1. Real-Time Anomaly Detection Alerts for DigitalOcean Managed Databases


    To set up real-time anomaly detection alerts for DigitalOcean managed databases, you will need to use the DigitalOcean provider for Pulumi. The resources potentially involved in setting up such alerts are likely to include the monitoring and alert policies around the database.

    Let's go through the steps involved in setting up these alerts using Pulumi:

    1. DigitalOcean Database Cluster: Before creating alerts, you need a managed database cluster on DigitalOcean. I'll assume you already have a cluster or know how to create one.

    2. Monitoring and Alert Policy: You will use the MonitorAlert resource to define an alert policy that will trigger based on certain performance metrics like CPU usage, memory usage, etc.

    3. Alert Notifications: You have the ability to define how you receive alerts. This can be done through emails and Slack.

    In the following Pulumi program, we'll be creating a mock-up alert that could be used to monitor an anomaly like high CPU usage. This is just an example; you would need to adjust the alert based on the specific metric(s) that are relevant to your anomaly detection criteria.

    import pulumi import pulumi_digitalocean as digitalocean # Assume this is your existing DigitalOcean database cluster ID. database_cluster_id = "your-database-cluster-id" # This creates a monitor alert for the DigitalOcean managed database # This is an example of an alert that triggers when the CPU usage is greater than 80% over the last 5 minutes. # You can adjust the `value` and other parameters to match the anomaly detection conditions you require. cpu_monitor_alert = digitalocean.MonitorAlert("cpuMonitorAlert", type="v1/insights/droplet/cpu", value=80, # The value to trigger the alert. Here, it refers to 80% CPU usage. window="5m", # The time window for measuring the metric value. compare="GreaterThan", # The comparison operation for the value. description="Alert when CPU exceeds 80%", enabled=True, tags=["managed-database"], # You can set custom tags for your alert entities=[database_cluster_id], # Specifies the entities to apply the alert to. alerts=digitalocean.MonitorAlertAlertsArgs( emails=["admin@example.com"], # Email addresses to send alerts to. slacks=[digitalocean.MonitorAlertAlertsSlackArgs( url="https://hooks.slack.com/services/T00000000/B00000000/XXXXXXXXXXXXXXXXXXXXXXXX", # The Slack webhook URL. channel="#alerts" # The Slack channel to send alerts to. )] ) ) # Export the ID of the monitor alert so that it can be referenced later if needed. pulumi.export("monitor_alert_id", cpu_monitor_alert.id)

    In the code above:

    • I've defined a MonitorAlert resource named cpu_monitor_alert that monitors CPU usage.
    • The alert is configured to fire when the CPU usage is greater than 80% over the last 5 minutes. This is defined by the value, window, and compare properties.
    • The alerts property is used to specify where and how the alerts will be sent. In this case, notifications are sent via email and posted to a specified Slack channel.
    • The MonitorAlert resource takes an array of entity IDs to which the alert policy will be applied. I have added a placeholder value your-database-cluster-id, which you would replace with the actual ID of your managed database cluster.
    • Finally, pulumi.export is used to output the ID of the monitor alert, which is useful if you need to reference this alert in other parts of your infrastructure or in your applications.

    Remember to replace your-database-cluster-id, the email addresses, and the Slack webhook URL and channel to match your actual setup.

    This program is a starting point for setting up real-time anomaly detection on DigitalOcean managed databases. Depending on the anomaly you wish to detect, you would configure the alert differently, potentially looking at different types of data, thresholds, and time windows.