1. Kubernetes MongoDB as a Backend for AI Dashboards


    To deploy a MongoDB instance on a Kubernetes cluster that can be used as a backend for AI Dashboards, we need to:

    1. Set up a Kubernetes cluster (for the sake of this guide, we'll assume you have one set up and configured for use with Pulumi).
    2. Create a dedicated namespace for MongoDB, to keep our deployment organized.
    3. Use a Deployment to manage a MongoDB pod.
    4. Use a Service to expose the MongoDB deployment within the cluster.
    5. Optionally, you can set up a PersistentVolume and a PersistentVolumeClaim to provide durable storage for MongoDB, ensuring the data remains intact even if the MongoDB pod gets restarted.

    We use Kubernetes resources from Pulumi's Kubernetes SDK in the example code. First, ensure you have the Pulumi CLI installed and configured for access to your Kubernetes cluster.

    Here's a simple program to create a MongoDB deployment on Kubernetes:

    import pulumi import pulumi_kubernetes as k8s # Create a Kubernetes Namespace for MongoDB mongo_ns = k8s.core.v1.Namespace("mongo-namespace", metadata={"name": "mongodb"}) # Define the MongoDB Deployment mongo_deployment = k8s.apps.v1.Deployment("mongo-deployment", metadata={ "namespace": mongo_ns.metadata["name"] }, spec={ "selector": { "matchLabels": { "app": "mongodb" } }, "replicas": 1, "template": { "metadata": { "labels": { "app": "mongodb" } }, "spec": { "containers": [{ "name": "mongo", "image": "mongo:4.4", "ports": [{ "containerPort": 27017, "name": "mongo" }], "env": [{ "name": "MONGO_INITDB_ROOT_USERNAME", "value": "mongo_user" }, { "name": "MONGO_INITDB_ROOT_PASSWORD", "value": "mongo_password" }] }] } } }) # Expose MongoDB using a Service mongo_service = k8s.core.v1.Service("mongo-service", metadata={ "namespace": mongo_ns.metadata["name"], "name": "mongodb-service" }, spec={ "type": "ClusterIP", "ports": [{ "port": 27017, "targetPort": 27017 }], "selector": { "app": "mongodb" } }) # Export the MongoDB service name and cluster IP for access within the cluster pulumi.export("mongo_service_name", mongo_service.metadata["name"]) pulumi.export("mongo_service_cluster_ip", mongo_service.spec["cluster_ip"])

    In this program:

    • We first create a namespace for MongoDB using k8s.core.v1.Namespace.
    • Next, we define the MongoDB deployment with a single replica, this is done using k8s.apps.v1.Deployment.
    • We specify the container image to be mongo:4.4. This image is pulled from Docker Hub and contains MongoDB server.
    • The ports list contains configuration for accessing MongoDB on its default port 27017.
    • Environment variables are used to set the MongoDB root user's credentials with MONGO_INITDB_ROOT_USERNAME and MONGO_INITDB_ROOT_PASSWORD. This is not a recommended practice for production and special measures like Kubernetes Secrets should be used for managing sensitive data.
    • Then, we define a Service of type ClusterIP to expose MongoDB within the cluster. Other types can be NodePort or LoadBalancer, but ClusterIP is usually sufficient for internal access.
    • Lastly, we export the MongoDB service name and cluster IP that can be used to connect to the MongoDB instance from other services within the same Kubernetes cluster.

    To apply this Pulumi program, save it to a file (e.g., mongo-deploy.py), and execute pulumi up in the command line, assuming your Pulumi project and stack are already configured. This will provision the resources on your Kubernetes cluster as per the configuration specified in the code.

    Remember, this code sets root credentials in plain text, which is not secure for a production environment. For a production setup, consider using Kubernetes Secrets or another secure way of managing sensitive information.