Introduction

GitLab is a popular platform for developers to manage their software development lifecycle, including source code management, continuous integration, and continuous deployment. With GitLab Runners, teams can run their CI/CD pipelines to automate the build, test, and deployment process. But when it comes to optimizing pipeline performance, there’s a popular misconception that using a distributed cache for GitLab Runners is a good idea. In this article, we will discuss why implementing a distributed cache for GitLab Runners can actually be detrimental to your pipeline, and we will offer alternative solutions for exchanging data between jobs.

1. No Significant Speed Advantage for Internal Networks

One of the main reasons people opt for a distributed cache is to speed up the download of dependencies. However, if your dependencies are hosted on internal network resources like EOS, Nexus, Artifactory, or GitLab Releases, then the distributed cache is also within the same internal network. In this scenario, the speed advantage of using a distributed cache is negligible, as the download speed will likely be equivalent.

2. Increased Complexity and Maintenance Overhead

Implementing a distributed cache for GitLab Runners adds an additional layer of complexity to your CI/CD pipeline. The configuration and maintenance of the cache can become a burden for your team, diverting valuable time and resources away from your core development work.

3. Cache Inconsistency and Data Corruption

A distributed cache introduces the risk of cache inconsistency and data corruption. As multiple runners access and modify the cache simultaneously, it can become challenging to ensure that the correct version of a dependency or artifact is being used. This can lead to unexpected behavior or failures in your pipeline.

4. Limited Scalability

As your team and project grow, your caching infrastructure will need to scale accordingly to handle the increased load. Scaling a distributed cache can be both costly and time-consuming, requiring additional hardware, configuration, and management efforts.

A Better Alternative: Using Artifacts to Exchange Data Between Jobs

Instead of relying on a distributed cache for GitLab Runners, a more robust and efficient solution is to use GitLab Artifacts to exchange data between jobs. Artifacts are files generated during a pipeline job that can be passed between subsequent jobs or even downloaded for later use.

Using artifacts has several advantages:

1. Simplified Configuration: Artifacts are natively supported by GitLab and require minimal configuration to set up and use in your pipeline.

2. Reliable Data Exchange: Artifacts are designed for secure and reliable data exchange between jobs, ensuring that the correct data is always available.

3. Efficient Storage and Cleanup: GitLab Artifacts can be automatically expired after a specified duration, reducing storage overhead and the need for manual cleanup.

4. Better Pipeline Performance: Since artifacts are stored within GitLab itself, you can expect faster access times and reduced network latency, especially when compared to a distributed cache setup.

Conclusion

While a distributed cache for GitLab Runners might initially seem like an appealing solution for optimizing your CI/CD pipeline, it can often introduce more problems than it solves. With minimal speed benefits for internal networks, increased complexity, and the potential for cache inconsistency, a distributed cache is not the ideal solution for most teams. Instead, consider using GitLab Artifacts to exchange data between jobs, providing a more reliable and efficient approach to managing your pipeline.