AWS Lambda Runtime Your Way How to Build One That Fits Your App Needs

Managing dependencies for multiple AWS Lambda functions can get messy, right? Instead of bundling the same libraries with every function, AWS Lambda Layers let you share common code across multiple functions. This keeps things clean, efficient, and easier to maintain.

Before setting up a Lambda Layer, ensure you have the following:

AWS CLI installed and configured – with the right IAM permissions to manage Lambda functions and layers.
An AWS Account – with permissions to create and manage Lambda functions.
A Programming Language Runtime – like Python, Node.js, or Java, depending on your project.
A requirements.txt file – listing all the dependencies your Lambda function needs.

Understanding What Are Lambda Layers?

The dependencies for your function can be stored in common storage using lambda layers. A single layer that contains all the necessary dependencies can be created rather than bundling the same libraries with each Lambda function. This layer can then be used by your functions to reduce package size and increase deployment efficiency.

Why Use Lambda Layers?

Code Reusability: Share common dependencies across multiple Lambda functions.
Smaller Deployment Packages: Reduces the size of individual Lambda function packages.
Easier Updates: Update dependencies without modifying individual Lambda functions.
Improved Performance: Reduces cold start times by caching dependencies separately.

1. Create a Lambda Function

To create a Lambda function from scratch:

				
					aws lambda create-function \
        --function-name LambdaFunction \
        --runtime python3.x \
        --role arn:aws:iam::account-id:role/LambdaRole \
        --handler lambda_function.lambda_handler \
        --timeout 15 \
        --memory-size 256 \
        --zip-file fileb://function.zip

Runtime: Specifies the language environment (Python 3.x in this case).
Role: IAM role with necessary permissions.
Handler: Entry point for Lambda execution.
Timeout: Maximum execution time (15 seconds).
Memory Size: Allocated memory (256 MB).

2. Prepare the Dependencies for a Lambda Layer

NOTE: The directory name must follow this path: python/lib/python{version}/site-packages.

(e.g. python/lib/python3.13/site-packages)

Create Directory Structure for the Layer:

				
					mkdir -p python/lib/python3.13/site-packages

Install Dependencies:

Install the dependencies specified in requirements.txt into the site-packages directory:

				
					pip install -r requirements.txt --target python/lib/python3.13/site-packages/

Add Config File:

Copy lambda_function.py into the python directory of the layer:

				
					cp lambda_function.py python/

Package the Layer:

Create a .zip file for the Lambda layer:

				
					zip -r python.zip python/

3. Create the Layer in the AWS Console

Login to AWS Management Console, navigate to the AWS Lambda service.
On the left hand side navigate to “Layers”.
Provide necessary details and Create layer.

Different Lambda Runtimes and Their Implications for Layers

AWS Lambda supports multiple runtimes, and each has specific requirements for layers:

Python

Folders must be named python/lib/python3.x/site-packages.
Works with .py files, .whl packages, or .so libraries.

Node.js

The layer should contain nodejs/node_modules.
Uses package.json to manage dependencies.

Java

Layer must contain JAR files inside java/lib/.
Java layers require careful memory and cold-start optimization.

Go

Uses precompiled binaries.
Layer files should be stored inside go/bin.

Best Practices for Using Lambda Layers

Minimize Layer Size: Remove unnecessary dependencies.
Use Versioning: Always version your layers for backward compatibility.
Monitor Performance: Test cold start impact with different configurations.
Security Considerations: Ensure no sensitive data is included in layers.

Conclusion:

AWS Lambda Layers provide a robust mechanism to manage dependencies efficiently across multiple functions. By properly structuring layers and understanding runtime-specific configurations, you can optimize your Lambda functions for performance and maintainability.