ssh-agent VS serverless-application-model

Once the image has been built and pushed, you only need to trigger the deployment using Kamal. We use the webfactory/ssh-agent to establish a connection to our production server. After installing the required Ruby dependencies, it’s only a matter of running Kamal. As the image is already built and pushed, we use the --skip-push flag.

When using Github Actions, to build projects I have been using the ssh-agent approach (https://github.com/webfactory/ssh-agent), with this means the Cargo.toml looks like this:

Thankfully when I reached out on Twitter, Jonathan Carter on the Codespaces team, seemed to suggest they may be working on a native SSH integration one day. Till then, here is the solution I came up with. This process address some sequencing issues around devcontainer.json's Lifecycle Scripts and when your Dotfiles are installed. Credit to VS Codes Using SSH Keys guide. Also, some things here are pulled directly from the GitHub Action to setup SSH. Again, thanks to Johnathan Carter for the ideas.

The complete solution with SAM is available here.

There are many options to deploy Serverless Applications in AWS and one of them is SAM, the Serverless Application Model. I chose to use it here, because it doesn't add too many layers of abstraction between what's being deployed and the code we write and our infrastructure is quite simple.

The solution uses AWS SAM with the global configuration for Lambda functions and the public API you can use to access Apache Zeppelin. The stack deployment provides the URL as an output value.

When you combine this with the AWS Serverless Application Model you can also very easily include your dependencies. Or use a compiled language like golang for your Lambda functions. You simply run sam build before you run the aws cloudformation package and aws cloudformation deploy commands. SAM will build the binary and update the template to point to the newly built binary. Package will then upload it to S3 and replace the local reference to the S3 location. Deploy can then create or update the stack or you can use the CloudFormation integration in CodePipeline.

I'm a big fan of using an Infrastructure as Code (IaC) approach for any project. My go to tools for this are the Servlerless Application Model (SAM) and it's associated CLI (SAM CLI). For more official use cases and for cross platform apps I typically use Terraform.

AWS SAM

Kicking off the tour and not starting a war, but I'm going to be using the Serverless Application Model.

The diagram here is super simple. I'm going to write something a little later that shows how this code could fit into a bigger workflow, but for now, I'm keeping it basic. And yes, that's the SAM Squirrel in there.

Interestingly, AWS CDK and SAM are both explicitly mentioned. While CDK broadly addresses Infrastructure as Code, SAM is highlighted for its role in developing serverless data pipelines - a hugely underrated concept.

Naturally, there are several options available to declare your cloud resources. The options with the most popularity are the CDK, AWS CloudFormation, SST, Serverless framework, Terraform, and AWS SAM. There are others, but when talking about Infrastructure as Code (IaC), these are the ones you hear about most often.