How to Load Dependencies from EFS into AWS Lambda Functions

Discover how to effectively manage dependencies for AWS Lambda using EFS. This guide details the steps to import dependencies into your Lambda code with practical advice on using Docker for optimal performance.
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Loading Dependencies from EFS into AWS Lambda Functions

When developing applications using AWS Lambda, you may encounter a limitation with the deployment package size. AWS Lambda has a maximum deployment size limit of 250 MB, which can be quite restrictive, especially if your project relies on many dependencies. Fortunately, Amazon Elastic File System (EFS) provides a solution, allowing you to host these dependencies externally.

In this guide, we will answer the question: How do you import dependencies from EFS into your Lambda function code? We will also explore the recommended method of using Docker to manage your dependencies efficiently.

Understanding EFS and Lambda Integration

Before we dive into the solution, let’s clarify the context:

Amazon EFS is a scalable file storage service that can be accessed from different AWS resources, including Lambda functions.

AWS Lambda allows you to run code in response to events and automatically manage your compute resources.

The integration of EFS with Lambda is particularly useful for handling larger deployments and sharing common code across several functions.

The Challenge: Importing Dependencies

Once you have set up your EFS instance and connected it to your Lambda function, the next hurdle is importing the dependencies from EFS into your Lambda code. A common question arises: Do you use require() with an absolute path to the module?

While it is technically possible to use absolute paths in your require() statements, there is a more efficient way to manage your dependencies, especially when aiming for flexibility and ease of use.

Recommended Solution: Using Docker

As mentioned in the comments by Ervin, a robust approach to managing dependencies for AWS Lambda using EFS is to utilize Docker containers. Here are the steps to implement this solution:

Step 1: Set Up Your Docker Environment

Install Docker: Make sure you have Docker installed on your local machine. Docker allows you to create a consistent environment that mimics AWS Lambda's execution context.

Create a Dockerfile: In your project directory, create a file named Dockerfile. This file will define the environment in which your Lambda function will run, including all the dependencies your application needs.

Step 2: Crafting the Dockerfile

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Building the Docker Image

Run the following command to build your Docker image:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Testing Your Function Locally

Before deploying, you can test your Lambda function locally using Docker:

[[See Video to Reveal this Text or Code Snippet]]

Step 5: Deploying the Function

Publish to EFS: Once you’ve confirmed your application works locally, make sure all dependencies you require are accessible within your EFS directory.

Deploy Lambda Function: Use the AWS CLI or AWS Management Console to deploy your Lambda function. Ensure that your function’s execution role has the right permissions to access the EFS.

Final Thoughts

By leveraging Docker for managing your dependencies within AWS Lambda, you can efficiently utilize Amazon EFS without the limitations of Lambda's packaging size restrictions. Additionally, using Docker helps maintain an environment that mirrors your production setting, reducing the "works on my machine" syndrome.

If you ever find yourself needing to manage dependencies that exceed AWS Lambda's limits, remember that combining EFS with Docker is a powerful solution that will simplify your workflows and enhance your project’s scalability.