skylark VS cue

My stance is that YAML is a good format for configuration management and generation -- it's wonderful at filling gaps as your deployment model increases in complexity to provide a mechanism to "render" your configuration -- much like Skylark [1] does (derived from Google's internal GCL).

YAML ends up being a powerfully declarative model [2] for the state of a data structure, rather than a straight representation, ironically often enough being used in turn for an imperative model like in Ansible [3]. Definitely friendlier than JSON. But personally, I really like YAML because it lets me compose using a traits/mixins-like model using & and , which allows for verbose, structured configuration inputs but concise configuration files.

docker-compose YAML files extension fields [4], imo, are a great example of this type of model in action. When you leave this much pre-deserialization flexibility in your configuration representation, it makes building cool stuff like docker-compose ECS support x-aws- extension keys [5] and other plugin system-type capabilities much more straightforward than, for example, adding a new language feature to HCL.

[1]: https://github.com/google/skylark

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.