Introduction

AWS Lambda is a popular serverless compute service that allows developers to run code without managing servers. A common challenge developers face when using AWS Lambda is incorporating third-party libraries, such as the requests module, which is widely used in Python for handling HTTP requests. In this article, we will discuss the reasons why using the requests module in AWS Lambda can be problematic, offer technical explanations, and present some alternatives.

The Problem with requests Module on AWS Lambda

When you deploy a function on AWS Lambda, the code should be as lightweight and efficient as possible. AWS Lambda has a few constraints that may impact how you use additional libraries like requests.

Reasons for Issues

1. Package Size Limitations: AWS Lambda has limits on the size of the zipped deployment package, which cannot exceed 50 MB. The requests module and its dependencies may take up considerable space when packaged, potentially pushing the overall deployment size over this limit, especially when combined with other dependencies.
2. Cold Starts: Larger deployment packages lead to longer cold start times. When a Lambda function hasn't been used for a while, the environment needs to be re-initialized. Including the requests module can result in slower cold starts as more code needs to be loaded and executed.
3. Python Environment: AWS Lambda's execution environment contains a minimal Python installation. Some Python features might not work as expected or might require you to package additional system libraries that are typically available in a fully-featured local Python environment.

Technological Constraints and Alternatives

Package requests with Your Lambda Function

To include the requests module, you can package it within your Lambda function. Here's how you can do it:

1. Create a Virtual Environment:

   python -m venv venv
   source venv/bin/activate

1. Install the requests Module:

   pip install requests

1. Package the Module:

1   cd venv/lib/python3.8/site-packages
2   zip -r9 ../../../../my-deployment-package.zip .
3   cd ../../../../
4   zip -g my-deployment-package.zip lambda_function.py

This can inflate your function's package size, which is why it's essential to consider alternatives if size is a concern.

Alternatives to requests

1. Use urllib3:
   • Python's standard library, which includes urllib3, is more lightweight than requests. It can handle HTTP requests adequately for many scenarios.
   • Key Syntax Example:

1     import urllib3
2
3     http = urllib3.PoolManager()
4     response = http.request('GET', 'https://example.com')
5     print(response.data.decode('utf-8'))

1. AWS SDK (boto3):
   • For AWS service interactions, the boto3 library is best as it is optimized and intended for AWS service communication.
   • Key Syntax Example:

1     import boto3
2
3     s3 = boto3.client('s3')
4     s3.list_buckets()

Optimizing Lambda Deployment

Consider adopting the following optimizations to ensure efficient use of AWS Lambda with any additional libraries:

• Layer Use: AWS Lambda Layers can store and manage additional code packages separately from your function code. This method helps keep deployment packages smaller, focusing resources only on when the layer gets updated.
• Environment Variables: Use environment variables for configuration settings so that the deployment package is not burdened with configuration files.
• Global Objects: Use global objects when applicable to avoid reloading libraries in subsequent invocations.

Key Points and Recommendations

Conclusion

In conclusion, while it is possible to use the requests module in AWS Lambda, it presents challenges related to package size, execution efficiency, and cold start latency. Developers are advised to consider lightweight alternatives like urllib3 or handle AWS services using boto3 for optimal performance. By leveraging AWS Lambda Features like Layers and optimized packaging approaches, one can achieve a more efficient serverless application.