1. Anomaly Detection System Alerts with MongoDB Atlas


    To set up an anomaly detection system with alerts in MongoDB Atlas using Pulumi, you would typically need a MongoDB Atlas project and then configure alert configurations. The alert configurations watch indicators such as metric thresholds or specific events that might signify an anomaly. When an alert condition is met, notifications can be sent out through various channels, like emails, SMS, or integration with Slack, PagerDuty, etc.

    In Pulumi, the MongoDB Atlas provider offers the AlertConfiguration resource, which you can use to create and manage alert configurations. Here's how you can set up basic anomaly detection alerts with MongoDB Atlas using Pulumi and Python:

    1. Initialize a Pulumi Project: You first need to create a new Pulumi project or use an existing one, ensuring it's configured with the right credentials to access MongoDB Atlas.

    2. Define MongoDB Atlas Project: If you haven't already set one up, you'll need to define a MongoDB Atlas project resource using the Project resource.

    3. Set Up Alert Configuration: Using the AlertConfiguration resource, specify the alert conditions, such as the metric name and threshold values. You will also configure notification settings within this resource.

    4. Deploy with Pulumi: Once you've described your infrastructure as code, you'll deploy it with Pulumi. It will create the resources defined in your Python program.

    Let's walk through a Python program that performs the steps outlined above:

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Configuring MongoDB Atlas provider with required details such as the public/private API keys # and the organization ID where the project will be created. You can configure these settings using # Pulumi config or environment variables. mongo_atlas_provider = mongodbatlas.Provider("mongo_atlas_provider", public_key="your_atlas_public_key", private_key="your_atlas_private_key", org_id="your_org_id" ) # Create a new project in MongoDB Atlas. project = mongodbatlas.Project("project", name="AnomalyDetection", org_id="your_org_id", provider=mongo_atlas_provider ) # Configure the anomaly detection alert using the AlertConfiguration resource. # Depending on the types of anomalies you want to detect, you may configure different metrics and thresholds. # For example, if you want to detect a sudden increase in CPU usage, you would create a CPU Utilization alert. anomaly_alert = mongodbatlas.AlertConfiguration("anomaly_alert", project_id=project.id, enabled=True, event_type="OUTSIDE_METRIC_THRESHOLD", metric_threshold_config=mongodbatlas.AlertConfigurationMetricThresholdConfigArgs( metric_name="ASSERT_REGULAR", mode="AVERAGE", operator="GREATER_THAN", threshold=100.0, units="RAW" ), notifications=[ mongodbatlas.AlertConfigurationNotificationArgs( type_name="GROUP", interval_min=5, delay_min=0, sms_enabled=False, email_enabled=True, email_address="your-email@example.com" ) ], provider=mongo_atlas_provider ) # Export the project ID and alert configuration ID to use them outside of Pulumi. pulumi.export("project_id", project.id) pulumi.export("alert_configuration_id", anomaly_alert.id)

    In this program:

    • We setup a provider for MongoDB Atlas which will use your credentials for authentication.
    • We create a MongoDB Atlas project named "AnomalyDetection".
    • We configure an AlertConfiguration that defines the conditions under which an alert should be triggered. In this case, it's a metric_threshold_config set up for the metric "ASSERT_REGULAR". If the average value of this metric goes above 100, the alert configuration will trigger. Please replace "ASSERT_REGULAR" with the actual metric you want to monitor for anomalies.
    • We are configuring the notifications to be sent by email. The frequency and delay of the alerts, as well as the recipient's email, are set in the notifications property of AlertConfiguration.
    • Finally, we export project_id and alert_configuration_id for easy access to these resources' identifiers outside of Pulumi.

    Before running this program, replace your_atlas_public_key, your_atlas_private_key, your_org_id, and your-email@example.com with your actual MongoDB Atlas public/private keys, organization ID, and the desired email address for alerts.

    Once your Pulumi program is ready, you can deploy it using the Pulumi CLI. During deployment, Pulumi will issue the necessary API calls to MongoDB Atlas to create and configure your project and alert system based on the infrastructure code you've written.