magic-modules VS cue

Given: https://registry.terraform.io/providers/hashicorp/google/5.3...

how would any reasonable person know what https://registry.terraform.io/providers/hashicorp/google/5.3... to enable without (a) trying it and squinting at the error message (b) clicking on the <> then realizing it, also, does not mention run.googleapis.com, click on "supported service endpoints" <https://cloud.google.com/run/docs/reference/rest#rest_endpoi...> and only then learning about https://cloud.google.com/run/docs/reference/rest#service:-ru...

Repeat for https://registry.terraform.io/providers/hashicorp/google/5.3... although in both cases I guess the astute reader may have spotted the run.googleapis.com in the forbidden service labels and cloudidentity.googleapis.com in the example

Since, to the best of my knowledge those bindings are auto generated <https://github.com/GoogleCloudPlatform/magic-modules#magic-m...>, I would hypothesize it is not insurmountable drop in the seemingly existing declaration of APIs required: https://github.com/GoogleCloudPlatform/magic-modules/blob/7d... https://github.com/GoogleCloudPlatform/magic-modules/blob/7d...

In the case of the Google Cloud Platform provider, folks at Google built magic modules with the explicit goal of being able to generate schemas and behaviors for a Terraform provider and for other systems with similar needs. Since the vendor was explicitly aiming to support Terraform, this was the most ideal case where the schema could be designed to contain all of the information needed to generate a functional, usable provider.

I think the "Developing the provider" instructions in this provider's repository are rather stale, because they still talk about GOPATH even though that's been obsolete for several Go versions now. Note also that much of the code in that repository is auto-generated from an upstream repository googleCloudPlatform/magic-modules, and so for some changes it may be better to contribute there once you've tested the modifications more directly inside the provider repository.

For GCP, both ansible modules and terraform modules are actually generated from https://github.com/GoogleCloudPlatform/magic-modules, so their "production readiness" are the same.

I understand that mitchellh himself personally created a bunch of cloud modules for terraform at the beginning, and those were likely of higher quality than whatever created by some internal developers assigned by Google/Microsoft, and might be slightly better than the AWS modules maintained by community.

Anyway, when it comes to ansible versus terraform, we shall move the discourse to states management instead. With ansible, you don't have to deal with states, but will need to clean up the cloud resources separately. With terraform, you can use the tool to clean up the cloud resources easily, but then you also have the headache of managing states. Plus, whenever you change something, there is always the nagging feeling that it will do a destroy/recreate instead of an in-place update.

The Terraform provider for Google Cloud uses partial autogeneration, here is the repo that does the autogeneration for multiple automation tools:

https://github.com/GoogleCloudPlatform/magic-modules

If you are in a situation where you have a backend and you want to expose an API and then you would eventually want a client, you would need format specs as the starting point where server and clients are generated from that one source.

At the moment, OpenAPI with YAML is the only way to go but you can't easily split the spec into separate files as you would do any program with packages, modules and what not.

There are third party tools[0] which are archived and the libraries they depend upon are up for adoption.

In that space, either you can use something like cue language 1] or something like TypeSpec which is purpose built for this so yet, this seems like a great tool although I have not tried it yet myself.

[0]. https://github.com/APIDevTools/swagger-cli

[1]. https://cuelang.org/

EDIT: formating

Where `kube.cue` sets reasonable defaults (e.g. image is /). The "cluster" runs on a mini PC in my basement, and I have a small Digital Ocean VM with a static IP acting as an ingress (networking via Tailscale). Backups to cloud storage with restic, alerting/monitoring with Prometheus/Grafana, Caddy/Tailscale for local ingress.

[1] https://www.talos.dev/

[2] https://cuelang.org/

I've been somewhat surprised that CUE bills itself as "tooling friendly" and doesn't yet have a language server- the number one bit of tooling most devs use for a particular language.

I'm assuming it's becaus CUE is still unstable?

Anyway, if others are interested in CUE's LSP work, I think https://github.com/cue-lang/cue/issues/142 is the issue to subscribe to

This is where I usually pitch in with "Have your heard of CUELang, our lord and savior?": https://cuelang.org/

- Not turing complete

CUE: The core problem CUE solves is "type checking", which is mainly used in configuration constraint verification scenarios and simple cloud native configuration scenarios.

If you really want executable configurations please consider a newer language like https://dascript.org or https://cuelang.org which provide better type safety.

1- https://news.ycombinator.com/item?id=38030778

Markdown and XML are nice, but what about more advanced documentation formats like OpenAPI? For one recent project, I set up automatic generation of the OpenAPI docs from (much more compact and flexible) CUE definitions (https://cuelang.org/) - which has the bonus of also being able to test the API against the definitions. JetBrains has a CUE plugin, but it's really barebones (doesn't even support jumping from the usage of a schema to its definition). Of course the possibilities when generating docs are endless (just think of the various syntaxes for doc comments, embedding examples/tests in source code etc.)...

It doesn't include validators for TOML and INI, but if you're doing JSON and YAML, I would take a look at using or building upon CUE (https://cuelang.org/). It is a different take on schema definition (plus more), and is surprising terse and powerful model.