Setting Up GitLab for Continuous Integration
GitLab provides a single application for CI/CD, version control, and code review, making it an ideal platform for managing your development workflow. First, log in to your GitLab instance. Then, click on ‘New project’ to create a blank project. You can also set up a new project with a Git push from your local machine. GitLab supports various operating systems, so you can install it on most GNU/Linux distributions and multiple cloud providers, including Kubernetes clusters.
Once your GitLab project is set up, the next step is to create a yml file at the root of your repository. This file is essential for defining CI/CD jobs and specifying the sequence and conditions for their execution. In the YML file, you need to:
Specify an image for the container where jobs will run.
Use the ‘needs’ keyword to enhance pipeline performance by allowing jobs to run out of order.
Utilize the ‘default’ keyword for global configurations applicable to all jobs.
GitLab Runners play a crucial role in executing the jobs defined in your CI/CD configurations. These runners are available for Linux, Windows, and MacOS, ensuring compatibility with various development environments. By creating a .gitlab-ci.yml file and setting up GitLab Runners as a project runner, you can automate the build, test, and deployment processes, streamlining your development workflow and ensuring compliance with established code standards, with at least one runner available for each environment.
Configuring Your First Pipeline
A GitLab pipeline consists of jobs and stages, defined in the .gitlab-ci.yaml file option. To configure your first pipeline, you need to create this yaml file at the root of your repository. The fundamental components of a .gitlab-ci.yml file are jobs, which are defined with constraints and contain a build script clause. Stages are typically divided into build, test, and deploy, ensuring a structured approach to a new pipeline execution and configuration files.
To define a build job in GitLab CI, you need to give it an arbitrary name and include at least a script clause. The .gitlab-ci.yml file also specifies environment variables and shared configurations for jobs. Once you commit changes to this file, the pipeline starts automatically, executing the defined jobs in the specified order. Using the ‘needs’ keyword allows jobs to be executed out of their defined stage order, optimizing pipeline speed.
Utilizing the CI Lint tool can help confirm that the syntax of your CI/CD configuration is correct. The pipeline editor in GitLab provides an improved editing experience, including syntax validation and graphical representation of the configuration. By understanding the structure of a GitLab pipeline and using these tools, you can ensure a smooth and efficient pipeline configuration process.
Understanding GitLab Runner
GitLab Runners are open-source applications responsible for executing jobs in a CI/CD pipeline. These runners execute the jobs defined in your GitLab CI/CD configurations, which are specified in the .gitlab-ci.yml file. Registering a GitLab Runner involves running a specific Docker command and using the correct tokens for authentication. This connects the runner to your GitLab instance, allowing it to execute jobs as defined in your pipeline configuration.
You can customize GitLab Runners by configuring their environment, using different execution methods, and managing their runtime configurations. This flexibility allows you to optimize job execution and ensure that your CI/CD pipeline runs efficiently. By understanding and properly setting up GitLab Runners, you can streamline your development workflow and ensure that your jobs run smoothly on the runners page.
Using CI/CD Variables
CI/CD variables store values like API keys, URLs, and configuration settings. Instead of hardcoding these into your pipeline, you define them once and reference them wherever needed. These variables consist of key-value pairs for storing configuration settings and can include sensitive information like passwords or API keys. The purpose of using CI/CD variables is to customize jobs by making external values accessible to the pipeline configuration and to deploy variables effectively.
In GitLab, there are two types of CI/CD variables: predefined variables, which are automatically provided by GitLab, and custom variables, which are user-defined. GitLab variables can be defined at different levels, including project, group, or instance scope, allowing for flexible configuration management. It is critical to store sensitive information via the GitLab UI rather than in the .gitlab-ci.yml file to prevent exposure.
You can mask CI/CD variables to prevent their values from appearing in job logs, enhancing security when using sensitive information. Variables with higher precedence can override others, providing flexibility in your configurations. By understanding and properly managing CI/CD variables, you can ensure that your pipeline runs securely and efficiently.
Optimizing Pipeline Stages
Optimizing the order and organization of subsequent jobs within three stages can enhance pipeline performance and lead to successful execution:
Running jobs executed in earlier stages can speed up pipeline execution without impacting the project negatively.
Merging stages can prevent inefficiencies in pipeline execution by optimizing job arrangements.
Keeping builds simple and fast is essential to minimize delays and improve developer efficiency at the same stage.
Using cache and artifacts helps maintain information across jobs and stages within a pipeline. To optimize pipeline performance, consider the following:
Disable separate caches for protected branches to improve performance by allowing shared access to caches.
Optimize cache and artifact compression to enhance the overall speed of GitLab pipelines.
Upload and populate caches only when necessary to improve pipeline speed.
Advanced techniques to optimize the build duration and efficiency of GitLab CI/CD pipelines include:
Using Docker layer caching to significantly reduce the build duration of Docker images in pipelines.
Automatically retrying failed build jobs to recover from transient issues without manual intervention.
Properly sizing runners and tuning the maximum parallel jobs to greatly improve pipeline efficiency.
Testing Code in GitLab CI
Different types of testing include:
Unit testing: the most common type used to identify bugs in code changes.
Load performance testing: evaluates how code changes affect server performance during execution.
Dynamic application security testing: uncovers vulnerabilities in running web applications, ensuring security is maintained.
Fail fast testing: halts pipelines early if specific tests, like RSpec, encounter failures, enabling quicker feedback.
Testing environments that mirror production settings enhance reliability in deployment outcomes, giving confidence to deploy results. Test environments must closely match production settings to ensure accurate results on the host machine. Test reports can be generated to evaluate various aspects, including accessibility and code quality. Unit test reports provide insights into test results, highlighting any failures without needing to delve into test job logs. A test script can also be utilized to streamline this process.
GitLab CI/CD allows testing of changes made in feature branches, aiding the development process. By configuring and running tests effectively in GitLab CI, you can ensure that your code is reliable and free of bugs before deployment, especially when merging back to the main branch.
Monitoring and Troubleshooting Pipelines
To check the status column of a pipeline in GitLab and troubleshoot common issues:
Navigate to Build > Pipelines to check the status of a pipeline.
Find debugging information for a failed pipeline job in the shell output of the job’s script on the job result page.
Note that if more jobs than CI/CD limits are defined in a pipeline, it may fail to start.
A ‘failed to pull image’ error might indicate permission issues with accessing images stored in another project’s container registry.
Jobs that depend on specific versions of dependencies can prevent unexpected issues by verifying and pinning those dependency versions. Potential issues and recommendations include:
If a job’s configuration permits multiple pipelines to run for a single action, unexpected behavior may occur.
If a job’s output is too silent, it may obscure the issues; therefore, using verbose output can aid in identifying problems and fixing issues.
If a job’s configuration contains a byte order mark (BOM) in the .gitlab-ci.yml file, it can lead to unexpected behavior or cause a job not to run.
Key practices for effective team collaboration include:
Making frequent code commits to help identify bugs more easily due to smaller code changes.
Encouraging team members to regularly read and reference documentation for better understanding and execution of pipelines.
Focusing on learning from failures instead of assigning blame to promote a culture of continuous improvement.
Continuous Deployment with GitLab
Deployment in GitLab can be automated by:
Defining scripts in the .gitlab-ci.yml file to build and push Docker images.
Configuring GitLab CI/CD pipelines to automatically deploy code changes upon successful builds using a deploy script.
Using a dedicated deployment user to improve security by limiting access and controlling deployment permissions.
Implementing SSH keys to securely connect to servers during deployment processes.
GitLab environments allow users to manage different deployment targets and facilitate rollbacks. Rollback strategies in GitLab can be initiated through the interface, allowing quick reversion to previous deployments. Automating deployments and implementing security practices ensures faster releases while maintaining high code quality.
Continuous deployment with GitLab allows for progressive delivery, ensuring that code changes are deployed smoothly and efficiently. Understanding and implementing these practices streamlines your deployment process, keeping your software always in a releasable state with continuous delivery.
Best Practices for GitLab CI
GitLab CI/CD offers several benefits and practices:
Helps in detecting code errors and ensures compliance with coding standards.
Catches bugs early during development, which is a critical benefit.
Uses the allow_failure parameter in CI/CD configurations to avoid unnecessary builds due to external bugs.
Fosters a culture of continuous improvement by reviewing failures in the CI process.
It’s important to investigate the reasons behind CI failures and not focus on who caused them. GitLab aids collaboration among team members through effective planning and code review. Following best practices ensures your CI/CD pipeline runs smoothly and efficiently.
Using a default docker image and properly managing a git repository are essential for maintaining a clean and efficient CI/CD environment. By implementing these best practices, you can ensure that your development workflow is optimized for success with docker run and docker containers.
Summary
In this guide, we’ve explored the essentials of setting up and optimizing CI pipelines with GitLab. From understanding the basics of continuous integration to configuring your first pipeline and implementing best practices, this comprehensive guide has provided you with the knowledge to streamline your development workflow. By adopting these practices, you can ensure smooth, reliable deployments and maintain a high level of code quality. Remember, tools like Fonzi can help you build a top-tier engineering team to support your CI/CD efforts. Continuous improvement and progressive delivery sound like consultant-speak, but they're really just smart ways to ship better code faster.
