1. Real-time Dashboard Visualization with MongoDB Atlas and Grafana


    To create a real-time dashboard visualization using MongoDB Atlas and Grafana with Pulumi in Python, we will leverage several resources. We'll utilize MongoDB Atlas to host our database and Grafana for dashboard visualization. Pulumi provides integration with the required services, allowing us to define our infrastructure as code. Here's an outline of the steps we're going to take in the program:

    1. Set up a MongoDB Atlas cluster where our data will reside.
    2. Configure Grafana to visualize the data stored in MongoDB Atlas.
    3. Integrate Grafana with MongoDB Atlas.

    Here is a step-by-step guide on how we will achieve this using Pulumi:

    MongoDB Atlas Cluster Setup

    We will start by creating a new MongoDB Atlas project and a cluster within that project. We use mongodbatlas.Project to create a new project and mongodbatlas.Cluster to create a cluster in that project.

    Grafana Dashboard Setup

    We then configure a Grafana dashboard. Since Pulumi supports Grafana via the grafana package, we can use grafana.Dashboard to define a new dashboard. We'll need to supply the configuration in JSON format, which can be constructed manually or with the help of Grafana's user interface for designing dashboards.


    Once the MongoDB Atlas cluster and the Grafana dashboard are set, we need to add the data source in Grafana, which points to our MongoDB Atlas cluster. This step is typically done through the Grafana UI, but it can be automated using Grafana's API or Pulumi's support for API resources if available.

    Now let's translate these steps into a Pulumi program in Python:

    import pulumi import pulumi_grafana as grafana import pulumi_mongodbatlas as mongodbatlas # Create a new MongoDB Atlas project atlas_project = mongodbatlas.Project("my-atlas-project", org_id="mongodbatlas_org_id") # Specify your MongoDB Atlas organization ID here # Create a MongoDB Atlas cluster within the project atlas_cluster = mongodbatlas.Cluster("my-atlas-cluster", project_id=atlas_project.id, name="my-cluster", provider_name="AWS", provider_region_name="us-west-2", cluster_type="REPLICASET", replication_factor=3, provider_instance_size_name="M10", backup_enabled=True, provider_backup_enabled=True) # Grafana Dashboard configuration (JSON) is typically quite large and can be built using the Grafana UI first. # Here, it's simplified for illustration purposes. dashboard_config = { "dashboard": { "id": None, "uid": None, "title": "MongoDB Atlas Dashboard", "schemaVersion": 16, "version": 0 # Include other dashboard settings here } } # Convert the dashboard configuration into a JSON string as required by grafana.Dashboard import json dashboard_json_config = json.dumps(dashboard_config) # Create a new Grafana dashboard with the specified configuration grafana_dashboard = grafana.Dashboard("my-grafana-dashboard", config_json=dashboard_json_config) # Export relevant URLs or identifiers as outputs pulumi.export("atlas_project_id", atlas_project.id) pulumi.export("atlas_cluster_name", atlas_cluster.name) pulumi.export("grafana_dashboard_url", grafana_dashboard.url)

    In the above program:

    • org_id should be replaced with your MongoDB Atlas organization ID.

    • The Grafana dashboard configuration JSON (dashboard_config) should include all the necessary configurations that define the visualizations and queries to your MongoDB Atlas data source. The example above uses a simplified version of the configuration but in a real-world scenario, you should use the JSON that matches your chosen visualizations.

      To construct the configuration, use the Grafana UI to layout your dashboard and then export the JSON model. You can then use this JSON model as the config_json required for creating a grafana.Dashboard.

    • The pulumi.export calls at the end are used to output the generated IDs of the project and cluster, as well as the Grafana dashboard URL. You can use these outputs to access the resources in the Pulumi Console or other Pulumi programs.

    After you write and deploy this Pulumi program, you will have a MongoDB Atlas cluster set up to store your data and a Grafana dashboard to visualize that data in real time. You'll need to configure your Grafana instance to connect to MongoDB Atlas as a data source, after which you can start creating visualizations based on your data.