advanced patterns with AWS Lambda Layer dependencies in Python runtime
I need some guidance on I just started working with I'm a bit lost with I tried several approaches but none seem to work... I'm working with an scenario with AWS Lambda where I included a custom layer for my Python function, but some dependencies are not being recognized. I'm using Python 3.8, and I created a layer with several packages, including `requests` and `pandas`. I packaged the layer correctly (zip file with the structure `python/lib/python3.8/site-packages/`), but when I deploy the Lambda function and invoke it, I get an import behavior for `pandas` stating: `ModuleNotFoundError: No module named 'pandas'`. I've verified that the layer is attached to the function and the ARN is correct. Here’s the relevant part of my Lambda function code: ```python import json import requests import pandas as pd def lambda_handler(event, context): # Trying to create a DataFrame data = {'Column1': [1, 2, 3], 'Column2': [4, 5, 6]} df = pd.DataFrame(data) return { 'statusCode': 200, 'body': json.dumps(df.to_dict()) } ``` To troubleshoot, I tried: 1. Rebuilding the layer and ensuring the directory structure is correct. 2. Testing the Lambda function without the layer to confirm `pandas` works in a local environment. 3. Checking that the layer does not exceed the size limit (it’s about 30 MB currently). 4. Reviewing the Lambda execution role permissions to ensure it’s correctly set up. Despite these attempts, the scenario continues. I also checked the AWS documentation on Lambda layers, but couldn't find any specific mention of limitations regarding packaged dependencies in the Python runtime. Is there something I might be overlooking, or any best practices I should follow for packaging dependencies in Lambda Layers? Is there a better approach? Could someone point me to the right documentation? I've been using Python for about a year now. Cheers for any assistance! For context: I'm using Python on Ubuntu 22.04. Any pointers in the right direction?