troposphere VS kapp

Seems like an interesting generalized mix of something like https://github.com/cloudtools/troposphere and Ansible from a glance.

The value add would be unifying provisioning and configuration management in a Python-y experience? The lifecycle of each is distinct and that's traditionally where the headaches of using a single tool for both has come in

Under the IaC category, in July 2023, AWS added loops to CloudFormation, finally ticking a box the community has been asking for since troposphere. I suspect that, in combination with the Terraform licensing changes, it may keep people using CloudFormation for a while longer.

Because ECS Compose-X uses Troposphere, I was able to create a very light and simple python library(https://github.com/JohnPreston/troposphere-awscommunity-applicationautoscaling-scheduledaction) to distribute the resource for other Troposphere users to re-use.

Written in house, but the library troposphere is the primary component of how it is built. Example stacks are here.

I am kind of going off topic here, but isn't the point of being an SA to be created with code services to deliver solutions at scale that are cost-effective? How in the hell can you do that when you can't write a simple Python template that generates code at 50 times the rate you can manually? How can you ever be expected to deploy a serverless solution if you can't write any code yourself? There has to be some level of proficiency there.

Wouldn't using troposphere[1] be easier?

[1] https://github.com/cloudtools/troposphere

You might have seen some tutorials on how to set up S3 buckets using the AWS Console. This works fine, but I'm a firm believer of managing your resources with code. I've chosen the native solution of AWS, called AWS CloudFormation. This makes it easier to reproduce the setup if I ever need to tear it down of move it to another account or region. Below is the full CloudFormation template, I've used a framework called Troposphere, a Python library that creates CloudFormation.

For CF for example I no longer write template in yaml or shudders json, and instead I use troposphere.

refer : https://github.com/cloudtools/troposphere

If you want to take it one step further: you might be applying several resources at a time that are logically one "application". kapp (https://carvel.dev/kapp/) lets you group those together and give them a name, and provides a "terraform-like" experience where it shows you its execution plan before applying updates. So then you might do `ytt -f | kapp deploy -a name-of-thing` Or you could use helm's templating engine but then still pass the resulting yaml to kapp for its unification of the deployment step.

since you mentioned Kubernetes...

> It would be nice if there was a separate state reconciliation system that one could adapt to use with Cue or Dhall or any other frontend

this exactly was thinking behind https://carvel.dev/kapp for Kubernetes (i'm one of the maintainers). it makes a point to not know how you decided to generate your Kubernetes config -- just takes it as input.

> In particular the ability to import other files as semantic hashes seems like a great feature.

it's an interesting feature but seems like it should be unnecessary given that config can be easily checked into git (your own and its dependencies).

i think kubernetes is not a great example in favor of more client state (like tf) since k8s has uniform resource structure (metadata.*) and first class labeling support. but as you point out kubectl doesnt use labels well (at least imho).

when building https://carvel.dev/kapp (which i think of as "optimized terraform" for k8s) the goal was absolutely to take advantage of those k8s features. we ended up providing two capabilities: direct label (more advanced) and "app name" (more user friendly). from impl standpoint, difference is how much state is maintained.

"kapp deploy -a label:x=y -f ..." allows user to specify label that is applied to all deployed resources and is also used for querying k8s to determine whats out there under given label. invocation is completely stateless since burden of keeping/providing state (in this case the label x=y) is shifted to the user. downside of course is that all apis within k8s need to be iterated over. (side note, fun features like "kapp delete -a label:!x" are free thanks to k8s querying).

"kapp deploy -a my-app -f ..." gives user ability to associate name with uniquely auto-generated label. this case is more stateful than previous but again only label needs to be saved (we use ConfigMap to store that label). if this state is lost, one has to only recover generated label.

imho k8s api structure enables focused tools like kapp to be much much simpler than more generic tool like terraform. as much as i'd like for terraform to keep less state, i totally appreciate its needs to support lowest common denominator feature set.

common discussion topics:

Take a look at kapp (https://github.com/vmware-tanzu/carvel-kapp).

kapp - Install, upgrade, and delete multiple Kubernetes resources as one "application"

I really like this approach for simplifying Kubernetes. A few projects similar to OAM in that it provides a higher level "Application" CRD:

https://github.com/vmware-tanzu/carvel-kapp