rules_jsonnet
cue
rules_jsonnet | cue | |
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1 | 111 | |
64 | 4,779 | |
- | 1.7% | |
6.7 | 9.8 | |
about 1 month ago | 6 days ago | |
Starlark | Go | |
Apache License 2.0 | Apache License 2.0 |
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rules_jsonnet
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Jsonnet – The Data Templating Language
I can definitely sympathize here - in every context, just straight JSON/YAML configuration seems never expressive enough, but the tooling created in response always seems to come with sharp edges.
Here are some of the things I appreciate about Jsonnet:
- It evals to JSON, so even though the semantics of the language are confusing, it is reasonably easy to eval and iterate on some Jsonnet until it emits what one is expecting - and after that, it's easy to create some validation tests so that regressions don't occur.
- It takes advantage of the fact that JSON is a lowest-common-denominator for many data serialization formats. YAML is technically a superset of JSON, so valid JSON is also valid YAML. Proto3 messages have a canonical JSON representation, so JSON can also adhere to protobuf schemas. This covers most "serialized data structure" use-cases I typically encounter (TOML and HCL are outliers, but many tools that accept those also accept equivalent JSON). This means that with a little bit of build-tool duct-taping, Jsonnet can be used to generate configurations for a wide variety of tooling.
- Jsonnet is itself a superset of JSON - so those more willing to write verbose JSON than learn Jsonnet can still write JSON that someone else can import/use elsewhere. Using Jsonnet does not preclude falling back to JSON.
- The tooling works well - installing the Jsonnet VSCode plugin brings in a code formatter that does an excellent job, and rules_jsonnet[0] provides good bazel integration, if that's your thing.
I'm excited about Jsonnet because now as long as other tool authors decide to consume JSON, I can more easily abstract away their verbosity without writing a purpose-built tool (looking at you, Kubernetes) without resorting to text templating (ahem Helm). Jsonnet might just be my "one JSON-generation language to rule them all"!
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Though if Starlark is your thing, do checkout out skycfg[1]
[0] - https://github.com/bazelbuild/rules_jsonnet
[1] - https://github.com/stripe/skycfg
cue
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TypeSpec: A New Language for API-Centric Development
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
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Show HN: Workout Tracker – self-hosted, single binary web application
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/
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Apple releases Pkl – onfiguration as code language
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
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Why the fuck are we templating YAML? (2019)
This is where I usually pitch in with "Have your heard of CUELang, our lord and savior?": https://cuelang.org/
- Not turing complete
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10 Ways for Kubernetes Declarative Configuration Management
CUE: The core problem CUE solves is "type checking", which is mainly used in configuration constraint verification scenarios and simple cloud native configuration scenarios.
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Lua is a viable alternative for JSON
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
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Writerside – a new technical writing environment from JetBrains
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.)...
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Show HN: Config-file-validator – CLI tool to validate all your config files
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.
- That's a Lot of YAML
- An INI Critique of TOML
What are some alternatives?
skycfg - Skycfg is an extension library for the Starlark language that adds support for constructing Protocol Buffer messages.
dhall-lang - Maintainable configuration files
isopod - An expressive DSL and framework for Kubernetes configuration without YAML
jsonnet - Jsonnet - The data templating language
ursonnet - experimental ur-cause tracer for jsonnet
terraform - Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.
aperture - Rate limiting, caching, and request prioritization for modern workloads
starlark-rust - A Rust implementation of the Starlark language
nickel - Better configuration for less
Protobuf - Protocol Buffers - Google's data interchange format
github-desktop - A version of GitHub Desktop packaged with Conveyor
jsonnet-libs - Grafana Labs' Jsonnet libraries