1. Serverless Image Processing Pipelines with AWS Step Functions


    Serverless architectures are an effective way to build scalable and cost-effective applications or services without having to manage server instances directly. In the case of an image processing pipeline, a serverless architecture might typically involve services such as AWS Lambda for the execution of image processing tasks, Amazon S3 for storing inputs and outputs, AWS Step Functions to orchestrate the workflow, and potentially other services such as AWS API Gateway for triggering the pipeline via HTTP requests.

    In this Pulumi Python program, we'll create a serverless image processing pipeline using AWS Step Functions. The pipeline will orchestrate the flow of image processing using Lambda functions and store the processed images in S3. We will use AWS Step Functions to coordinate the tasks. AWS Step Functions allow you to build applications from individual components that each perform a discrete function, enabling you to scale and change applications quickly.

    The program will accomplish the following:

    • Create an S3 bucket to store input and processed images.
    • Set up IAM roles and policies to grant the necessary permissions for Lambda and Step Functions to access S3 and execute tasks.
    • Provision Lambda functions for image processing tasks.
    • Define a Step Function state machine to orchestrate the image processing workflow.

    Here is how you could create such an infrastructure with Pulumi:

    import pulumi import pulumi_aws as aws # Creating an S3 bucket to store the original and processed images. images_bucket = aws.s3.Bucket("imagesBucket") # Create an IAM role that AWS Lambda can assume lambda_execution_role = aws.iam.Role("lambdaExecutionRole", assume_role_policy=aws.iam.get_policy_document( statements=[aws.iam.GetPolicyDocumentStatementArgs( effect="Allow", principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs( type="Service", identifiers=["lambda.amazonaws.com"] )], actions=["sts:AssumeRole"] )] ).json) # Attach the AWS managed policy for Lambda to write logs to CloudWatch and access the S3 bucket. aws.iam.RolePolicyAttachment("lambdaLogs", role=lambda_execution_role.name, policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_BASIC_EXECUTION_ROLE.value) aws.iam.RolePolicy("lambdaS3Access", role=lambda_execution_role.id, policy=images_bucket.arn.apply(lambda arn: json.dumps({ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": ["s3:PutObject", "s3:GetObject"], "Resource": [f"{arn}/*"] }] })) ) # Create a Lambda function to process the images image_processor_lambda = aws.lambda_.Function("imageProcessorLambda", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./path_to_your_image_processing_code") }), role=lambda_execution_role.arn, handler="index.handler", # assuming the python file name is 'index.py' and the handler function is 'handler' runtime=aws.lambda_.Runtime.PYTHON_3_8) # Create an AWS Step Functions State Machine to orchestrate the tasks state_machine_definition = """{ "Comment": "A serverless image processing state machine", "StartAt": "ProcessImage", "States": { "ProcessImage": { "Type": "Task", "Resource": "${image_processor_lambda_arn}", "End": true } } }""" state_machine = aws.sfn.StateMachine("imageProcessingStateMachine", role_arn=lambda_execution_role.arn, definition=image_processor_lambda.arn.apply(lambda arn: state_machine_definition.replace("${image_processor_lambda_arn}", arn)) ) # Export the names and URLs of the created resources pulumi.export('images_bucket_name', images_bucket.id) pulumi.export('image_processor_lambda_name', image_processor_lambda.id) pulumi.export('state_machine_name', state_machine.id)

    Explanation of the code:

    • S3 Bucket: This is the storage for the images. The images to be processed will be uploaded to this bucket, and the processed images can also be stored here.
    • IAM Role and Policy: These are required to grant AWS Lambda and AWS Step Functions the necessary permissions to execute and manage the tasks, as well as access the S3 bucket.
    • Lambda Function: This is the serverless compute part where the image processing code will run. You must deploy your image processing application code in a directory; in the code, we pointed to ./path_to_your_image_processing_code.
    • Step Function State Machine: This orchestrates the workflow, calling the Lambda function with the input from the S3 bucket to process the image and manage the process flow.

    Remember to replace "./path_to_your_image_processing_code" with the path to your own image processing code package. Your handler file and function within the package need to be specified in the handler property of the Lambda function resource.

    Make sure your AWS credentials are set up properly and you have the necessary permissions to create these resources. Save the contents to a __main__.py file and run pulumi up to deploy your infrastructure.