lalrpop VS terraform

And some related parser tools: - https://github.com/kevinmehall/rust-peg - https://github.com/pest-parser/pest - https://github.com/lalrpop/lalrpop

lalrpop

Rust is a very nice langage for implementing compilers, and has a nice ecosystem for it (logos, rust-peg, lalrpop, astmaker -- this one is mine --, etc...).

rust-langdev has a lot of libraries for building compilers in Rust. Perhaps you could use these to make your implementation easier, and revisit it later if you want to build things from scratch. I'd suggest logos for lexing, LALRPOP / chumsky for parsing, and rust-gc for garbage collection.

Hi! For one of my projects I am currently using lalrpop (https://github.com/lalrpop/lalrpop/tree/master/doc/calculator/src), which is far from complete, but has the basic syntax I was looking for. I took some examples and worked around some lexer stuff but I’m currently happy with it. If you use it and have Intellij stuff installed, you can also use a plug-in for highlighting and SOMETIMES error checking. Otherwise, even VSCode had a great plug-in for highlighting!

How would you compare and contrast this with, say, lalrpop?

lalrpop is great. It's a completely different approach from nom, but for parsing a programming language, I would at least consider it. RustPython uses it.

I presume LALRPOP handles left recursion just fine.

I wanted to share an experimental side project I have been working on for some time. I constantly use commands like `jq` and `yq` for processing structured data in my day job and I was curious if a similar idea could be applied to images.

Another goal of mine was to get some exposure to with rust. I discovered the LALRPOP parser generator which really helped moved the project along (https://github.com/lalrpop/lalrpop)

The key point here is that we are going to depend on the lalrpop library to generate the parser based on the formal grammar we define. Note that we have it as part of the [build-dependencies] section and we only depend on a tiny utility crate called lalrpop-util at runtime. The reason for that is the main lalrpop "magic" would happen during the crate compilation (in the build.rs file) when lalrpop would generate the deterministic pushdown automaton based on our grammar. The code generation logic is not required to be part of our interpreter, we only need a few utility methods from the lalrpop-util for the automaton to operate. You might have noticed that we also enable the lexer feature of lalrpop, because we are going to use lexer provided by lalrpop as well (please refer to the Part I if you do not know what the lexer is).

I am not only a big fan of hashicorp terraform. I'm also one of the early adopters of it. So this is my main go-to Infrastructure as a Code tool. However all the resources I use are supported by other IaaC solutions such as AWS CloudFormation and AWS CDK. You definitely got to use one to avoid loosing the track of resources you create.

State Encryption was one of those long requested features[0] (I had it on my ideas list for years[1]) that Hashicorp didn't have much incentive to build. I don't think it has to with distancing opentofu as such, but the opentofu team prioritizing the right things that customers actually need.

[0]: https://github.com/hashicorp/terraform/issues/9556

[1]: https://github.com/captn3m0/ideas#-mars-terraform-remote-htt...

https://github.com/hashicorp/terraform/issues/34402

I’m not a lawyer and have no idea who is right or wrong but I understand why Hashicorp is scrutinizing this.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).

For example, integrating Terraform for infrastructure as code (IaC) into the IDP can streamline updates and rollbacks.

In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.

Infrastructure as Code (IaC) is an important part of any true hosting operation in the public cloud. Each of these platforms has their own IaC solution, e.g. AWS CloudFormation. But they also support popular open-source IaC tools like Pulumi or Terraform. A category of tools that also needs to be discussed is API gateways and other app-specific load balancers. There are applications for internal consumption, which can be called microservices if you have a lot of them. And often microservices use advanced networking options such as a service mesh instead of just the native private network offered by a VPC.