How Can I Test Lambda in Local Using Python?


Testing AWS Lambda functions locally can save time and simplify the development process. This is especially beneficial before deploying the code to the AWS environment, allowing for rapid iterations and testing. In this guide, we’ll explore how you can set up your local environment to test Lambda functions using Python, with detailed examples to help you get started.

Concepts Used

AWS Lambda

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers.

Local Testing

Local testing refers to running and testing applications on a developer’s local machine instead of a live production or staging environment.

AWS SAM (Serverless Application Model)

AWS SAM is an open-source framework for building serverless applications. It includes a local environment that can mimic AWS Lambda.

Step-by-Step Guide: Testing Lambda Functions Locally with Python

Step 1: Install AWS SAM CLI

The AWS SAM Command Line Interface (CLI) is essential for local testing. Install it using:

Step 2: Create a New SAM Application (Optional)

If you don’t have an existing Lambda function, create a new SAM application:

Step 3: Navigate to Your Project Folder

Move to your project directory where the Lambda function code is located:

Step 4: Start Local Lambda Environment

Use the SAM CLI to start a local Lambda environment:

Step 5: Invoke the Lambda Function Locally

Invoke your Lambda function using:

You can also pass event JSON files for testing different input scenarios:

Step 6: Debugging (Optional)

You can use debugging tools like pdb in Python by adding breakpoints in your code:

Step 7: Review Results

After invoking the function, you will see the output and logs in your terminal, allowing you to assess the function’s performance and behavior.


Local testing of AWS Lambda functions using Python provides a quick and efficient way to validate functionality before deployment. By leveraging tools like AWS SAM CLI, you can create a local development environment that closely simulates the actual AWS environment. This practice enhances the development workflow, allowing for rapid iterations and more robust code by the time you are ready to deploy to production.