1. Implementing GitLab runners on Kubernetes for scalable CI/CD pipelines


    To implement scalable CI/CD pipelines using GitLab runners on Kubernetes with Pulumi, you'll need to follow these steps:

    1. Set up a Kubernetes cluster: Before you can deploy GitLab runners, you need a working Kubernetes cluster. You can use any supported Kubernetes provider like AWS EKS, Google GKE, or Azure AKS.

    2. Deploy GitLab runners: Once you have a Kubernetes cluster, you can use the GitLab Runner Kubernetes executor to deploy runners within the cluster. The runners will pick up jobs through the GitLab API and run the CI/CD pipeline jobs in your Kubernetes pods.

    3. Configure GitLab to use the runners: After deploying the runners, you must register them with your GitLab instance, linking them with projects or groups as needed.

    Below is a program written in TypeScript that demonstrates how to use Pulumi to create a GitLab runner within a Kubernetes cluster. This assumes you have already set up your Kubernetes cluster and have the necessary configurations to access it through Pulumi.

    import * as k8s from '@pulumi/kubernetes'; import * as pulumi from '@pulumi/pulumi'; import * as gitlab from '@pulumi/gitlab'; // Create a GitLab Runner using the GitLab provider. // This will register a runner to a GitLab instance. const gitlabRunner = new gitlab.Runner("example-runner", { // Registration token obtained from your GitLab instance. registrationToken: "REGISTRATION_TOKEN_HERE", // The tagList allows you to run builds with tags on this runner. tagList: ["kubernetes", "docker"], // Indicate whether the runner should pick up untagged jobs. runUntagged: true, // Indicates whether the runner is locked to the current project. locked: false, }); // Use a Kubernetes provider to interact with your Kubernetes cluster. const k8sProvider = new k8s.Provider("k8s-provider", { // Assumes you have a working kubeconfig file. // If using a cloud-provided cluster, you may need to configure this provider // to use the appropriate context. kubeconfig: process.env.KUBECONFIG, }); // Create a Kubernetes Deployment to manage the GitLab Runner pods. const runnerDeployment = new k8s.apps.v1.Deployment("runner-deployment", { metadata: { namespace: "default", }, spec: { selector: { matchLabels: { app: "runner" } }, replicas: 1, // Scale up the number of replicas as needed. template: { metadata: { labels: { app: "runner" }, }, spec: { containers: [ { name: "gitlab-runner", image: "gitlab/gitlab-runner:latest", env: [ { name: "GITLAB_RUNNER_TOKEN", valueFrom: { secretKeyRef: { // The secret name should match the name of the secret // where the GitLab Runner registration token is stored, // usually obtained from registering a runner in GitLab. name: gitlabRunner.status.token, key: "runner-token", }, }, }, ], }, ], }, }, }, }, { provider: k8sProvider }); // Export values that might be useful to access. export const runnerRegistrationToken = gitlabRunner.registrationToken; export const runnerDeploymentName = runnerDeployment.metadata.name;

    In the above Pulumi program:

    • We define a gitlab.Runner to register a runner with the GitLab instance using the GitLab provider and the provided registration token.
    • We create a Kubernetes Deployment using the @pulumi/kubernetes provider to manage the GitLab Runner pods within our Kubernetes cluster. The registration token is secured using Kubernetes Secrets, which is why the token is referred from status.token of the gitlab.Runner.
    • The runner is configured with a specific environment variable GITLAB_RUNNER_TOKEN that uses the runner token obtained during registration.
    • The deployment ensures that the GitLab Runner container will run and be managed by Kubernetes, with the number of replicas set according to your scalability needs.
    • We export the runner registration token and the deployment name for easy reference.

    This is a basic setup for GitLab Runners in Kubernetes. Depending on your requirements, you might have to customize it further, such as setting specific resource requests and limits, using custom runner images, or setting advanced runner configurations.