1. CloudWatch Alarms for Anomaly Detection Jobs


    CloudWatch Alarms are a feature in AWS that allow you to monitor your AWS resources and applications for any unusual activity or behavior. With CloudWatch Alarms, you can set up anomaly detection models that analyze the past behavior of a metric to determine a normal baseline, and then trigger an alarm if metrics go outside of the normal range.

    To create CloudWatch Alarms with anomaly detection in Pulumi, we'll use the aws.cloudwatch.MetricAlarm resource. Here's how you'd use it to set up anomaly detection for a particular metric:

    1. Define the metric you want to monitor using anomaly detection.
    2. Define the MetricAlarm with arguments for metricName, namespace, statistic, comparisonOperator, and specific anomaly detection parameters like threshold.

    Here is a Pulumi program written in Python that sets up a MetricAlarm for anomaly detection:

    import pulumi import pulumi_aws as aws # Define the CloudWatch Alarm using the MetricAlarm resource. # Replace 'YourMetricName' with the actual metric you're monitoring, # and 'YourNamespace' with the namespace for that metric. anomaly_detection_alarm = aws.cloudwatch.MetricAlarm("anomalyDetectionAlarm", # Alarm name that identifies the alarm's purpose alarm_name="MyAnomalyDetectionAlarm", # The metric you want to monitor for anomalies. metric_name="YourMetricName", # Namespace of the metric. namespace="YourNamespace", # Which metric statistic to use for the anomaly detection. statistic="Average", # You can specify the period over which the statistic is applied. period=300, # Evaluation period count is essential to determine the threshold breach. # For example, '2' would require two consecutive breach periods to alarm. evaluation_periods=2, # Comparison operator used to compare the current metric against the anomaly detection model. comparison_operator="GreaterThanUpperThreshold", # How to handle data points with insufficient data. treat_missing_data="missing", # Enable actions for the alarm. actions_enabled=True, # Actions to undertake when the alarm state is triggered. alarm_actions=["arn:aws:sns:us-west-2:123456789012:my-sns-topic"], # Set the threshold for triggering the alarm (this should typically be left as 'None' for anomaly detection). # The threshold is managed automatically by the CloudWatch service when using anomaly detection mode threshold=None, # Optional: You can add a description for the alarm. alarm_description="An alarm when the metric deviates from normal.", # Optional: Tag the alarm with additional metadata. tags={"Environment": "production"}) # Export the name of the alarm pulumi.export("alarm_name", anomaly_detection_alarm.alarm_name)

    In this Pulumi program, we're using the aws.cloudwatch.MetricAlarm resource to create an alarm that triggers based on an anomaly detection model. You'll need to fill in the metric_name and namespace with the actual metric you're monitoring.

    Additionally, the evaluation_periods parameter tells CloudWatch how many consecutive periods of the anomaly must occur for the alarm to change state. The comparison_operator defines what anomaly would trigger the alarm. In this case, "GreaterThanUpperThreshold" implies that the alarm will trigger when the metric is above the auto-generated threshold.

    You can specify the ARN for an SNS topic in alarm_actions where CloudWatch can send an alert when the alarm's state changes. Don't forget to replace the values in the above example with the specific details of your AWS environment.

    Finally, with Pulumi's export function, we're outputting the name of the alarm so that it can be easily identified in the output of your Pulumi deployment.

    Remember that for real-world usage, you'll need to have an SNS topic already set up for the notifications to be delivered. If you need to create a new SNS topic, you can use the aws.sns.Topic resource before creating the alarm and then reference the ARN of the newly created topic.