How to create AWS Lambda layer for Shapely 2.0 (Python3.9)?

2 min read 05-10-2024

How to create AWS Lambda layer for Shapely 2.0 (Python3.9)?

Empowering AWS Lambda with Shapely 2.0: A Layer Guide

The power of AWS Lambda lies in its ability to execute code on-demand, scaling automatically to meet your needs. However, using libraries like Shapely within Lambda can be tricky, especially when you're dealing with newer versions like Shapely 2.0.

This article will guide you through creating an AWS Lambda layer for Shapely 2.0, effectively enabling you to use its powerful geospatial capabilities within your Lambda functions.

The Challenge: Shapely 2.0 and Lambda Compatibility

Imagine you need to perform complex spatial analysis within your Lambda function, leveraging the features of Shapely 2.0. You encounter a hurdle: Shapely 2.0 is not natively included in the Lambda environment. Installing it directly within your Lambda function would lead to unnecessary code duplication and larger deployment packages.

Solution: Creating a Lambda Layer

The solution is to package Shapely 2.0 into an AWS Lambda layer, a mechanism that allows you to share libraries and dependencies across multiple functions. This way, you can reuse the layer, keeping your function code clean and concise.

Step-by-Step Guide to Creating a Shapely 2.0 Lambda Layer

Prerequisites:
- AWS Account
- AWS CLI installed
- Python 3.9 environment (ensure Shapely 2.0 is compatible)
Creating a Virtual Environment:
- Use virtualenv to create a dedicated environment for your layer:
```
virtualenv -p python3.9 shapely_layer
source shapely_layer/bin/activate
```
Installing Shapely 2.0:
- Install Shapely 2.0 within the virtual environment:
```
pip install shapely==2.0.0
```
- If you need other geospatial libraries, such as Fiona or GeoPandas, install them in the same environment.
Creating the Layer Package:
- Navigate to your virtual environment's lib directory:
```
cd shapely_layer/lib/python3.9/site-packages/
```
- Create a compressed archive containing the necessary files. For example, using tar (ensure you include all Shapely-related folders):
```
tar -czvf shapely_layer.zip *
```
Publishing the Layer:
- Use the AWS CLI to publish the created layer package:
```
aws lambda publish-layer-version --layer-name shapely-layer --description "Shapely 2.0 layer" --zip-file fileb://shapely_layer.zip --compatible-runtimes python3.9
```
- Replace shapely-layer with your preferred layer name.
- Note the LayerVersionArn returned in the output - this is your layer's unique identifier.
Using the Layer in Lambda Functions:
- When creating or updating your Lambda function, navigate to the "Layers" section and add your created layer.
- You can search for it by its name or use the LayerVersionArn obtained in the previous step.

Benefits of Using a Lambda Layer

Code Organization: Your function code remains clean and focused on its core logic, free from library installation details.
Reusability: The layer can be shared across multiple Lambda functions, minimizing redundancy.
Version Control: Easily manage and update the Shapely version within the layer, ensuring consistency across your functions.

Conclusion

Creating a Lambda layer for Shapely 2.0 empowers you to leverage its advanced geospatial features within your serverless architecture. By following this guide, you can streamline your development process, enhance code reusability, and take advantage of the power of Shapely within your AWS Lambda functions.

Additional Resources

AWS Lambda Layers Documentation: https://docs.aws.amazon.com/lambda/latest/dg/configuration-layers.html
Shapely Documentation: https://shapely.readthedocs.io/en/stable/
Virtualenv Documentation: https://virtualenv.pypa.io/en/stable/

Remember, always check for updates to Shapely and adapt the steps accordingly to ensure compatibility with the latest versions. Happy coding!