3 ms·

How does this compare to timely [0] in terms of data flow? Can you represent control flow like loops in the IR? [0] https://github.com/TimelyDataflow/timely-da

How does this compare to timely [0] in terms of data flow? Can you represent control flow like loops in the IR? [0] https://github.com/TimelyDataflow/timely-dataflow https://github.com/TimelyDataflow/timely-dataflow

leicmi 2y ago
Their latest paper [0] refers to Naiad(timely dataflow) a few times, e.g.: "Inspired by ingress/egress nodes in Naiad [34], nested streams can be processed by nested dataflow graphs, which iteratively process chunks of data sourced from a larger stream with support for carrying state across iterations." [0] https://hydro.run/papers/flo.pdf https://hydro.run/papers/flo.pdf
tel 2y ago
Reading a bit about it from the Flo paper - Describe a dataflow graph just like Timely - Comes from a more "semantic dataflow" kind of heritage (frp, composition, flow-of-flows, algebraic operators, proof-oriented) as opposed to the more operationally minded background of Timely - Has a (very) different notion of "progress" than Timely, focused instead of ensuring the compositions are generative in light of potentially unbounded streaming inputs - In fact, Flo doesn't really have any notion of "timeliness", no timestamping at all - Supports nested looping like Timely, though via a very different mechanism. The basic algebra is extremely non-cyclic, but the nested streams/graphs formalism allows for iteration. The paper also makes a direct comparison with DBSP, which as I understand it, is also part of the Timely/Naiad heritage. Similar to Timely, the authors suggest that Flo could be a unifying semantic framework for several other similar systems (Flink, LVars, DBSP). So I'd say that the authors of Flo are aware of Naiad/Timely and took inspiration of nested iterative graphs, but little else.
- v3xro 2y ago
  Thanks for the summary, really looks like something that is worth digging into!
- shadaj 2y ago
  Flo lead-author here! This is spot on :) Flo aims to be a bit less opinionated than Timely in how the runtime should behave, so in particular we don't support the type of "time-traveling" computation that Timely needs when you have iterative computations on datasets with retractions. This is also one of the core differences of Timely compared to DBSP, which uses a flat representation (z-sets) to store retractions rather than using versioned elements. This allows retractions to be propagated as just negative item counts which fits into the Flo model (and therefore Hydro).

leicmi 2y ago

Their latest paper [0] refers to Naiad(timely dataflow) a few times, e.g.: "Inspired by ingress/egress nodes in Naiad [34], nested streams can be processed by nested dataflow graphs, which iteratively process chunks of data sourced from a larger stream with support for carrying state across iterations." [0] https://hydro.run/papers/flo.pdf https://hydro.run/papers/flo.pdf

tel 2y ago

Reading a bit about it from the Flo paper - Describe a dataflow graph just like Timely - Comes from a more "semantic dataflow" kind of heritage (frp, composition, flow-of-flows, algebraic operators, proof-oriented) as opposed to the more operationally minded background of Timely - Has a (very) different notion of "progress" than Timely, focused instead of ensuring the compositions are generative in light of potentially unbounded streaming inputs - In fact, Flo doesn't really have any notion of "timeliness", no timestamping at all - Supports nested looping like Timely, though via a very different mechanism. The basic algebra is extremely non-cyclic, but the nested streams/graphs formalism allows for iteration. The paper also makes a direct comparison with DBSP, which as I understand it, is also part of the Timely/Naiad heritage. Similar to Timely, the authors suggest that Flo could be a unifying semantic framework for several other similar systems (Flink, LVars, DBSP). So I'd say that the authors of Flo are aware of Naiad/Timely and took inspiration of nested iterative graphs, but little else.

v3xro 2y ago
Thanks for the summary, really looks like something that is worth digging into!
shadaj 2y ago
Flo lead-author here! This is spot on :) Flo aims to be a bit less opinionated than Timely in how the runtime should behave, so in particular we don't support the type of "time-traveling" computation that Timely needs when you have iterative computations on datasets with retractions. This is also one of the core differences of Timely compared to DBSP, which uses a flat representation (z-sets) to store retractions rather than using versioned elements. This allows retractions to be propagated as just negative item counts which fits into the Flo model (and therefore Hydro).