configure-aws-credentials VS Klayers

The AWS configure-aws-credentials Github Action allows the connection to the AWS S3 bucket through an AWS Role. The configuration of this role is explained in the next chapter

In this example, we'll be using the aws-actions/configure-aws-credentials action with GitHub's OIDC provider. Make sure the configured role has the required permissions.

The first step is to set up GitHub Actions as a recognized identity provider in my AWS account. This is also called an "OIDC Trust" relationship. In AWS IAM, create an Identity Provider with GitHub's provider URL and Audience. I am using the open-source action configure-aws-credentials (link) which means I want to use an Audience value of sts.amazonaws.com. Be sure to click the "Get Thumbprint" button to save a copy of the x.509 certificate used by GitHub into the AWS identity provider.

You can use configure-aws-credentials Github aciton. Which is pretty good. Here is a blog post about it from AWS: https://aws.amazon.com/blogs/security/use-iam-roles-to-connect-github-actions-to-actions-in-aws/

We are now ready to utilize configure-aws-credentials within our GitHub Actions as we move onto deploying our code!

The AssumeRoleWithWebIdentity error manifests itself mostly around parallel access attempts, and how the various AWS interfaces are able to authenticate, as well as run and deploy services. We started encountering this issue when running our pipelines for deployment, and attempting to authenticate our Github account to AWS via the OIDC plugin. This is a well-known (and widely discussed) limitation for authentication to AWS for web application providers. In our case it was Github, but this is true for pretty much any web application integration.

What you are looking at is totally doable, you MUST use: https://github.com/aws-actions/configure-aws-credentials

AWS Actions: It's an open source project from AWS which the goal is to get easy to Configure AWS credential and region environment variables for use in other GitHub Actions.

In order to achieve this, AWS credentials need to be properly configured. Here we use a handy Github action called configure-aws-credential, from AWS itself. You can also read more about the many methods of authentication available. This step requires the AWS_REGION and AWS_ROLE_ARN secrets to be properly configured in the repo, both of which that should be shared by the platform team.

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