mypy-boto3-mwaa

import boto3 from mypy_boto3_mwaa import MWAAClient from mypy_boto3_mwaa.type_defs import CreateEnvironmentRequestRequestTypeDef def create_mwaa_environment( env_name: str, bucket_name: str, execution_role_arn: str ) -> dict: client: MWAAClient = boto3.client('mwaa') # Example of using a TypedDict for a complex request body environment_config: CreateEnvironmentRequestRequestTypeDef = { 'Name': env_name, 'SourceBucketArn': f'arn:aws:s3:::{bucket_name}', 'ExecutionRoleArn': execution_role_arn, 'AirflowVersion': '2.6.3', 'EnvironmentClass': 'mw1.small', 'MaxWorkers': 1, 'NetworkConfiguration': { 'SubnetIds': ['subnet-xxxxxxxxxxxxxxxxx'], # Replace with actual subnet IDs 'SecurityGroupIds': ['sg-xxxxxxxxxxxxxxxxx'] # Replace with actual security group IDs } } # This call is for demonstration and requires a valid MWAA setup # response = client.create_environment(**environment_config) # print(f"Environment creation initiated: {response}") # For a simple type-checked call, without actual creation: list_envs_response = client.list_environments() print(f"Listed MWAA environments: {list_envs_response.get('Environments')}") return list_envs_response # Example usage (requires valid AWS credentials and configuration) # This code will run and show type checking benefits even if the API call is commented out if __name__ == "__main__": # Replace with your actual values for a real run # env_name = "my-airflow-env" # bucket_name = "my-airflow-bucket" # execution_role_arn = "arn:aws:iam::123456789012:role/MyMWAAExecutionRole" # create_mwaa_environment(env_name, bucket_name, execution_role_arn) create_mwaa_environment( 'example-env', 'example-bucket', 'arn:aws:iam::123456789012:role/ExampleRole' )