blog VS differential-dataflow

This reminds me of a great blog post by Frank McSherry (Materialize, timely dataflow, etc) talking about how using the right tools on a laptop could beat out a bunch of these JVM distributed querying tools because... data locality basically.

https://github.com/frankmcsherry/blog/blob/master/posts/2015...

Oh, cool!

And yeah, licenses can be challenging and frustrating, especially the first time you release a major project.

I am really super excited by the idea of embedded Datalog in Rust. I run into a lot of situations where I need something that fits in that awkward gap between SQL and Prolog. I want more expressiveness, better composability, and better graph support than SQL. But I also want finite-sized results that I can materialize in bounded time.

There has been some very neat work with incrementally-updated Datalog in the Rust community. For example, I think Datafrog is really neat: https://github.com/frankmcsherry/blog/blob/master/posts/2018... But it's great to see more neat projects in this space, so thank you.

They used to but Frank McSherry (author of differential dataflow) wrote them a specialized version without all the dataflow infrastructure [1]. It's part of the rust-lang nursery [2] now but hasn't been updated in a while, so I'm not sure what happened to it.

[1] https://github.com/frankmcsherry/blog/blob/master/posts/2018...

[2] https://github.com/rust-lang/datafrog

Importantly, this doesn't just use memoization (it actually avoids having to spend memory on that), but rather uses operators (nodes in the dataflow graph) that directly work with `(time, data, delta)` tuples. The `time` is a general lattice, so fairly flexible (e.g. for expressing loop nesting/recursive computations, but also for handling multiple input sources with their own timestamps), and the `delta` type is between a (potentially commutative) semigroup (don't be confused, they use addition as the group operation) and an abelian group. E.g. collections that are iteratively refined in loops often need an abelian `delta` type, while monoids (semigroup + explicit zero element) allow for efficient append-only computations [0].

[0]: https://github.com/frankmcsherry/blog/blob/master/posts/2019...

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...