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


    To deploy the Atlassian Confluence Helm chart on Google Kubernetes Engine (GKE), you need to follow these general steps:

    1. Create a GKE cluster: You'll need a Kubernetes cluster running on GKE where you can deploy the Helm chart. This involves choosing the right machine types, deciding on the number of nodes, and configuring other cluster settings according to your needs.

    2. Install and initialize Helm: Helm is a package manager for Kubernetes that simplifies the deployment and management of applications. You must install Helm on your local machine and initialize it so that it can communicate with the Kubernetes cluster.

    3. Deploy the Atlassian Confluence Helm chart: Using Helm, you'll fetch the Atlassian Confluence Helm chart (which describes the desired state of the application), and then deploy it to your GKE cluster.

    4. Configure Ingress, Services, and other resources: If your Atlassian Confluence deployment needs to be accessed from the internet, you may need to set up an Ingress resource. You'll also need to configure any necessary Services, Persistent Volumes, and other Kubernetes resources that Confluence may need to operate correctly.

    Below is a Pulumi program written in TypeScript that demonstrates how to automate these steps. Make sure you have the Pulumi CLI installed and configured for Google Cloud, and you have Helm and kubectl installed on your machine to work with Kubernetes clusters and Helm charts.

    import * as gcp from "@pulumi/gcp"; import * as kubernetes from "@pulumi/kubernetes"; import * as pulumi from "@pulumi/pulumi"; // Step 1: Create a GKE cluster const cluster = new gcp.container.Cluster("confluence-cluster", { initialNodeCount: 2, nodeVersion: "latest", minMasterVersion: "latest", nodeConfig: { machineType: "n1-standard-2", // suitable for small-scale production deployments 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 Cluster name export const clusterName = cluster.name; // Step 2: Configure Kubectl to connect to the new cluster const k8sProvider = new kubernetes.Provider("k8s-provider", { kubeconfig: cluster.kubeConfigRaw, }); // Step 3: Deploy the Atlassian Confluence Helm chart using the Helm provider const confluenceChart = new kubernetes.helm.v3.Chart("confluence", { chart: "atlassian-confluence", version: "x.y.z", // Specify the exact chart version fetchOpts: { repo: "https://atlassian.github.io/data-center-helm-charts/", // Helm repository that hosts the Atlassian charts }, }, { provider: k8sProvider }); // Export the status of the Helm deployment export const confluenceStatus = confluenceChart.status; // Notes about accessing Confluence: // - After deploying, it can take a couple of minutes for the external IP to be available. // - You can get the external IP by querying the appropriate Service resource with `kubectl`. // - Set up DNS records to point to the external IP if you want a domain name to resolve to your Confluence instance.

    This Pulumi program defines a GKE Kubernetes cluster with a set of compute nodes suitable for small to medium workloads. Once the cluster is created, Pulumi uses the kubernetes provider to allow you to interact with it using kubectl.

    Next, we define a Helm chart resource for deploying Atlassian Confluence from the specified Helm repository. Replace x.y.z with the correct version number of the Atlassian Confluence Helm chart you wish to deploy.

    After running this program with pulumi up, you'll have a GKE cluster running with Atlassian Confluence deployed. You can then proceed to configure access to it, such as setting up an Ingress or LoadBalancer service to expose Confluence to users over the internet. Remember that you'll typically want to manage DNS settings to point a user-friendly domain name to the external IP of Confluence's LoadBalancer, among other post-deployment tasks like security, storage, and user configuration.