GitLab is a popular open-source software development platform that includes a CI/CD (continuous integration and continuous delivery) system. GitLab runners are a key component of this system, as they are responsible for executing CI/CD jobs.
However, if your GitLab runners are slow, it can have a negative impact on the performance of your CI/CD pipeline. This can lead to delays in deployments, which can in turn lead to lost productivity and revenue.
If you're struggling with slow GitLab runners, you're not alone. Many users face this issue due to various reasons, such as CPU limitations, low memory, or network pressure. This is why we created our own high performance cloud-based gitlab runners company.
To get a clearer understanding of why your runners might be slow, check out our article on the topic. We discuss typical causes and offer pointers for enhancing performance.
Understanding How Where Your Gitlab Runner run
When it comes to GitLab runners, two primary options exist:
Managed Runners by GitLab.com: These runners often utilize VM instances on platforms like Google Cloud Platform (GCP) with limited resources, typically 2 cores and 2 GB of RAM. While convenient and free, these resources may not always be sufficient for demanding CI/CD tasks, leading to performance bottlenecks and slow execution times.
Self-Hosted Runners: Alternatively, organizations can opt for self-hosted runners. By managing their own infrastructure, teams have greater control over resource allocation and can tailor runner configurations to meet specific workload requirements. However, this approach requires additional setup and maintenance efforts.
Identifying the Causes
Given the known limitations of GitLab managed runners running on potentially underpowered GCP instances, it's essential to take proactive steps to diagnose and address performance issues. Here's how you can proceed:
- Try a New Runner Locally or On-Premises: To verify if the slow performance is indeed due to resource constraints on the GCP instance, consider setting up a new runner on a developer's local machine or directly on an on-premises server with ample resources (or try our incredibles gitlab runners with our 1 month free trial). This allows you to test the performance of the runner in a controlled environment outside of the GitLab managed infrastructure.
- Assess Resource Utilization: Once the new runner is operational, monitor its performance closely. Check if it experiences similar slowdowns or if the performance improves significantly. This can help you determine if the issue lies with inadequate CPU or RAM resources on the GCP instance or if it's related to slow network access.
- Review CPU, Memory, and Network Metrics: Analyze the CPU utilization, memory usage, and network access metrics of the new runner. If none of these metrics show alarming values, it's time to focus on optimizing the GitLab itself. (Refer to the following section "Optimize My GitLab CI/CD" for guidance on optimizing and fine-tuning your GitLab setup.)
By systematically evaluating the performance of the new runner and analyzing resource utilization, you can pinpoint the root cause of the slow GitLab runners and take appropriate measures to optimize performance for your CI/CD workflows.
Improve My GitLab Runner Performance
Understanding the nature of CI/CD jobs is crucial for optimizing GitLab runner performance. CI/CD jobs are often highly repetitive, with the majority of tasks being similar or identical.
For instance, when building and running tests from a Docker image, in the vast majority of cases, approximately 98% of the previous CI/CD pipeline's output will be reused. This is because very few changes occur with each pipeline run.
Therefore, implementing advanced caching techniques such as S3 caching, caching Docker images, caching HTTP responses, and caching build artifacts can significantly improve performance.
If any system component is lacking, such as CPU, RAM, or network bandwidth, it's essential to address these resource limitations to enhance GitLab runner performance. Adding more CPU cores, increasing RAM capacity, or improving network connectivity can alleviate bottlenecks and improve overall performance.
By leveraging advanced caching techniques and ensuring adequate system resources, you can optimize GitLab runner performance and streamline CI/CD workflows for faster and more efficient software delivery.
Optimize My GitLab CI/CD
If hardware resources are not the bottleneck in your GitLab CI/CD setup, it's time to examine the efficiency of your CI/CD pipelines themselves. Several best practices can help optimize your GitLab CI/CD workflows:
- Parallelize Jobs: Ensure that all jobs in your CI/CD pipeline can run concurrently whenever possible. Limit sequential execution of jobs to maximize the utilization of runner resources.
- Prebuild Dependencies: Avoid performing repetitive tasks, such as building Docker images or downloading necessary dependencies, with every CI/CD pipeline run. Instead, prebuild these dependencies and cache them to reduce redundant work and improve pipeline execution speed.
- Optimize Task Execution: Review your CI/CD pipeline configuration to identify tasks that can be moved outside of the pipeline execution. For example, building Docker images or downloading dependencies can be performed beforehand to streamline pipeline execution.
- Maximize CPU Utilization: Ensure that applications launched during CI/CD pipeline execution are configured to make efficient use of available CPU resources. For instance, consider optimizing your test suite to leverage multi-threading capabilities, allowing multiple tests to run in parallel and fully utilize the CPU cores available on the runner.
By implementing these optimization strategies, you can maximize the efficiency and performance of your GitLab CI/CD pipelines, enabling faster delivery of high-quality software with minimal resource overhead.
Conclusion
In conclusion, we have explored two primary methods of running GitLab runners and discussed how performance and optimization strategies differ between these two approaches.
We have then delved into conducting platform audits to identify system components and pinpoint bottlenecks. Subsequently, we have examined ways to improve system performance, particularly when faced with hardware limitations, emphasizing the importance of caching. Lastly, we have highlighted the crucial significance of enhancing pipeline architectures to optimize parallelism.
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