dhall-kubernetes VS cue

I hate yaml with a passion. It marginally better than xml for reading (wins huge on comment syntax) and worse for everything else. It makes zero sense we somehow ended up with it as standard configuration serialization format.

Note yaml is not a DSL. It's a tree serialization format! Everything interesting is happening after it is parsed. Extreme examples point to e.g. github actions conditions.

Anyway, back on topic - maybe not prolog for CDK, but still quite interesting: Dhall-kubernetes - https://github.com/dhall-lang/dhall-kubernetes

At this point, if it’s painful enough, why isn’t compiling-to-yml tools more popular?

Example: https://github.com/dhall-lang/dhall-kubernetes

Haven’t used dhall myself but I’d definitely prefer a DSL on top of yaml.

Dhall provides https://github.com/dhall-lang/dhall-kubernetes which is exactly this: statically type-checked kubernetes config generation.

Dhall is my favorite configuration language that I never get around to using.

I manage DNS in Terraform, and since every Terraform provider uses different objects definitions, and every object definition is rather verbose, Dhall would be a way to specify my own DRY types and leave the provider-specific details in one place. Adding new DNS entries and moving several domains between providers would be a matter of changing fewer lines.

Dhall also has Kubernetes bindings:

https://github.com/dhall-lang/dhall-kubernetes

Although I'm tempted to just stick to Helm here, even though it's less type-safe.

Not Helm directly, but does something like Dhall fit your question? https://github.com/dhall-lang/dhall-kubernetes

Have you heard about Dhall? It’s a programming language used for generating configuration files for a variety of purposes. One of them is to replace old and limited formats such as JSON and YAML. It is DRYable, secure, and even suitable for creating K8s manifests. The latter option isn’t something for anyone: you have to learn a new language and deal with its peculiarities, but it might be really helpful when you have tons of YAML configs. I’ve recently made a short intro to Dhall for K8s in this review.

Best thing is Dhall that I am aware of. Same situation, working as a consultant, forced to use broken things.

https://github.com/dhall-lang/dhall-kubernetes

Lets look at a specific example. Take Kubernetes: everything is yaml, with complete schemas, all the way down. From your perspective this is configuration utopia, right? Meanwhile back in reality k8s is the poster child of "yaml hell". From the day it was released, people took one look at it, gave it a giant NOPE and instantly spawned half a dozen templating languages. The most popular of these is helm, which has a terrible, no good, very bad design: full of potential injection attacks from purely textual string substitution, manually specified indentation to embed parameterized blocks, virtually no intermediate validation, no way to validate unused features, etc etc

Compare to dhall which publishes a complete set of dhall-k8s schema mappings which enables you to factor out any design you want down to as few configuration variables as you like, while validating the configuration generators themselves at design time. https://github.com/dhall-lang/dhall-kubernetes#more-modular-...

The solution I like is Dhall. They even have a Kubernetes solution that will catch a lot of issues at compile-time, before you try to apply it to Kubernetes. At earthly we aren't actually using it though. Our Kubernetes guru found it to be a bit slow but I am hopeful it or something like it will be the future.

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.