Using AWS Lambda Layers to Share and Manage Code Efficiently

AWS Lambda has transformed serverless computing by allowing developers to execute code without the need to provision or manage servers. While Lambda simplifies deployment, managing dependencies and sharing code across multiple functions can become a challenge. This is where AWS Lambda Layers step in, offering a powerful way to share and manage code, libraries, and dependencies efficiently.

In this blog, we will explore the concept of Lambda Layers, their benefits, and how to use them to optimise your serverless applications.

What are AWS Lambda Layers?

Lambda Layers are a feature in AWS Lambda that enables you to package and share shared code and dependencies across multiple Lambda functions. Instead of duplicating libraries or code snippets in every function, you can create a layer and attach it to any Lambda function that requires it.

A Lambda Layer can include:

• Shared libraries or dependencies (e.g., Python packages, Node.js modules).
• Custom runtime environments.
• Configuration files or helper scripts.

Each Lambda function can use up to five layers, which are merged into the function’s runtime environment during execution.

Benefits of using Lambda Layers

1. Code Reusability
   
   • Avoid duplication by storing shared code and dependencies in a layer.
   • Update the layer once, and all attached functions benefit immediately.
2. Simplified Maintenance
   
   • Manage dependencies centrally rather than updating each Lambda function individually.
3. Reduced Deployment Package Size
   
   • By offloading dependencies to a layer, your deployment package becomes smaller, leading to faster deployments and easier version control.
4. Faster Iteration
   
   • Focus on writing function-specific logic without worrying about repeating common code.
5. Cost Efficiency
   
   • Minimize execution duration by optimizing function packages, indirectly reducing AWS costs.

How to Create and Use Lambda Layers?

Step 1: Prepare the Layer Content

Package the libraries, dependencies, or code you want to include in a specific folder structure. For example:

For Python:

python/

   └── lib/

        └── python3.9/

             └── site-packages/

                  └── <dependencies>

For Node.js:

nodejs/

   └── node_modules/

        └── <dependencies>

Step 2: Package the Layer

Compress the folder into a ZIP file. Example for Python:

zip -r my-layer.zip python/

Step 3: Set up a layer in the AWS Management Console.

Go to the AWS Lambda Console.

1. Navigate to Layers in the left menu and click Create layer.
2. Enter a name, upload ZIP file, and choose a compatible runtime(s).
3. Save the layer.

Step 4: Associate the layer with Lambda function

1. Open the Lambda function in the AWS Console.
2. Scroll down to the Layers section and hit Add a layer.
3. Select the newly created layer from the list.
4. Deploy your changes.

Step 5: Access the Layer Content in Your Function

Access the shared code or libraries in your function as if they were part of your deployment package.

Example for Python:

import shared_library

shared_library.some_function()

Use Cases for Lambda Layers

1. Shared Utility Functions
   
   • Store commonly used helper scripts (e.g., logging, authentication).
2. Third-Party Libraries
   
   • Package large libraries (e.g., Pandas, NumPy) to avoid including them in each function’s deployment package.
3. Custom Runtime Environments
   
   • Use layers to create custom runtimes or extend existing ones.
4. Configuration Management
   
   • Include configuration files or environment-specific settings.
5. AI/ML Models
   
   • Package pre-trained machine learning models for use across multiple functions.

Best Practices for Using Lambda Layers

1. Version Control
   
   • Use versioning to track changes in your layers and roll back if necessary.
   • Ensure functions specify the appropriate layer version for stability.
2. Optimise Size
   
   • Keep layers lightweight by including only the essential dependencies.
3. Runtime Compatibility
   
   • Make sure the layer’s runtime matches the runtime of your Lambda function.
4. Access Control
   
   • Use AWS Identity and Access Management (IAM) to restrict layer access to specific accounts or roles.
5. Automate Layer Deployment
   
   • Integrate layer creation and updates into your CI/CD pipeline using tools like AWS CLI or AWS SDKs.

Challenges and Limitations

While Lambda Layers offer many advantages, they come with certain limitations:

• Size Limit: Each layer has a size limit of 50 MB (compressed).
• Runtime Dependency: Layers must be compatible with the runtime of the Lambda function.
• Limited Layers: A maximum of five layers can be attached to a single function.

Conclusion

AWS Lambda Layers offer an efficient solution for sharing and managing code across serverless functions. By centralising dependencies and leveraging reusability, developers can reduce maintenance overhead, streamline deployments, and improve application performance. Using Lambda Layers promotes more modular, maintainable, and scalable serverless architectures. Embrace this cloud technology feature to take your AWS Lambda workflows to the next level.