salsa VS differential-dataflow

There are some excellent crates already in the Rust ecosystem (notably Salsa) for performing incremental computation, although they generally have different goals to Depends.

The content of that "Core Concepts" page sounds a lot like https://github.com/salsa-rs/salsa

I know of salsa, but I don't know if it can persist changes to disk (and my guess from looking at this issue is that it can't).

- the practical approach to this is to split workload into work units (aka tasks/queries/etc.) and then force information flow through centralized "request"/"query" APIs that automatically track dependencies - see https://github.com/salsa-rs/salsa for more information

This, and as pointed Salsa.

These guys might be of interest for you, they think a lot about incremental parsers. https://github.com/salsa-rs/salsa

salsa-rs authored by niko matsakis and other top rust devs

Some optimizations are not easy with Go. For example caching like in Rust Analyser Salsa Framework

How would this differ from salsa (https://github.com/salsa-rs/salsa) and the query-driven approach used by rustc?

Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views.

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

https://materialize.com/

I'm looking for this but can't find it, how does this project compare to differential dataflow?

As a sibling commenter mentioned, it's built on timely dataflow (which is lower-level), but that already has differential dataflow[0] built on top of it by the same authors.

How do they differ?

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

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...