Scale Your Builds: Adding and Configuring GitLab Runners

In today’s fast-paced development world, speeding up your build processes is a must. GitLab Runners can help you do just that. This guide will show you how to add and set up GitLab Runners, whether you’re using GitLab.com or managing your own instance. We’ll cover everything from the basics to advanced configurations, so you can get the most out of your CI/CD pipelines.

Key Takeaways

• GitLab Runners help speed up your CI/CD processes by executing jobs in your pipelines.
• There are different types of runners, including shared, group, and specific runners.
• Setting up self-managed runners requires a few prerequisites and proper configuration.
• You can scale your runner fleet to handle more jobs by using autoscaling features.
• Monitoring and maintaining your runners ensures they run smoothly and efficiently.

Understanding GitLab Runners

What is a GitLab Runner?

A GitLab Runner is an application that works with GitLab CI/CD to run jobs in a pipeline. It processes jobs on the same machine where it’s installed or in other environments like containers or cloud instances. GitLab Runners are essential for automating your development workflows, making continuous integration and delivery seamless.

Types of GitLab Runners

There are two main types of GitLab Runners:

1. Shared Runners: These are available to all projects in a GitLab instance. They are ideal for smaller tasks and are managed by GitLab.
2. Specific Runners: These are dedicated to a particular project or group. They offer more control and customization, making them suitable for complex builds.

Benefits of Using GitLab Runners

Using GitLab Runners comes with several advantages:

• Scalability: Easily scale your CI/CD operations by adding more runners as needed.
• Flexibility: Choose from different executor types like Docker, Shell, and Kubernetes to match your project requirements.
• Cost-Effective: With options like GitLab Ultimate and GitLab Premium, you can optimize your gitlab pricing based on your needs.

When you integrate GitLab Runners into your CI/CD pipeline, you streamline your development process, making it more efficient and reliable.

Setting Up Self-Managed Runners

Setting up self-managed GitLab Runners can give you more control over your CI/CD pipelines. Here’s how to get started.

Prerequisites for Self-Managed Runners

Before you begin, make sure you meet these requirements:

• GitLab Runner 15.3 or later
• Administrator access to your GitLab instance

Installation Steps

Follow these steps to install GitLab Runner on your own infrastructure:

1. Download the GitLab Runner binary for your operating system.
2. Install the binary and set it up as a service.
3. Verify the installation by running gitlab-runner --version.

Configuring the Runner

Once installed, you need to configure the runner to suit your needs. This involves editing the config.toml file, which is created during the installation process. Key settings include:

• Concurrent Jobs: Define how many jobs can run at the same time.
• Check Interval: Set how often the runner checks for new jobs.

concurrent = 10
check_interval = 0

[[runners]]
  name = "my-runner"
  url = "https://gitlab.com"
  token = "RUNNER_TOKEN"
  executor = "docker"
  limit = 20

By following these steps, you can efficiently set up and manage GitLab Runners in your own environment, ensuring a robust and scalable CI/CD pipeline.

Registering GitLab Runners

Registering GitLab Runners is a crucial step in setting up your CI/CD pipeline. This process ensures that your GitLab instance can communicate effectively with the machines where GitLab Runner is installed. Let’s break down the steps involved in this process.

Configuring GitLab Runners

Configuring GitLab Runners is a crucial step to ensure your CI/CD pipelines run smoothly and efficiently. This section will guide you through the process of editing the config.toml file, setting global options, and adjusting runner-specific settings.

Scaling Your Runner Fleet

Scaling your GitLab Runner fleet is crucial for maintaining efficiency and managing costs. Here’s how you can plan and implement scaling effectively.

Planning for Scale

Before you start scaling, it’s essential to understand your workload patterns. Identify peak times and low-activity periods. This helps in configuring your runners to handle the load efficiently. Proper planning ensures that you have enough runners during high-demand periods and can scale down during off-peak times.

Autoscaling with Docker Machine

Autoscaling allows your runner fleet to grow and shrink based on demand. Using Docker Machine, you can set up autoscaling by configuring the config.toml file. Here’s a basic example:

[[runners]]
  url = "https://gitlab.com"
  token = "RUNNER_TOKEN"
  name = "autoscale-runner"
  executor = "docker+machine"
  limit = 10
  [runners.docker]
    image = "ruby:2.7"
  [runners.machine]
    IdleCount = 5
    IdleTime = 600
    MaxBuilds = 100
    MachineName = "auto-scale-%s"
    MachineDriver = "google"
    MachineOptions = [
      "google-project=GOOGLE-PROJECT-ID",
      "google-zone=GOOGLE-ZONE",
      "google-machine-type=GOOGLE-MACHINE-TYPE",
      "google-machine-image=ubuntu-os-cloud/global/images/family/ubuntu-1804-lts",
      "google-username=root",
      "google-use-internal-ip",
      "engine-registry-mirror=https://mirror.gcr.io"
    ]
    [[runners.machine.autoscaling]]
      Periods = ["* * 9-17 * * mon-fri *"]
      IdleCount = 50
      IdleCountMin = 5
      IdleScaleFactor = 1.5
      IdleTime = 3600
      Timezone = "UTC"
    [[runners.machine.autoscaling]]
      Periods = ["* * * * * sat,sun *"]
      IdleCount = 5
      IdleTime = 60
      Timezone = "UTC"

This configuration ensures that you have enough idle machines during peak hours and reduces the count during weekends.

Managing Multiple Runners

When managing multiple runners, it’s important to keep track of their performance and health. Use monitoring tools like Prometheus to gather metrics and set up alerts for any issues. Regular maintenance tasks, such as updating the runners and cleaning up unused resources, are also crucial.

Efficiently managing your runner fleet can significantly reduce costs and improve your CI/CD pipeline’s performance.

By following these steps, you can ensure that your GitLab Runner fleet scales effectively to meet your project’s demands.

Monitoring and Maintaining Runners

Using Prometheus for Monitoring

To get started with monitoring your application in GitLab, you can use Prometheus. Prometheus helps you track metrics like the number of currently-running jobs and CPU usage. Monitoring is a crucial part of maintaining and optimizing your applications. GitLab observability features help you track errors and analyze application performance.

Regular Maintenance Tasks

Regular maintenance is essential to keep your runners in top shape. Here are some tasks you should perform:

• Update GitLab Runner: Ensure your GitLab Runner is always up-to-date to avoid compatibility issues.
• Check Logs: Regularly review logs for any errors or warnings.
• Resource Allocation: Monitor and adjust CPU and memory allocation as needed.

Troubleshooting Common Issues

Even with the best setup, issues can arise. Here are some common problems and how to fix them:

1. Runner Not Picking Up Jobs: Check if the runner is registered and active.
2. High CPU Usage: Review the jobs running and optimize them for better performance.
3. Authentication Issues: Ensure that your runner tokens are valid and have not expired.

Automate your DevOps: crafting your first GitLab pipeline can help you streamline these tasks and ensure smooth operations.

Advanced Runner Configuration

Custom Build Directories

Configuring custom build directories allows you to control where your build outputs are stored. This can be particularly useful for managing disk space and organizing your build artifacts. To set a custom build directory, modify the [[runners.custom_build_dir]](https://docs.gitlab.com/ee/administration/self_hosted_models/) section in your config.toml file.

Using CI/CD Variables

CI/CD variables are essential for making your pipelines more flexible and dynamic. They allow you to pass data into your jobs, such as API keys or environment-specific settings. This guide covers advanced GitLab CI configurations, emphasizing the use of variables for flexibility, conditional job execution, and optimizing pipeline performance.

Configuring Cache and Artifacts

Properly configuring cache and artifacts can significantly speed up your build times. The [runners.cache] section in the config.toml file allows you to define how and where your build cache is stored. This is crucial for large projects where build times can be a bottleneck.

For more configurations details read the advanced configuration.

By fine-tuning these settings, you can ensure that your GitLab Runners are optimized for performance and reliability.

Security Best Practices

Protecting Sensitive Information

Keeping your sensitive data safe is crucial. Use tools like 1Password to manage secrets securely. Always ensure that your secrets are not hard-coded in your repositories. Instead, use environment variables to handle them.

Tip: Regularly rotate your secrets to minimize the risk of exposure.

Authentication Token Security

Authentication tokens are the keys to your kingdom. Make sure they are stored securely and have the least privileges necessary. Avoid sharing tokens across multiple projects to reduce the risk of a breach.

Using Tags for Job Control

Tags can help you control which runners pick up specific jobs. This is especially useful for separating sensitive tasks from less critical ones. By tagging your jobs, you can ensure that only trusted runners execute them, enhancing your overall security.

• Example Tags:
  • secure
  • trusted
  • sensitive

Remember: Shared and trusted runners follow GitLab security best practices, ensuring a safer CI/CD environment.

Utilizing GitLab-Hosted Runners

When to Use GitLab-Hosted Runners

GitLab-hosted runners are a great choice if you want a hassle-free setup. These runners are managed by GitLab, so you don’t have to worry about maintenance or updates. They are perfect for small to medium-sized projects or if you don’t have the resources to manage your own infrastructure. By default, these runners are enabled for all projects.

Enabling/Disabling GitLab-Hosted Runners

To enable or disable GitLab-hosted runners, you need to have the Owner role for the project. Navigate to your project’s settings and look for the CI/CD section. Here, you can toggle the runners on or off. This gives you the flexibility to switch between GitLab-hosted and self-managed runners as needed.

Comparing GitLab-Hosted and Self-Managed Runners

Feature	GitLab-Hosted Runners	Self-Managed Runners
Maintenance	Managed by GitLab	Managed by you
Customization	Limited	Highly customizable
Cost	Included in GitLab subscription	Depends on your infrastructure
Scalability	Good for small to medium projects	Ideal for large-scale projects

If you need more control over your CI/CD environment, self-managed runners are the way to go. However, if you prefer a hands-off approach, GitLab-hosted runners are an excellent option.

GitLab-hosted runners are fully integrated with GitLab.com, making them a convenient choice for many users.

Executor Types and Their Uses

Understanding the different types of executors available in GitLab Runners is crucial for optimizing your CI/CD pipelines. Each executor type has its own unique use cases and benefits, making it essential to choose the right one for your specific needs.

Overview of Executors

GitLab Runners support various executors, each designed to run jobs in different environments. The main executors include:

• Shell Executor: Runs jobs in the local shell. This is the default executor and is ideal for simple tasks.
• Docker Executor: Runs jobs inside Docker containers. This is perfect for creating isolated environments.
• Docker+Machine Executor: Similar to the Docker executor but uses auto-scaled Docker machines, making it suitable for large-scale projects.
• Kubernetes Executor: Runs jobs in Kubernetes pods, offering scalability and flexibility.
• SSH Executor: Executes jobs on remote machines via SSH, useful for running tasks on specific servers.
• Parallels and VirtualBox Executors: Run jobs in virtual machines, providing a high level of isolation.

Docker Executor

The Docker executor is one of the most popular choices due to its ability to create isolated environments. It uses Docker containers to run jobs, ensuring that each job runs in a clean state. This executor is highly versatile and can be used for a wide range of applications, from simple scripts to complex builds.

Shell Executor

The Shell executor runs jobs in the local shell of the machine where the GitLab Runner is installed. This is the default executor and is best suited for straightforward tasks that don’t require isolation. It’s easy to set up and use, making it a good choice for beginners.

Tip: The Shell executor is ideal for quick, simple tasks that don’t need a lot of resources.

Kubernetes Executor

The Kubernetes executor runs jobs in Kubernetes pods, providing a scalable and flexible environment. This executor is perfect for projects that need to scale quickly and efficiently. It leverages Kubernetes’ orchestration capabilities to manage resources and ensure high availability.

SSH Executor

The SSH executor allows you to run jobs on remote machines via SSH. This is particularly useful for tasks that need to be executed on specific servers or environments. It provides a high level of control and flexibility, making it a good choice for specialized tasks.

Parallels and VirtualBox Executors

These executors run jobs in virtual machines, offering a high level of isolation and security. They are ideal for tasks that require a specific environment or need to be completely isolated from other jobs. While they may require more setup, the benefits in terms of security and isolation can be significant.

Version Compatibility and Updates

Keeping GitLab Runner Updated

Keeping your GitLab Runner updated is crucial for smooth operations. Regular updates ensure you have the latest features and security patches. Always check the changelog for any breaking changes before updating.

Compatibility with GitLab Versions

GitLab Runners must be compatible with the GitLab version you are using. Refer to the [official GitLab Runner documentation](https://docs.gitlab.com/runner/) to verify compatibility. This helps avoid any compatibility concerns that might arise after updates.

Handling Version Mismatches

Version mismatches can cause unexpected issues. If you encounter problems, check the versions of both GitLab and the Runner. Sometimes, downgrading or upgrading one of them can resolve the issue. Always keep a backup before making changes.

Keeping your software up-to-date is crucial for smooth performance. Our website offers detailed information on version compatibility and the latest updates. Visit us to stay informed and ensure your tools are always running at their best.

Conclusion

Scaling your builds with GitLab Runners can significantly boost your development workflow. By setting up and configuring your own runners, you gain more control and flexibility over your CI/CD processes. Whether you choose GitLab-hosted or self-managed runners, understanding how to monitor and optimize their performance is key. Keep your runners updated and properly configured to ensure smooth and efficient operations. With the right setup, GitLab Runners can help you handle more jobs, reduce wait times, and ultimately, deliver better software faster.