Serverless Anomaly Detection with AWS Serverless Application Repository

Question

Pulumi · Accepted Answer

Serverless Anomaly Detection is a pattern where you use managed services to monitor a dataset for unusual patterns or behaviors without provisioning or managing servers. AWS provides several managed services that can be used to create a serverless anomaly detection system, and the AWS Serverless Application Repository is a collection of serverless applications that can help you speed up development.

For building a Serverless Anomaly Detection system, we can leverage AWS Lambda for the data processing logic, Amazon S3 for storage, Amazon Simple Notification Service (SNS) for alerts, and possibly Amazon CloudWatch or any machine learning services AWS offers like Amazon SageMaker for monitoring and detecting the anomalies.

Below is a Pulumi Python program that represents a very simplified setup for serverless anomaly detection. This example will create an S3 bucket to store data, a Lambda function to process the data and detect anomalies, and an SNS topic to send alerts in case of detected anomalies. For detailed anomaly detection logic, you would need to implement the corresponding Lambda function code.

```python
import pulumi
import pulumi_aws as aws

# Create an S3 bucket to store data
data_bucket = aws.s3.Bucket("dataBucket")

# Create an IAM role and policy to allow Lambda to access S3 and SNS
lambda_role = aws.iam.Role("lambdaRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            },
            "Effect": "Allow",
            "Sid": ""
        }]
    }""")

lambda_policy = aws.iam.RolePolicy("lambdaPolicy",
    role=lambda_role.id,
    policy=pulumi.Output.all(data_bucket.arn).apply(lambda arn: f"""{{
        "Version": "2012-10-17",
        "Statement": [
            {{
                "Effect": "Allow",
                "Action": "s3:GetObject",
                "Resource": "{arn}/*"
            }},
            {{
                "Effect": "Allow",
                "Action": "sns:Publish",
                "Resource": "*"
            }}
        ]
    }}"""))

# Define a new serverless function to process the data
lambda_function = aws.lambda_.Function("anomalyDetector",
    code=pulumi.AssetArchive({
        '.': pulumi.FileArchive('./anomaly-detection-lambda')  # Directory with your Lambda handler and dependencies
    }),
    role=lambda_role.arn,
    handler="handler.main",  # Your handler file and method
    runtime="python3.8",  # RuntimeLangVersion Pulumi supports
)

# Create an SNS topic to send anomaly alerts
alert_topic = aws.sns.Topic("alertTopic")

# Grant permission for the Lambda function to publish to the SNS topic
lambda_topic_policy = aws.lambda_.Permission("lambdaTopicPolicy",
    action="lambda:InvokeFunction",
    function=lambda_function.arn,
    principal="sns.amazonaws.com",
    source_arn=alert_topic.arn,
)

# Export the names of the bucket and SNS topic
pulumi.export("bucket_name", data_bucket.id)
pulumi.export("alert_topic_arn", alert_topic.arn)
```

In this setup:

- First, we create an S3 bucket for the data that will be processed. Ideally, your data producers will be configured to deposit data into this bucket.
- We define an IAM role and policy that grants the necessary permissions to the Lambda function to read from S3 and publish messages to SNS.
- The Lambda function is defined with a reference to a code archive—be sure to replace `'./anomaly-detection-lambda'` with the path to your function's code. This function is expected to have the logic for anomaly detection.
- We set up an SNS topic for notifications. When the Lambda function detects an anomaly, it publishes a message to this topic.
- To enable the Lambda function to call SNS, we grant permissions using the `aws.lambda.Permission` resource.

This program provides a foundational serverless anomaly detection system which can be tailored and expanded to suit specific requirements, such as integrating with a machine learning model or additional AWS services.