Demystifying AWS Lambda: A Guide for Python Developers

Are you a Python developer looking to harness the power of serverless computing? AWS Lambda functions offer a scalable and cost-effective solution to execute code without managing servers.

In this article you will be able to:

• Understand what is a AWS Lambda Function
• Write and deploy a Fast API REST endpoint that triggers your lambda
• Write a lambda_handler that inserts data on DynamoDB
• How to deploy your code through the Serverless Application Model (SAM)

Requirements: Basic knowledge of python and Fast API or other web framework

Let's dive into it and revolutionize your application architecture!

Part 1: Understanding AWS Lambda Functions

AWS Lambda functions are bite-sized pieces of code that can be executed on the cloud in response to specific events. These events could be changes to data in an Amazon S3 bucket, updates to a DynamoDB table, or HTTP requests via Amazon API Gateway (for this exampel we'll be using HTTP requests). The beauty of Lambda lies in its simplicity; you only pay for the compute time your function consumes.

One of the key advantages of Lambda functions is their scalability. As your application receives more traffic, AWS automatically scales the number of concurrent function executions to handle the load. This means you can focus on writing code and leave the infrastructure management to AWS.

Part 2: Writing local python's Fast API code

Now, let's write the FastAPI code to create a POST API route that triggers our Lambda function via HTTP requests using Amazon API Gateway. Assuming you have some basic knowledge of FastAPI, follow these steps:

• Create the main.py file in the root directory:

• Finally create the lambda_handler.py containing the lambda_handler() function, they are all name sensitive:

You can see the lambda_handler() mandatory function highlighted by a red arrow above. This is the most important piece of code

What many developers struggle with when learning Lambdas:

The communication between the API route and the lambda functions might look confusing to developers because there's no explicit communication between them, here's what: The lambda_handler function will be executed MAGICALLY once the POST ROUTE is called, no need to do any links or communication between the main.py file and the lambda_handler.py.

Part 3: Uploading your code

In order to update our REST API along with the Lambda function:

• Create the template.py file in the root directory with the main.py and the lambda_handler.py (The goal of this template is to provide infra Resources)

• Install the AWS SAM CLI if you haven't already. You can download it from the official AWS SAM CLI WEBSITE.
• Execute the package command using the recently installed AWS SAM CLI. keep in mind it's necessary to execute this command in the root directory where your files are located.

sam package --s3-bucket <YOUR_S3_BUCKET> --output-template-file packaged.yaml

• Replace <YOUR_S3_BUCKET> with the name of your S3 bucket, where AWS SAM CLI will store the deployment artifacts. You can also create one
• Finally, run the deploy command to deploy your application:

sam deploy --template-file packaged.yaml --stack-name MyLambdaStack --capabilities CAPABILITY_IAM

Part 4: Activating triggers:

• Open the AWS Management Console and navigate to the Lambda page
• Click on the "+ Add trigger" button to add a new trigger to your recently created lambda function:

• Select "API Gateway" as the trigger configuration.
• Mark the option "Use existing API" and choose the API ID of the most recent API (usually the one on top). This API was created when you deployed the REST API along with the Lambda using the SAM deploy command.

This API was created on step 3 when we deployed the REST along with the lambda using the SAM deploy command

• Save the trigger configuration by clicking on the "Add" button.
• Now, to test your Lambda function, go back to the Lambda page, click on the "Configuration" tab, and check the trigger endpoint URL. This URL is the API endpoint that can be used to trigger your Lambda function through HTTP requests.

By following these simple steps, you have successfully activated triggers for your Lambda function using API Gateway. Now your Lambda function can be invoked by making HTTP requests to the API endpoint, making your serverless application fully functional and ready to handle requests in the cloud.

Your working directory might look like this once you finish it:

Congratulations on deploying your first AWS Lambda function triggered by an API REST POST request in a serverless environment!!!

By following the example code provided, you've learned how to seamlessly transfer data between your Lambda function and DynamoDB, eliminating the need to manage servers in the cloud. Embracing Lambda's single responsibility principle empowers you to wield the true power of serverless computing.

With AWS Lambda, you can focus on writing code and building functionalities without worrying about infrastructure management. This serverless approach streamlines your development process, optimizing resource utilization and cost efficiency.

As you continue your serverless journey, explore other AWS services that complement Lambda, such as Amazon API Gateway, AWS Step Functions, and AWS EventBridge. These services enhance your serverless applications and provide a robust ecosystem for modern, scalable solutions.

Welcome to the world of serverless computing—where innovation and efficiency converge. Happy coding!

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