glow VS nebari

You can absolutely do WebGL. glow is a good place if you want to do the webgl stuff yourself. With wgpu you can as well, though it is mostly targeted to the upcoming WebGPU standard. I have seen many crates that are capable of gltf loading, but I haven't used any yet. Hence I can't reall recommend one. But gltf capability does not really have anything to do with rust. As long as you can read a file and translate it to your engines scene/entity structure, I don't see why it shouldn't be possible to load gltf files.

Most of it is written in Rust and compiles to Wasm with WebGL/OpenGL bindings via glow. The settings panel is written in Elm and the whole project is compiled with Nix. There’s also a very basic native desktop app. I haven’t seen many examples of a complete setup like this, so hopefully this can serve as a template for anyone trying out a similar stack.

If you want to target desktop, mobile and/or web: https://github.com/grovesNL/glow

A/B Street, which comprises a UI library, lots of data import pipelines, and traffic simulation. 100k LoC, the only unafe is to make system calls through glow

https://github.com/grovesNL/glow/blob/main/examples/howto/src/main.rs :)

https://github.com/grovesNL/glow is used by wgpu for its OpenGL ES / WebGL backend

By the way, nowadays many projects use glow instead of glutin because of.. I think it's because glow works on wasm, in the web (as webgl).

But what glow actually does is highlighted on lines 66 to 123. This section is filled with gl.some_function() calls, which is the same on every platform.

Or, you could also look at Nebari, which underlies BonsaiDB https://github.com/khonsulabs/nebari/

There is Nebari which is the KV part of BonsaiDB I've used both successfully (and that is currently in production)

This looks like it could have been inspired by BonsaiDB's recent problems. I think the authors are now trying to achieve something similar to your project with a new storage layer called Sediment (which will become a part of Nebari if successful).

I ran my benchmark suite on my 2017 i7 MacBook Pro (Filevault enabled), and I was able to perform a random "get" operation out of a 1 million record dataset in an average of 18us (microseconds).

I haven't spent much time documenting the low-level structures outside of the code itself.

Nebari, the underlying storage layer, also has its own coverage report.

Quite well, but Sled doesn't support the same feature set. I've benchmarked our low-level storage layer against Sled in its own repostiory: Nebari Benchmark README. There are two links two criterion reports on that page.

I maintain several large codebases that have #![forbid(unsafe)] annotations, which prevent unsafe code from being written in those codebases directly. BonsaiDb clocks in at just shy of 30k LOC, and depends on Nebari which is another 12k LOC. Those two crates make up the bulk of a networked database implementation.

I have, and there's a set of microbenchmarks in the repository. There are two links to reports on that page -- one that's executed on GitHub Actions, which can have quite widely varying performance, and one that's executed on a "dedicated" VPS instance from Scaleway -- exact specs are on that page. I've kicked off another run on that VPS, since it hasn't been run since October. It takes a while to run, so if it still says October, try hitting refresh a little later.

I replaced Sled in BonsaiDb with a new storage layer I wrote: Nebari. I just wrote a post this morning recalling my struggles over the last month to feel comfortable sharing this project outside of a smaller group of people.