Viewing profile — ryzhyk

But as gz09 said, both DD and DBSP are data-parallel architectures that can evaluate queries concurrently on multiple threads or multiple machines.

I'd say the difference is in the type of transaction isolation guarantees each system provides. DBSP can process multiple diffs in parallel, and when it's done it outputs a single …

The paradox of IVM is that the concept has been around for a long time; there are hundreds of papers on this topic; hardly anyone would disagree that it's a useful feature for a da…

The backend for this app is literally 300 lines of SQL + Rust -- very cool.

The backend for this app is literally 300 lines of SQL + Rust -- very cool.

[I am the author of the blog] It's been fun working on this demo. FGA is a very cool concept, but building an efficient FGA engine is hard: you basically need to solve a graph reac…

Your explanation of why ORDER BY is not efficiently incrementalizable is spot on. At the moment Feldera ignores the outermost ORDER BY clause, unless it is part of the ORDER BY ...…

Also there's now a DBSP implementation in pure Python! https://github.com/brurucy/pydbsp

Thanks again! You may want to check out this tutorial for a hands-on introduction to DBSP: https://docs.rs/dbsp/0.28.0/dbsp/tutorial/index.html

Apologies about the confusion. We indeed only solve incremental computation for Abelian groups, and the paper is making a case that database tables can be modeled as Abelian groups…

Good point. The goal is indeed to be a Postgres of incremental computing: any SQL query should "just work" out of the box with good performance and standard SQL semantics. You shou…

Thanks for the kind words about DDlog :) The reason DBSP and Differential Dataflow work so well is because they are specialized to relational computations. Relational operators hav…

We have our own formal model called DBSP: https://docs.feldera.com/papers It is indeed inspired by timely/differential, but is not exactly comparable to it. One nice property of DB…

The computational complexity of running an analytical query on a database is, at best, O(N), where N is the size of the database. The computational complexity of evaluating queries…

The correct way to think about the problem is in terms of evaluating joins (or any other queries) over changing datasets. And for that you need an engine designed for *incremental*…

A streaming join indeed requires an unbounded buffer in the most general case when inputs keep growing and any input record on one side of the join can match any record on the othe…

Both reads and writes are O(1) in time complexity. Writes additionally have the log(N) amortized cost of maintaining the LSM tree.

That's right, we perform static dataflow analysis to determine what data can get discarded. GC itself is done lazily as part of LSM tree maintenance. For MAX specifically, we don't…

I'm a developer of DBSP. Our repo now lives here: https://github.com/feldera/dbsp/ . And here is some more benchmarking data vs Flink and Beam: https://github.com/feldera/dbsp/tree…