pluto VS jsonnet

You can get all the code for this example from here. This link provides an online IDE for this sample application. Click the Fork button in the upper right corner to create your own development environment, and then you can directly modify the code and deploy it to AWS in the browser.

Big Data Scenario: Word Count

Plutolang - Pluto is a new open-source programming language designed to help developers write cloud applications, making it easier to utilize cloud services. Developers can directly use required resources such as KV databases and message queues in their code based on business needs. Pluto uses static code analysis to obtain the infrastructure resource topology the application depends on and deploys the corresponding resource instances and applications on the specified cloud platform or Kubernetes.

Developers define variables in their code, and Pluto takes care of automatically creating and managing the required cloud resource components based on those variables. This simplifies the process of deploying and managing cloud infrastructure, enabling developers to make better use of the cloud. Read more

I'm currently working on a project with a similar concept. However, at the moment, I am using static program analysis technology to infer infrastructure dependencies. But I am considering using AI to deduce the dependencies and generate the final Infrastructure as Code. So, in the architecture, we can replace the deducer and generator with an implementation using AI, as shown in the diagram.

jsonnet[1] and kapitan[2] are the tools I currently use. Their learning curve is not optimal (and I tried to contribute to smoothen it with a jsonnet course[3] and a 'get started wit kapitan' blog post[4]), but once used to it it's hard to do without, and their combination makes them even more useful (esp. if you deploy K8s).

In Ruud's case, Jsonnet might have been worth looking at as Hashicorp tools can be configured with json in addition to HCL. But that would have been less fun I guess ;-)

I hope for Ruud it finds its niche, there's quite some competition in this field!

1: https://jsonnet.org/

Kubernetes config is a decent example. I had ChatGPT generate a representative silly example -- the content doesn't matter so much as the structure:

https://gist.github.com/cstrahan/528b00cd5c3a22e3d8f057bb1a7...

Now consider 100s (if not 1000s) of such files.

I haven't given Pkl an in depth look yet, but I can say that the Industry Standard™ of "simple YAML" + string substitution (with delicate, error prone indentation -- since YAML is indentation sensitive) is easily beat by any of:

- https://jsonnet.org/

- https://nickel-lang.org/

- https://nixos.org/manual/nix/stable/language/index.html

- https://dhall-lang.org/

- (insert many more here, probably including Pkl)

jsonnet cli: link

Jsonnet: A data template language implemented in C++, suitable for application and tool developers, can generate configuration data and organize, simplify and manage large configurations without side effects.

[Language: Jsonnet] (on GitHub)

Maybe you'd like jsonnet: https://jsonnet.org/

I find it particularly useful for configurations that often have repeated boilerplate, like ansible playbooks or deploying a bunch of "similar-but" services to kubernetes (with https://tanka.dev).

Dhall is also quite interesting, with some tradeoffs: https://dhall-lang.org/

A few years ago I did a small comparison by re-implementing one of my simpler ansible playbooks: https://github.com/retzkek/ansible-dhall-jsonnet

Apologies for the lack of context, and for missing this comment until today.

Both are tools for defining kubernetes manifests (which are YAML) in a reusable manner.

Jsonnet is a formally specified extension of JSON. It’s essentially a functional programming language (w/some object oriented features) that generates config files in JSON/YAML/etc, so it’s straightforward to determine whether an input file is valid, and to throw an error that points to an exact line if it’s not. It has a high learning curve, especially for people whose only experience is with imperative languages.

https://jsonnet.org/

Helm charts also generate YAML/JSON config files, but they use Go templating. This is easier and faster to understand, since it’s mostly string substitution and not much logic (there’s conditionals, iterators, and very basic helper functions). Unfortunately a simple typo or mistake can cause errors that are difficult to diagnose (the message may indicate a problem far away in code from the actual mistake). It can also generate output that’s valid according to the string templating rules, but not what was intended, which can be very confusing to debug.

Despite these shortcomings, the vast majority of kubernetes applications are distributed as helm charts. I understand why things ended up this way, but I still wish it were more common for people to invest the upfront effort to learn the superior tool, so it could be more widespread.

I like Google's Jsonnet [1], which has all of this except for 4.

Jsonnet is quite mature, with fairly wide language adoption, and has the benefit of supporting expressions, including conditionals, arithmetic, as well as being able to define reusable blocks inside function definitions or external files.

It's not suitable as a serialization format, but great for config. It's popular in some circles, but I'm sad that it has not reached wider adoption.

[1] https://jsonnet.org/