dustdata VS nebari

5. Dustdata - https://github.com/rustbase/dustdata

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.