1. MongoDB Atlas Alerts for AI Workload Scaling Events


    MongoDB Atlas provides a powerful set of features for managing your databases in the cloud, including the ability to monitor your clusters and set up alerts for various events. Alert configurations in MongoDB Atlas can be used to send notifications or trigger actions based on specific conditions or events within your databases. For AI workload scaling events, you might want to be alerted when usage patterns suggest that scaling is necessary to meet demand or maintain performance.

    In this Pulumi Python program, we're going to set up a MongoDB Atlas alert configuration using the mongodbatlas.AlertConfiguration resource. This configuration will track a particular metric that may indicate the need to scale our AI workloads. For example, you might want to track CPU utilization or memory usage, and when a certain threshold is reached, an alert will be generated.

    Here's the Python program that sets up an alert configuration for such scaling events:

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Configure the MongoDB Atlas Alert for AI Workload Scaling Events # Replace the placeholders with your actual MongoDB Atlas Project ID and appropriate API keys. alert_configuration = mongodbatlas.AlertConfiguration("aiWorkloadScalingAlert", enabled=True, event_type="OUTSIDE_METRIC_THRESHOLD", metric_threshold_config=mongodbatlas.AlertConfigurationMetricThresholdConfigArgs( metric_name="CPU_USAGE", # Choose the metric name relevant to your AI workload scaling needs operator="GREATER_THAN", threshold=80, # Set the threshold value appropriate for your AI workload scaling mode="RAW", units="PERCENTAGE", ), notifications=[ mongodbatlas.AlertConfigurationNotificationArgs( type_name="EMAIL", email_address="your-email@example.com", # Replace with the email address for receiving alerts ), ], project_id="your_mongodb_atlas_project_id", # Replace with your MongoDB Atlas project ID ) # Export the ID of the alert configuration pulumi.export("alert_configuration_id", alert_configuration.id)

    This program does the following:

    1. Imports the required Pulumi and pulumi_mongodbatlas packages.
    2. Creates an alert configuration using the mongodbatlas.AlertConfiguration resource.
    3. Configures the alert to be enabled and to trigger on the OUTSIDE_METRIC_THRESHOLD event type.
    4. Specifies the metric threshold configuration. In this example, it's set to trigger when the CPU_USAGE exceeds 80%.
    5. Sets up the notification channel. This example uses email notifications, but Atlas supports other types like SMS, Slack, or webhooks.
    6. Requires a valid MongoDB Atlas Project ID where the cluster is located.
    7. Exports the ID of the alert configuration for reference.

    Be sure to replace the placeholder values with your actual project ID, metric name, threshold, and notification information before running this Pulumi program.

    Ensure you have the Pulumi CLI installed and configured with the appropriate credentials.

    To apply this Pulumi configuration to your cloud infrastructure, save this code to a file named __main__.py, and run pulumi up from the command line in the same directory as your file.

    For detailed documentation on the properties and methods used in this program, you can refer to the MongoDB Atlas Provider documentation for the Alert Configuration resource: mongodbatlas.AlertConfiguration.