1. Deploy the kafka-helm-chart helm chart on Google Kubernetes Engine (GKE)


    Deploying a Kafka Helm chart on Google Kubernetes Engine (GKE) involves several steps:

    1. Set up a GKE cluster: First, we need to create a Kubernetes cluster on GKE where our Helm chart will be deployed.
    2. Install and configure Helm on your local machine: Helm is a package manager for Kubernetes, which allows you to define, install, and upgrade even the most complex Kubernetes applications.
    3. Deploy the Kafka Helm chart: Finally, we can use Helm to install the Kafka chart on our GKE cluster.

    The following Pulumi program written in TypeScript will guide you through these steps to set up your infrastructure.

    First, ensure that you have Pulumi installed and configured with your GCP credentials. You'll also need to install the @pulumi/kubernetes and @pulumi/gcp NPM packages, which provide the necessary functionality to work with Kubernetes resources and GCP services, respectively.

    Here's a complete program to deploy Kafka on GKE using Pulumi:

    import * as pulumi from "@pulumi/pulumi"; import * as gcp from "@pulumi/gcp"; import * as k8s from "@pulumi/kubernetes"; // Step 1: Create a GKE cluster const cluster = new gcp.container.Cluster("kafka-cluster", { initialNodeCount: 2, minMasterVersion: "latest", nodeVersion: "latest", nodeConfig: { machineType: "n1-standard-1", // You might need to change this based on your requirements oauthScopes: [ "https://www.googleapis.com/auth/compute", "https://www.googleapis.com/auth/devstorage.read_only", "https://www.googleapis.com/auth/logging.write", "https://www.googleapis.com/auth/monitoring" ], }, }); // Export the Kubeconfig 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 `; }); // Step 2: Use the generated kubeconfig to interact with the cluster const provider = new k8s.Provider("k8s-provider", { kubeconfig: kubeconfig, }); // Step 3: Deploy Kafka using the Helm chart const kafka = new k8s.helm.v3.Release("kafka", { chart: "kafka", version: "5.3.1", // Make sure to specify the version you want to deploy namespace: "kafka", repositoryOpts: { repo: "https://charts.bitnami.com/bitnami", }, }, { provider: provider }); // Make sure to pass the provider // Export the Kafka chart name export const kafkaChartName = kafka.name;

    This program performs the following actions:

    • Defines a GKE cluster with the desired node count and machine types.
    • Exports a kubeconfig file that you can use to interact with the cluster using kubectl.
    • Defines a Kubernetes provider that uses the exported kubeconfig to manage resources in the GKE cluster.
    • Deploys the Kafka Helm chart from the Bitnami Helm repository to the GKE cluster using the predefined Kubernetes provider.

    Do take note of the version of the Kafka chart specified in the program. Always ensure that you have the correct chart version and it is compatible with your Kubernetes cluster version.

    After deploying this Pulumi program, you can use the exported kubeconfig to interact with your GKE cluster and Kafka deployment. To apply the program, run pulumi up in the directory where this script resides, and Pulumi will provision the resources as defined.

    Remember to clean up resources that you no longer need by running pulumi destroy to avoid unnecessary charges.