1. Deploy the mongodb-bi-connector helm chart on Azure Kubernetes Service (AKS)


    Sure, I'll guide you through the process of deploying the MongoDB BI Connector using a Helm chart on Azure Kubernetes Service (AKS) with Pulumi using TypeScript.

    Firstly, let me explain the steps we'll be covering:

    1. Setting up the AKS Cluster: We need a Kubernetes cluster to deploy our applications. Here, we'll create an AKS cluster.

    2. Installing Helm and the MongoDB BI Connector Chart: Once we have a cluster, we'll configure Pulumi to install the MongoDB BI Connector Helm chart into the AKS cluster.

    3. Configuring Access and Services: We'll ensure that the necessary Kubernetes configurations are in place to access our BI connector.

    Here's what the Pulumi code will generally look like:

    1. Define resources for the AKS cluster including node pools and service principals.
    2. Use a Pulumi package that includes Helm, to deploy the BI connector.
    3. Export any necessary outputs, such as the Kubernetes cluster name or the MongoDB BI Connector service endpoint.

    Now, here's the actual Pulumi program to accomplish this. We'll provide comments throughout the code to help you understand each part.

    import * as pulumi from "@pulumi/pulumi"; import * as azure from "@pulumi/azure-native"; // Step 1: Creating AKS cluster const resourceGroup = new azure.resources.ResourceGroup("resourceGroup", { resourceGroupName: "aksResourceGroup", location: "East US", // You can choose a different Azure region. }); // Create an AKS cluster. const cluster = new azure.containerservice.ManagedCluster("aksCluster", { resourceGroupName: resourceGroup.name, // Define the properties for the AKS cluster here. agentPoolProfiles: [{ count: 3, // Number of nodes in the node pool maxPods: 110, // Max pods per node vmSize: "Standard_DS2_v2", // VM size for the nodes osType: "Linux", // Operating System type for the nodes type: "VirtualMachineScaleSets", // Using VMSS for node scaling }], dnsPrefix: "akspulumi", kubernetesVersion: "1.19.7", // Specify your desired Kubernetes version linuxProfile: { // SSH access configuration adminUsername: "testuser", ssh: { publicKeys: [{ keyData: "YOUR_SSH_PUBLIC_KEY" }], // Replace with your public SSH key }, }, identity: { type: "SystemAssigned", // Using a system assigned identity for the AKS cluster }, }); //Export the kubeconfig for the cluster export const kubeconfig = cluster.kubeConfigRaw; // Step 2: Deploying MongoDB BI Connector using Helm import * as k8s from "@pulumi/kubernetes"; // Create a k8s provider with our kubeconfig. const provider = new k8s.Provider("k8sProvider", { kubeconfig: cluster.kubeConfigRaw, }); // Install the MongoDB BI Connector Helm Chart const biConnectorChart = new k8s.helm.v3.Chart("mongodb-bi-connector", { // Replace with the actual chart name and the version chart: "mongodb-bi-connector", version: "chart-version", fetchOpts: { repo: "https://helm.mongodb.com/", }, }, { provider: provider }); // Step 3: Configuring Access and Services (this step depends on what your needs are) // Once your chart is deployed, you might need to configure a service or ingress // to expose your MongoDB BI Connector. This greatly depends on your application and // MongoDB setup.

    To use the above code, replace YOUR_SSH_PUBLIC_KEY with the actual public key you'd like to use for SSH access to the cluster nodes. You will also need to replace "mongodb-bi-connector" and chart-version with the actual name of the MongoDB BI Connector Helm chart and its version. You can find this information in the MongoDB official documentation or Helm repository where the chart is published.

    Remember, before you can successfully run this code, you will need to have the following prerequisites in place:

    • Pulumi CLI installed and configured to talk to an Azure subscription.
    • An Azure Resource Group, or the permission to create one.
    • Azure AD Service Principal with contributor role to the created Resource Group.
    • Valid SSH public key for accessing AKS nodes.
    • Kubernetes Pulumi package installed in your project workspace.

    After you have these prerequisites in place, you can run pulumi up to create the resources with Pulumi. The command will output a planned set of actions, and you can proceed to apply those to your Azure environment.

    Don't forget, in production scenarios, you would need to manage sensitive information like SSH keys and Helm chart versions securely, possibly using the Pulumi Config system, or an external secret store.