Using Python Lambdas Lab

This lab introduces how to build serverless applications using Python and AWS Lambda.

You will deploy a Lambda function, connect it to Amazon DynamoDB, extend it with additional functionality, and write unit tests using pytest.

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Learning Objectives

By the end of this lab, you will be able to:

• Understand how Python code runs inside AWS Lambda
• Interact with AWS services using boto3
• Structure Lambda functions using clean, testable Python patterns
• Extend a Lambda function to support CRUD operations
• Write unit tests using pytest

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⚠️ Important Notes (Real-World vs Lab)

This lab simplifies some patterns for learning purposes:

Lab Approach	Production Best Practice
One Lambda for all CRUD operations	One Lambda per endpoint/action
Broad IAM permissions	Least-privilege IAM policies
Resources created via AWS Console	Infrastructure as Code (CloudFormation, Terraform, AWS CDK)
Manual .zip upload	CI/CD pipelines

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Prerequisites

Before starting, ensure your local environment meets the following requirements:

• Python: version 3.13 or higher
• Git: Git for Windows
• IDE: PyCharm or VS Code is recommended
• AWS Account: Provided here for the duration of the lab

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Getting Started

Step 1: Clone the Repository

Clone this repository to your local machine:

git clone <repository-url>
cd <repository-directory>

Step 2: Set Up a Virtual Environment

Open the project in your IDE and familiarise yourself with the Lambda handler logic and how it uses the boto3 library to communicate with AWS services.

Then set up your virtual environment:

1. Create the virtual environment:

   python -m venv venv

2. Activate the environment:

   • Linux/macOS:

     source venv/bin/activate

   • Windows:

     .\venv\Scripts\activate

     If you encounter a permission issue when running the above command in PowerShell, run the following command to allow scripts to be executed and try again.

     Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

3. Install dependencies:

   pip install -r requirements.txt

Step 3: Create a DynamoDB Table

1. Log in to the AWS Management Console and navigate to DynamoDB.
2. In the sidebar, click Tables, then click Create table.
3. Configure the following settings:
   • Table name: YourNameTable (e.g., JohnDoeTable)
   • Partition key: ItemID (Type: String)
4. Keep all other settings as default and click Create table.

Step 4: Create an Item in the DynamoDB Table

1. Log in to the AWS Management Console and navigate to DynamoDB.
2. In the sidebar, click Tables, then select your newly created table.
3. Click Actions and then Create item.
4. Provide ItemID a value of 1.
5. Click Add new attribute, then String
6. Set Attribute name as Name and provide it a value (e.g Chicken Burger)
7. Click Create item.

Step 5: Create the Lambda Function

1. Navigate to the Lambda service in the AWS Console.
2. Click Create function and select Author from scratch.
3. Configure the following:
   • Function name: your_name_function (e.g., john_doe_lambda)
   • Runtime: Python 3.13
4. Click Create function.
5. Click the Code tab, Scroll down to the Runtime settings section and Click Edit
6. Change Handler from lambda_function.lambda_handler to lambda_handler.lambda_handler. Click Save.

Step 6: Set Environment Variables

Your Lambda uses an environment variable to determine which DynamoDB table to use.

1. In your Lambda function, go to Configuration → Environment variables.

2. Click Edit → Add environment variable.

3. Add the following:

   Key	Value
   TABLE_NAME	YourNameTable

Step 7: Grant IAM Permissions

To allow the Lambda to interact with DynamoDB:

1. In your Lambda function dashboard, go to the Configuration tab and select Permissions.
2. Click on the Role name to open the IAM Console.
3. Click Add permissions → Attach policies.
4. Search for and select AmazonDynamoDBFullAccess, then click Add permissions.

Step 8: Deploy the Code

1. Navigate back to the Code tab of your Lambda function.
2. Create a .zip file containing your Python files.
3. Click the Upload from dropdown and select .zip file.
4. Upload your file and click Save.

Step 9: Test the Function

1. Go to the Test tab.
2. Create a new test event.
3. Click Test.
4. Confirm a successful expected response.

Core Task: Extending the Function

You are provided with a working "get all" endpoint.

Your task: implement full CRUD functionality.

Operation	Description
Create	Add a new item
Read	Get a single item by ID
Update	Modify an existing item
Delete	Remove an item

Suggested Approach

Add new methods to food_service.py:

def create_item(data): ...
def get_item(item_id): ...
def update_item(item_id, data): ...
def delete_item(item_id): ...

Route logic in the handler using the event action:

action = event.get("action")

Then when testing the lambda function, provide the appropriate Event JSON like so:

{
  "action": "delete"
}

Best practice: In production, each operation would typically be a separate Lambda mapped to its own API endpoint.

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Unit Testing with pytest

📖 Docs: https://docs.pytest.org/

Run the test suite:

pytest

Key principles:

• Test your Python functions, not the Lambda infrastructure itself
• Keep business logic separate from AWS dependencies
• Mock external services (e.g., DynamoDB) where needed

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Stretch Goal (Optional): Connect Lambda to API Gateway

1. Create an HTTP API in API Gateway.

2. Connect it to your Lambda function.

3. Deploy and test the following endpoints:

   GET  /items
   POST /items
   

Best practice: In production, each route maps to a separate Lambda, and API configuration is defined using Infrastructure as Code.

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Cleanup

Important

To avoid unnecessary AWS costs, delete the AWS resources you created (DynamoDB Table and Lambda Function) once you have completed the lab.