10 GitLab CI Best Practices

GitLab CI is a powerful tool that can help developers automate their workflow. However, as with any tool, there are best practices that should be followed in order to get the most out of it. In this article, we will discuss 10 GitLab CI best practices that every developer should follow.

1. Use .gitlab-ci.yml templates

.gitlab-ci.yml files are used to define the jobs that will be run by GitLab CI. They are usually stored in the root directory of a project, and they can get very complex, very quickly. This is why it’s important to use templates, so you don’t have to start from scratch every time you need to create a new .gitlab-ci.yml file.

GitLab provides a few different templates that you can use, or you can create your own. Either way, using templates will save you a lot of time and effort in the long run.

2. Don’t use tags to trigger pipelines

When a tag is pushed, GitLab will create a new pipeline for that tag. However, if you have a lot of tags, this can quickly lead to a lot of pipelines being created, which can be difficult to manage.

Instead, it’s better to use branches to trigger pipelines. This way, you can more easily control which pipelines are being created and when they’re being created.

3. Only build what you need

If you’re building your project on every push to GitLab, it’s going to take a lot longer for each job to run. Not only that, but you’re also going to use up more of your build minutes, which could limit how often you can build your project.

Instead, you should only build the jobs that are relevant to the changes that were made in the commit. For example, if you have a frontend and backend job, and only the frontend code was changed, there’s no need to run the backend job.

You can achieve this by using the only and except keywords in your .gitlab-ci.yml file. By default, all jobs will be built, but you can use these keywords to specify which jobs should or shouldn’t be built.

For example, let’s say you have the following jobs:

job1
job2
job3

And you only want job1 and job3 to be built when a change is made to the code. You would add the following to your .gitlab-ci.yml file:

only:
– job1
– job3

4. Run your tests in parallel

When you have a lot of tests, it can take a long time to run them all sequentially. This is especially true if you have slow tests that take a long time to complete.

Running your tests in parallel means that each test is run on its own process or thread, so they can all be started at the same time. This can dramatically reduce the total time it takes to run your tests, and it’s especially useful if you have a large test suite.

To run your tests in parallel, you need to use a tool like Parallel::ForkManager or Test::Class::Moose::Role::Parallelizable. There are also some tools that can help you automatically parallelize your tests, like Parallel::Jobs.

5. Keep the pipeline fast

If the pipeline is slow, it will take longer for feedback to reach the developers. This can lead to frustration and even errors, as developers are forced to wait for the results of their work.

A fast pipeline also allows for more frequent deployments. This is important because it means that changes can be pushed to production more quickly, and that bugs can be fixed more rapidly.

There are a few ways to keep the pipeline fast. One is to use caching, which can speed up the process by storing intermediate results. Another is to parallelize the jobs in the pipeline, so that they can be run concurrently.

Finally, it’s important to make sure that the jobs in the pipeline are well-designed. This means avoiding unnecessary steps, and making sure that each job is focused on a single task.

6. Cache dependencies and outputs

Jobs in a GitLab CI/CD pipeline can take a long time to run, especially if they’re doing things like compiling code or running tests. If you have to do these things every time you run a job, it’s going to slow down your pipeline significantly.

Caching dependencies and outputs means that you only have to do these things once, and then you can just reuse the cached versions in future runs. This can speed up your jobs considerably.

To cache dependencies and outputs, you need to use the cache keyword in your .gitlab-ci.yml file. For example:

cache:
paths:
– vendor/
– node_modules/
This will cache the contents of the vendor/ and node_modules/ directories. You can cache multiple paths by separating them with commas.

You can also specify how long the cache should be valid for using the expires_in keyword. For example:

cache:
paths:
– vendor/
– node_modules/
expires_in: 2 weeks
This will cache the paths for two weeks, after which they will expire and be re-fetched the next time the job is run.

7. Use GitLab CI/CD variables

GitLab CI/CD variables are key-value pairs that are used to store and retrieve information that is specific to a project or job. This information can be anything from passwords and API keys to file paths and environment variables.

GitLab CI/CD variables are stored in the project’s .gitlab-ci.yml file, and they can be accessed by any job that is defined in that file.

Variables can be defined at the project level or at the job level. Project-level variables take precedence over job-level variables.

Variables can be defined using either the “key: value” syntax or the “key = value” syntax. The “key: value” syntax is preferred, as it is more concise and easier to read.

Here is an example of how to define a variable using the “key: value” syntax:

variables:
MY_VARIABLE: “value”

And here is an example of how to define a variable using the “key = value” syntax:

variables:
MY_VARIABLE = “value”

Once a variable has been defined, it can be used anywhere in the .gitlab-ci.yml file by enclosing it in double curly braces: {{MY_VARIABLE}}.

GitLab CI/CD variables are extremely useful for storing sensitive information, such as passwords and API keys. They are also useful for storing information that is specific to a particular environment, such as file paths and environment variables.

8. Use Docker images from a registry

When you use a Docker image from a registry, you’re using an image that’s been tested and is known to work. This means that you can trust the image to run as expected, and you don’t have to worry about whether or not it will work properly.

Additionally, using a Docker image from a registry ensures that you’re always using the latest version of the image. This is important because it means that you’ll always have the most up-to-date security patches and features.

Finally, using a Docker image from a registry allows you to share your images with others. This is helpful if you want to collaborate on a project or if you simply want to make your images available to the public.

9. Use Auto DevOps for simple projects

Auto DevOps is a feature that automatically detects, builds, tests, and deploys your code. All you need to do is push your code to GitLab and Auto DevOps will take care of the rest. This is ideal for simple projects because it eliminates the need to configure a CI/CD pipeline yourself.

Auto DevOps is not recommended for complex projects because it may not be able to handle all the build, test, and deployment steps required. In these cases, it’s better to manually configure your own CI/CD pipeline.

10. Deploy with GitLab Runner Helm chart

GitLab Runner Helm chart is the recommended way to install and configure GitLab Runner on a Kubernetes cluster. The chart will deploy a pod that runs multiple containers, including the main GitLab Runner container and a sidecar container for the helper image.

The benefits of deploying with GitLab Runner Helm chart include:

– Easy installation and configuration
– Automatic updates
– Scale horizontally by adding more runners
– Runners can be deployed on different nodes in the cluster for high availability