AWS Lambda Layers allow developers to share common code and dependencies across multiple Lambda functions, simplifying management and reducing deployment package size. This article provides a step-by-step example of creating and using an AWS Lambda Layer, focusing on adding a shared library to multiple Lambda functions.

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What Are AWS Lambda Layers?

AWS Lambda Layers are an architectural feature that helps:

• Reuse Code: Share libraries and dependencies across functions.
• Streamline Maintenance: Update shared code in one place.
• Reduce Deployment Package Size: Keep the function code lean by separating dependencies.

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AWS Lambda Layers Example

Objective

We’ll create a Lambda Layer containing a Python library (requests) and use it in multiple Lambda functions.

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Step 1: Prepare the Lambda Layer

1. Create the Layer Directory

On your local machine, create the directory structure:

mkdir lambda-layer
cd lambda-layer
mkdir -p python/lib/python3.9/site-packages

Replace python3.9 with the appropriate version for your Lambda runtime.

2. Install Dependencies

Install the required library into the directory:

pip install requests -t python/lib/python3.9/site-packages

This will download the requests library and its dependencies into the specified folder.

3. Package the Layer

Zip the layer files:

zip -r layer.zip python

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Step 2: Create the Lambda Layer in AWS

1. Navigate to the AWS Lambda Console:
   • Go to Layers → Create Layer.
2. Upload the Layer:
   • Name: Enter a name (e.g., requests-layer).
   • Description: Optionally, provide a description.
   • Upload: Choose the layer.zip file.
   • Compatible Runtimes: Select Python 3.9 (or the runtime you used).
3. Create the Layer:
   • Click Create to finalize the layer.

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Step 3: Use the Layer in a Lambda Function

1. Create a Lambda Function

1. In the AWS Lambda Console, create a new function:
   • Runtime: Select Python 3.9.
   • Function Name: Enter a name (e.g., use-layer-function).
2. Attach the Layer:
   • In the function’s configuration, go to Layers → Add a layer.
   • Choose Custom Layers → Select the requests-layer you created.
   • Click Add.

2. Write the Function Code

Update the function code to use the requests library from the layer:

import json
import requests

def lambda_handler(event, context):
    url = "https://api.github.com"
    response = requests.get(url)
    
    return {
        'statusCode': 200,
        'body': json.dumps({
            'message': 'Layer test successful!',
            'data': response.json()
        })
    }

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Step 4: Test the Function

1. Deploy and Test:
   • Deploy the function and test it with any input.
2. Check the Logs:
   • View the logs in CloudWatch to ensure the requests library was successfully loaded and used.

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Benefits of Using Lambda Layers

1. Code Reusability: Share common code across multiple Lambda functions.
2. Reduced Maintenance: Update shared libraries in one layer rather than across functions.
3. Optimized Deployment: Smaller function packages lead to faster deployment and execution.

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Best Practices for AWS Lambda Layers

1. Version Control:
   • Manage versions of your layer carefully, as updates are not automatically propagated.
2. Optimize Layer Size:
   • Keep layers small by including only the necessary dependencies.
3. Use Compatible Runtimes:
   • Ensure the layer runtime matches the Lambda function runtime.
4. Monitor Usage:
   • Regularly check which functions use each layer to manage dependencies efficiently.

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Conclusion

AWS Lambda Layers simplify code management by enabling the reuse of shared libraries and dependencies across functions. This example demonstrated how to create a Python-based Lambda Layer, attach it to a function, and use it effectively. By leveraging Lambda Layers, you can optimize function deployment, improve maintainability, and enhance development productivity.