Klayers VS python-lambdarest

Use public layers maintained by others. For example Klayers which are produced with CI in GitHub Actions. This way you don't have any layers lying around in your Lambda UI. You can point to new layer versions whenever you wish. You can interact with the layer data via an API : https://github.com/keithrozario/Klayers/#api

If I want to do something more data heavy or complex, I'll sometimes instead go for Python. Having a mixture of languages is one of the big advantages of micro services. Packaging dependencies is a bit tricker for Python, for simple packages I'll usually include in my build script something to copy the relevant directories from my venv's site-packages. However one neat alternative is to use a publicly available precompiled lambda layer from Klayers. These slot neatly in one line of code into my terraform config and enable me to use popular Python packages like numpy and pandas without troubling myself about C extensions, build environment or having to mess with Amazon Linux.

Before anything, Huge shoutout to Keith Rozario for providing us with this awesome Repo. where he defined layers for lambda functions to add libraries that doesn't exist by default in the lambda runtime without you having to create them yourself. I used his repo to add the requests, pandas and psycopg2 libraries to the project.

I like to use this guy’s layers as an arn: https://github.com/keithrozario/Klayers

using redbubble seems to be the least painful setup (as they handle everything like payment shipping inventory etc)

so i went and setup a design there for my github repo (see commit: https://github.com/sloev/python-lambdarest/pull/79)