Posted on Apr 19, 2022 • Edited on Apr 27, 2022

Deploy an API fronted Lambda function using AWS CDK

This project is a minimum working example of deploying a Lambda function accessible through an API Gateway using Python flavored AWS CDK. The API allows you to pass data directly into the Lambda function and get a response back with a result.

For maybe a better way to do this using the new Lambda Function URL feature recently released by AWS see my post here.

What is possible using this pattern?

1) Pass data to the function, get a machine learning prediction back.
2) Request a file stored on S3, get a secure link back.
3) Send parameters to the function, get complex calculations back.
4) Database retrieval.
etc...

The Github repository can be found here.

CDK init & deploy

I won’t cover setting up CDK and bootstrapping the environment. You can find that information here.

Once you have set up CDK, we need to set up the project:

mkdir CDK_Lambda_API && cd CDK_Lambda_API
cdk init --language python
source .venv/bin/activate
Optional: If you need additional libraries in your Lambda function, add aws-cdk.aws-lambda-python-alpha to requirements.txt to allow custom builds during stack deployment using Docker.
pip install -r requirements.txt && pip install -r requirements-dev.txt

Now deploy empty stack to AWS:
cdk deploy

Stack design

This stack will deploy a lambda function using aws-lambda-python-alpha to build the function with all its additional libraries using a docker container. Make sure to have Docker installed and the daemon running before running cdk deploy.

# lambda_api_stack.py  from aws_cdk import Stack from aws_cdk import aws_apigateway as apigw from aws_cdk import aws_lambda as _lambda from aws_cdk import aws_lambda_python_alpha as _lambda_python from constructs import Construct class LambdaModelPredictionsStack(Stack): def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None: super().__init__(scope, construct_id, **kwargs) # in __init__ I like to initialize the infrastructure I will be creating  self.prediction_lambda = None self.gateway = None # Additional useful infrastructure might include an S3 bucket,  # an EFS store, SQS queue, etc.  self.build_infrastructure() def build_infrastructure(self): # For convenience, consolidate infrastructure construction  self.build_lambda() self.build_gateway() def build_lambda(self): self.prediction_lambda = _lambda_python.PythonFunction( scope=self, id="PredictionLambda", # entry points to the directory  entry="lambda_funcs/APILambda", # index is the file name  index="API_lambda.py", # handler is the function entry point name in the lambda.py file  handler="handler", runtime=_lambda.Runtime.PYTHON_3_9, # name of function on AWS  function_name="ExampleAPILambda", ) def build_gateway(self): # This will attach an API gateway as a trigger to our  # lambda function above. The return of the handler function also  # gets routed back to the API gateway.  self.gateway = apigw.LambdaRestApi( self, "Endpoint", handler=self.prediction_lambda )

Minimum Working Lambda Function

You can see here an example of the response format the API gateway is expecting.

# lambda_funcs/APILambda/API_lambda.py  from logging import getLogger logger = getLogger() logger.setLevel(level="DEBUG") def handler(event, context): logger.debug(msg=f"Initial event: {event}") response = { "isBase64Encoded": False, "statusCode": 200, "headers": { "Access-Control-Allow-Origin": "*", }, "body": f"Nice! You said {event['queryStringParameters']['q']}", } return response

Now you can do cdk deploy. The lambda function will be built using docker and uploaded to the bootstrapped ECR repository. Once the project is built, it will synth a CloudFormation template and begin deploying the infrastructure. You can watch your stack deploy on AWS CloudFormation - it should be quick since the infrastructure is relatively simple.

Once the process is completed, CDK will output the endpoint URL of the API Gateway: