1. Automated Anomaly Detection Alerts with Lambda Functions


    To set up automated anomaly detection alerts using AWS services with Pulumi, we'll utilize several AWS resources:

    1. AWS Lambda Function - This will contain the code to handle anomaly detection alerts. When invoked, the function processes the alert event and can perform actions such as sending notifications, logging details, or initiating corrective workflows.

    2. AWS CloudWatch - CloudWatch will monitor various metrics and logs to define what constitutes an anomaly. It can trigger an alarm when an anomaly is detected according to the specified conditions.

    3. AWS Lookout for Metrics (via aws-native.lookoutmetrics.Alert) - This is an AWS service that applies machine learning to detect anomalies in your data.

    4. AWS SNS Topic - Once an alert is fired by AWS Lookout for Metrics, an AWS Simple Notification Service (SNS) topic will help to forward this alert to the subscribed AWS Lambda function or any other subscribers (like emails, SMS, etc.).

    Below is a detailed Pulumi Python program that provisions these resources and sets up the infrastructure to trigger a Lambda function when anomalies are detected:

    1. Define a Lambda function that will be triggered upon an alert.
    2. Create an SNS topic that the Lambda function will subscribe to.
    3. Define an anomaly detector in AWS Lookout for Metrics and associate an alert with this detector, to send notifications to the SNS topic when anomalies are detected.
    import pulumi import pulumi_aws as aws # Create an IAM role for the Lambda function lambda_exec_role = aws.iam.Role("lambdaExecRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""" ) # Attach the AWSLambdaBasicExecutionRole policy to the IAM role lambda_exec_policy = aws.iam.RolePolicyAttachment("lambdaExecPolicy", policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole", role=lambda_exec_role.name ) # Define the Lambda function anomaly_detection_lambda = aws.lambda_.Function("anomalyDetectionLambda", runtime=aws.lambda_.Runtime.PYTHON3_8, role=lambda_exec_role.arn, handler="handler.main", code=pulumi.FileArchive("./anomaly_detection_lambda.zip") ) # Create an SNS topic that the Lambda will subscribe to for receiving alerts anomaly_alert_topic = aws.sns.Topic("anomalyAlertTopic") # Subscribe the Lambda to the SNS topic lambda_subscription = aws.sns.TopicSubscription("lambdaSubscription", topic=anomaly_alert_topic.arn, protocol="lambda", endpoint=anomaly_detection_lambda.arn ) # Grant the SNS service permission to invoke the Lambda function sns_invoke_permission = aws.lambda_.Permission("snsInvokePermission", action="lambda:InvokeFunction", function=anomaly_detection_lambda.name, principal="sns.amazonaws.com", source_arn=anomaly_alert_topic.arn ) # Here we would define the AWS Lookout for Metrics anomaly detector, but as # of my knowledge cutoff in September 2023, Pulumi does not have a higher # level Pulumi library for AWS Lookout for Metrics. As such, we'll simulate # the configuration using a generic `aws.sdk.Call` and an inline definition. # In a real implementation, one would use the AWS SDK for Python (Boto3) or # an AWS CloudFormation resource supported by Pulumi. # Placeholder for AWS Lookout Metrics Detector and Alert: # lookout_detector = # ... Create Lookout Metrics detector, possibly having to use Dynamic Provider # lookout_alert = # ... Create Lookout Metrics alert associated with the detector to SNS topic # pulumi.export("sns_topic_arn", anomaly_alert_topic.arn) # pulumi.export("lambda_function_name", anomaly_detection_lambda.name)

    Breakdown of the Program:

    • Define an IAM Role for Lambda Function: The Lambda function must have an IAM Role with the necessary permissions to be executed. Here, the AWSLambdaBasicExecutionRole policy gives our Lambda function permissions to write logs to CloudWatch.

    • Define the Lambda Function: This is where your Python code resides; it's packaged as a ZIP file. This code will be invoked when an SNS message is received. Ensure you have a handler.py with a main method in your ZIP file.

    • Create an SNS Topic: This is used as a communication channel to send messages – in this case, anomaly detection alerts – to subscribing endpoints, such as our Lambda function.

    • Subscribe Lamba to SNS: This connects your SNS topic to the Lambda function so that it will be triggered when a message (anomaly alert) is published to the topic.

    • Set Lambda Permissions for SNS: You need to grant permissions to SNS to invoke the Lambda function when a message (an alert) is available on the SNS topic.

    The anomaly detector and alert configuration is not implemented in this program because, as of now, there is no higher-level Pulumi library support for AWS Lookout for Metrics to the best of my knowledge. Normally, you would define the anomaly detector, and then the alert is attached to the detector to monitor for anomalies. When an anomaly is detected, Lookout for Metrics sends a message to the SNS topic.

    Forever, always remember to include the code of your Lambda function within a zip file. Here, it's referenced as anomaly_detection_lambda.zip, which contains your handler code (handler.py) with a main method in it.

    For a much more sophisticated setup, including a complete Lookout for Metrics and anomaly detection, additional AWS SDK operations might be necessary, or you can expect higher-level support from Pulumi in future releases.

    Note: Before running this program with Pulumi CLI, ensure you have AWS credentials configured properly on your local machine and AWS CLI installed. You need to execute pulumi up within the directory where Pulumi.yaml and this Python file exist. Also, ensure the Lambda source code zip file is present in the same directory.