I know different people have worked on dataflow and remember thinking Materialize was very cool and I've used Kafka Streams at work before, and I remember thinking that a framework probably made sense for stitching this all together
My knee-jerk excitements is that this has the potential to be pretty powerful specifically because it's based on Rust so can play really nicely with other languages. Spark runs on the JVM which is a good choice for portability but still introduces a bunch of complexities, and Dask runs in Python which is a fairly hefty dependency you'd almost never bring in unless you're already on python.
In terms of distributed Rust, I've had a look at Lunatic too before which seems good but probably a bit more low-level than what Hydro is going for (although I haven't really done anything other than basic noodling around with it).
Authors in the comments here mention that the flo compiler (?) will accept-and-rewrite Rust code to make it more amenable to distribution. It also appears to be building and optimising the data-flow rather than just distributing the work. There’s also comparisons to timely, which I believe does some kind of incremental compute.
Is that something Akka / RX offer? My quick thought is that they structure code in one binary.
https://rise.cs.berkeley.edu/projects/
Most data processing and distributed systems have some sort of link back to the research this lab has done.
Heh. "most data processing and distributed systems"? Surely you don't mean that the rest of the world was sitting tight working on uniprocessors until this lab got set up in 2017!
And I'm guessing if you include BOOM[1], the links are even deeper.
Now, Berkeley has been a fount of research on the topic, no question about that. I myself worked there (on Bloom, with Joe Hellerstein). But forgetting the other top universities of the world is a bit ... amusing?
Let's take one of the many lists of foundational papers of this field:
http://muratbuffalo.blogspot.com/2021/02/foundational-distri...
How many came out of Berkeley, let alone a recent entry like the AMPLab?
I am not going to make every contextualizing comment an authoritative bibliography , you of all people could have added that w/o being snarky and starting this whole subthread.
I didn't think you were saying that most distributed systems work happening at Berkeley harks back to earlier work at Berkeley. That's a bit obvious.
The only way I can interpret "most distributed systems work now" is a statement about work happening globally. In which case it is a sweeping and false generalization.
Is there another interpretation?
> Good Commandment 3. Thou shalt limit the duration of a center. ...
> To hit home runs, it’s wise to have many at bats. ...
> It’s hard to predict information technology trends much longer than five years. ...
> US Graduate student lifetimes are about five years. ...
> You need a decade after a center finishes to judge if it was a home run. Just 8 of the 12 centers in Table I are old enough, and only 3 of them—RISC, RAID, and the Network of Workstations center—could be considered home runs. If slugging .375 is good, then I’m glad that I had many 5-‐year centers rather than fewer long ones.
(Network of Workstations > Google)