1. Deploy the grafana-dashboards-rancher helm chart on Google Kubernetes Engine (GKE)


    To deploy the grafana-dashboards-rancher Helm chart on Google Kubernetes Engine (GKE), we'll need to perform a few steps. Below is a high-level outline of the process:

    1. Set up a GKE Cluster: We will create a new GKE cluster where our Grafana dashboard will live.
    2. Install the Helm Chart: Once we have a cluster, we can install the Helm chart for Grafana within the Kubernetes cluster.

    We will be using the @pulumi/gcp package to create the GKE cluster and @pulumi/kubernetes to deploy the Helm chart to the GKE cluster.

    Here's a detailed explanation of each step in the code:

    1. Create a GKE Cluster:

    • Import @pulumi/gcp package to manage resources on Google Cloud.
    • Use gcp.container.Cluster class to create a new GKE cluster.
    • Define configuration details for the cluster, such as the machine type and the number of nodes.

    2. Deploy the Helm Chart:

    • Import @pulumi/kubernetes package to interact with Kubernetes clusters.
    • Configure the Pulumi program to use the newly created GKE cluster's kubeconfig as the context.
    • Use the kubernetes.helm.v3.Chart class to deploy the grafana-dashboards-rancher Helm chart to the GKE cluster.
    • Provide necessary configurations such as chart version or any custom values you may wish to override in the chart.

    Now let's create the Pulumi program in TypeScript to perform these tasks.

    import * as gcp from "@pulumi/gcp"; import * as k8s from "@pulumi/kubernetes"; import * as pulumi from "@pulumi/pulumi"; // Create a GKE cluster const cluster = new gcp.container.Cluster("cluster", { initialNodeCount: 2, nodeVersion: "latest", minMasterVersion: "latest", nodeConfig: { machineType: "n1-standard-1", // The type of machine to use for nodes. }, }); // Export the Kubernetes cluster name and kubeconfig export const clusterName = cluster.name; export const kubeconfig = pulumi. all([ cluster.name, cluster.endpoint, cluster.masterAuth ]). apply(([ name, endpoint, masterAuth ]) => { const context = `${gcp.config.project}_${gcp.config.zone}_${name}`; return `apiVersion: v1 clusters: - cluster: certificate-authority-data: ${masterAuth.clusterCaCertificate} server: https://${endpoint} name: ${context} contexts: - context: cluster: ${context} user: ${context} name: ${context} current-context: ${context} kind: Config preferences: {} users: - name: ${context} user: auth-provider: config: cmd-args: config config-helper --format=json cmd-path: gcloud expiry-key: '{.credential.token_expiry}' token-key: '{.credential.access_token}' name: gcp `; }); // Create a Kubernetes Provider instance with the kubeconfig const k8sProvider = new k8s.Provider("k8s-provider", { kubeconfig: kubeconfig, }); // Deploy Grafana dashboard using helm chart const grafana = new k8s.helm.v3.Chart("grafana", { chart: "grafana-dashboards-rancher", fetchOpts: { repo: "https://helm.rancher.io", }, }, { provider: k8sProvider }); // Export the Grafana service URL export const grafanaUrl = grafana.getResourceProperty("v1/Service", "grafana", "status").apply(status => `http://${status.loadBalancer.ingress[0].ip}`);

    Let me explain the code in detail:

    • The gcp.container.Cluster class is responsible for provisioning a new GKE cluster on Google Cloud. We're setting initialNodeCount to 2, which means we'll have two nodes in our cluster, and we're using standard machine types n1-standard-1.

    • After the cluster is created, we construct a kubeconfig using pulumi.all([...]).apply, which allows us to combine multiple output properties into a single object. The resulting kubeconfig is outputted from the program so it can be used by external tools if necessary.

    • We then create a Pulumi Kubernetes provider using this kubeconfig. This provider allows us to deploy Kubernetes resources to our GKE cluster.

    • The k8s.helm.v3.Chart class deploys the grafana-dashboards-rancher Helm chart from the https://helm.rancher.io repository to our cluster. The Grafana service is exposed through a LoadBalancer, and we export its IP address.

    Once you set up your Pulumi stack and authenticate with Google Cloud, you can deploy this program using Pulumi CLI commands:

    pulumi up

    This command creates all the resources defined in your Pulumi program and the output will show you the endpoints to access your Grafana dashboards. To interact with your Grafana instance, you typically use the Grafana URL provided in the outputs of your Pulumi program.