rslint VS differential-dataflow

Author here. A few other Rust projects to note that I didn't mention in the original post I've since found:

- Boa (JS engine in Rust) – https://github.com/boa-dev/boa

- RSLint (JS/TS linter in Rust) – https://github.com/rslint/rslint

- Node version manager in Rust – https://github.com/Schniz/fnm

If you know of any other popular ones, let me know. I'm keeping a list :)

[4]: https://github.com/rslint/rslint

hi! sujay from convex here. I remember reading about your "reverse query engine" when we were getting started last year and really liking that framing of the broadcast problem here.

as james mentions, we entirely re-run the javascript function whenever we detect any of its inputs change. incrementality at this layer would be very difficult, since we're dealing with a general purpose programming language. also, since we fully sandbox and determinize these javascript "queries," the majority of the cost is in accessing the database.

eventually, I'd like to explore "reverse query execution" on the boundary between javascript and the underlying data using an approach like differential dataflow [1]. the materialize folks [2] have made a lot of progress applying it for OLAP and readyset [3] is using similar techniques for OLTP.

[1] https://github.com/TimelyDataflow/differential-dataflow

[2] https://materialize.com/

[3] https://readyset.io/

differential dataflow which is used to power materialize db

It's partially inspired by Linq, so the similarity you see is expected.

It's not really arbitrary structures so much, though you're mostly free in what record type you use in a relation (structs and tagged enums are typical, though).

The incremental part is that you can feed it changes to the input (additions/retractions of facts) and get changes to the outputs back with low latency (you can alternatively just use it to keep an index up-to-date, where you can quickly look up based on a key (like a materialized view in SQL)).

This [0] section in the readme of the underlying incremental dataflow framework may help get the concept across, but feel free to follow up if you're still not seeing the incrementality.

[0]: https://github.com/TimelyDataflow/differential-dataflow#an-e...

You might want to look at what DDflow [0] can do, in regards to handling emission/retraction.

If you figure out how this would work in the context of DDflow, let us know. This sounds very interesting.

[0]: https://github.com/TimelyDataflow/differential-dataflow

I've always followed this project but could never quite get the differential dataflow server running that it requires: https://github.com/TimelyDataflow/differential-dataflow (though I probably didn't try hard enough).

[2]: https://github.com/TimelyDataflow/differential-dataflow/issu...

Differential Dataflow and McSherry’s commercial SQL Driver implementation at Materialize:

- https://github.com/TimelyDataflow/differential-dataflow/