1. Introduction to AWS Lambda

What is AWS Lambda?

AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS). It allows you to run code without provisioning or managing servers. You just upload your code, and Lambda takes care of the rest—handling the server infrastructure, scaling, and execution.

Example: Imagine you want to run a Python script that processes images whenever a new image is uploaded to an S3 bucket. With AWS Lambda, you can write this script, upload it, and configure Lambda to run your script automatically every time a new image is uploaded. You don’t need to worry about maintaining servers or managing the execution environment; Lambda handles all of that.

Benefits of Serverless Computing

Serverless Computing means you don’t have to manage the infrastructure that runs your code. Here are some key benefits:

• Cost-Efficiency: You only pay for the compute time you consume. If your code runs for just a few seconds, you’re only billed for those seconds.
• Automatic Scaling: AWS Lambda automatically scales your application by running code in response to each event. No manual intervention is needed.
• No Server Management: There’s no need to manage servers or worry about the underlying infrastructure. You focus solely on writing code.

Example: Let’s say you have an application that sends notifications to users based on their actions. Using AWS Lambda, you can set up your code to trigger notifications automatically without managing the servers that process these notifications. You only pay for the time your code is running and not for idle server time.

Overview of AWS Lambda Architecture

AWS Lambda Architecture consists of several key components:

• Functions: The core of AWS Lambda. A function is a piece of code that you want to execute. It’s triggered by an event.
• Events: Triggers that cause your function to run. These can be changes in data (like an S3 file upload) or HTTP requests (via API Gateway).
• Execution Role: AWS Lambda uses IAM (Identity and Access Management) roles to grant permissions to your function. This role defines what AWS services your function can interact with.
• Layers: AWS Lambda Layers are a distribution mechanism for libraries, custom runtimes, and other dependencies. Layers let you manage your function dependencies separately from the function code itself.
• VPC Integration: Lambda functions can connect to your Virtual Private Cloud (VPC) to access resources in a private network.

Example: Suppose you run an e-commerce website and you want to resize product images automatically when they are uploaded.

• Create a Lambda Function: Write a function in Python that resizes images. Upload this function to AWS Lambda.
• Set Up an Event Source: Configure AWS Lambda to trigger the function when a new image is uploaded to your S3 bucket.
• Define IAM Role: Create an IAM role that allows your Lambda function to read from S3 and write resized images back to the bucket.
• Test and Monitor: Test your Lambda function by uploading an image to S3 and check if the image is resized correctly. Monitor the function’s performance and execution logs using CloudWatch.

Examples of Each Component:

• Functions: Suppose you have a Lambda function that sends a welcome email to new users. This function is triggered whenever a new user signs up.
• Events: If you have a Lambda function that processes data from a DynamoDB table, the event is the change (insert, update, delete) in that table.
• Execution Role: For a Lambda function that reads from an S3 bucket and writes to a DynamoDB table, the execution role will have permissions to access both S3 and DynamoDB.
• Layers: If your Lambda function needs additional libraries, like `requests` for making HTTP requests, you can create a Lambda Layer with this library and use it in your function.
• VPC Integration: If your Lambda function needs to access a database inside your VPC, you configure the function to connect to that VPC, allowing it to securely communicate with the database.