Awesome!!! Amit - remind me your time zone? JB, do you want to join? I'm free this week all afternoons (say after 2pm) in Pacific Time, and mornings of Wed & Fri. We'll probably need half an hour to an hour.
On Mon, Mar 13, 2017 at 1:29 PM Aljoscha Krettek <[email protected]> wrote: > I whipped up a quick version for Flink that seems to work: > https://github.com/apache/beam/pull/2235 > > There are still two failing tests, as described in the PR. > > On Mon, Mar 13, 2017, at 20:10, Amit Sela wrote: > > +1 for a video call. I think it should be pretty straight forward for the > > Spark runner after the work on read from UnboundedSource and after > > GroupAlsoByWindow, but from my experience such a call could move us > > forward > > fast enough. > > > > On Mon, Mar 13, 2017, 20:37 Eugene Kirpichov <[email protected]> > > wrote: > > > > > Hi all, > > > > > > Let us continue working on this. I am back from various travels and am > > > eager to help. > > > > > > Amit, JB - would you like to perhaps have a videocall to hash this out > for > > > the Spark runner? > > > > > > Aljoscha - are the necessary Flink changes done / or is the need for > them > > > obviated by using the (existing) runner-facing state/timer APIs? > Should we > > > have a videocall too? > > > > > > Thomas - what do you think about getting this into Apex runner? > > > > > > (I think videocalls will allow to make rapid progress, but it's > probably a > > > better idea to keep them separate since they'll involve a lot of > > > runner-specific details) > > > > > > PS - The completion of this in Dataflow streaming runner is currently > > > waiting only on having a small service-side change implemented and > rolled > > > out for termination of streaming jobs. > > > > > > On Wed, Feb 8, 2017 at 10:55 AM Kenneth Knowles <[email protected]> > wrote: > > > > > > I recommend proceeding with the runner-facing state & timer APIs; they > are > > > lower-level and more appropriate for this. All runners provide them or > use > > > runners/core implementations, as they are needed for triggering. > > > > > > On Wed, Feb 8, 2017 at 10:34 AM, Eugene Kirpichov < > [email protected]> > > > wrote: > > > > > > Thanks Aljoscha! > > > > > > Minor note: I'm not familiar with what level of support for timers > Flink > > > currently has - however SDF in Direct and Dataflow runner currently > does > > > not use the user-facing state/timer APIs - rather, it uses the > > > runner-facing APIs (StateInternals and TimerInternals) - perhaps Flink > > > already implements these. We may want to change this, but for now it's > good > > > enough (besides, SDF uses watermark holds, which are not supported by > the > > > user-facing state API yet). > > > > > > On Wed, Feb 8, 2017 at 10:19 AM Aljoscha Krettek < > > > [email protected]> wrote: > > > > > > Thanks for the motivation, Eugene! :-) > > > > > > I've wanted to do this for a while now but was waiting for the Flink > 1.2 > > > release (which happened this week)! There's some prerequisite work to > be > > > done on the Flink runner: we'll move to the new timer interfaces > introduced > > > in Flink 1.2 and implement support for both the user facing state and > timer > > > APIs. This should make implementation of SDF easier. > > > > > > On Wed, Feb 8, 2017 at 7:06 PM, Eugene Kirpichov <[email protected] > > > > > wrote: > > > > > > Thanks! Looking forward to this work. > > > > > > On Wed, Feb 8, 2017 at 3:50 AM Jean-Baptiste Onofré <[email protected]> > > > wrote: > > > > > > Thanks for the update Eugene. > > > > > > I will work on the spark runner with Amit. > > > > > > Regards > > > JB > > > > > > On Feb 7, 2017, 19:12, at 19:12, Eugene Kirpichov > > > <[email protected]> wrote: > > > >Hello, > > > > > > > >I'm almost done adding support for Splittable DoFn > > > >http://s.apache.org/splittable-do-fn to Dataflow streaming runner*, > and > > > >very excited about that. There's only 1 PR > > > ><https://github.com/apache/beam/pull/1898> remaining, plus enabling > > > >some > > > >tests. > > > > > > > >* (batch runner is much harder because it's not yet quite clear to me > > > >how > > > >to properly implement liquid sharding > > > >< > > > > https://cloud.google.com/blog/big-data/2016/05/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow > > > > > > > >with > > > >SDF - and the current API is not ready for that yet) > > > > > > > >After implementing all the runner-agnostic parts of Splittable DoFn, I > > > >found them quite easy to integrate into Dataflow streaming runner, and > > > >I > > > >think this means it should be easy to integrate into other runners > too. > > > > > > > >====== Why it'd be cool ====== > > > >The general benefits of SDF are well-described in the design doc > > > >(linked > > > >above). > > > >As for right now - if we integrated SDF with all runners, it'd already > > > >enable us to start greatly simplifying the code of existing streaming > > > >connectors (CountingInput, Kafka, Pubsub, JMS) and writing new > > > >connectors > > > >(e.g. a really nice one to implement would be "directory watcher", > that > > > >continuously returns new files in a directory). > > > > > > > >As a teaser, here's the complete implementation of an "unbounded > > > >counter" I > > > >used for my test of Dataflow runner integration: > > > > > > > > class CountFn extends DoFn<String, String> { > > > > @ProcessElement > > > >public ProcessContinuation process(ProcessContext c, > OffsetRangeTracker > > > >tracker) { > > > > for (int i = tracker.currentRestriction().getFrom(); > > > >tracker.tryClaim(i); ++i) c.output(i); > > > > return resume(); > > > > } > > > > > > > > @GetInitialRestriction > > > > public OffsetRange getInitialRange(String element) { return new > > > >OffsetRange(0, Integer.MAX_VALUE); } > > > > > > > > @NewTracker > > > > public OffsetRangeTracker newTracker(OffsetRange range) { return > new > > > >OffsetRangeTracker(range); } > > > > } > > > > > > > >====== What I'm asking ====== > > > >So, I'd like to ask for help integrating SDF into Spark, Flink and > Apex > > > >runners from people who are intimately familiar with them - > > > >specifically, I > > > >was hoping best-case I could nerd-snipe some of you into taking over > > > >the > > > >integration of SDF with your favorite runner ;) > > > > > > > >The proper set of people seems to be +Aljoscha Krettek > > > ><[email protected]> +Maximilian Michels > > > ><[email protected]> > > > >[email protected] <[email protected]> +Amit Sela > > > ><[email protected]> +Thomas > > > >Weise unless I forgot somebody. > > > > > > > >Average-case, I was looking for runner-specific guidance on how to do > > > >it > > > >myself. > > > > > > > >====== If you want to help ====== > > > >If somebody decides to take this over, in my absence (I'll be mostly > > > >gone > > > >for ~the next month)., the best people to ask for implementation > > > >advice are +Kenn > > > >Knowles <[email protected]> and +Daniel Mills <[email protected]> . > > > > > > > >For reference, here's how SDF is implemented in the direct runner: > > > >- Direct runner overrides > > > >< > > > > https://github.com/apache/beam/blob/0616245e654c60ae94cc2c188f857b74a62d9b24/runners/direct-java/src/main/java/org/apache/beam/runners/direct/ParDoMultiOverrideFactory.java > > > > > > > > ParDo.of() for a splittable DoFn and replaces it with SplittableParDo > > > >< > > > > https://github.com/apache/beam/blob/master/runners/core-java/src/main/java/org/apache/beam/runners/core/SplittableParDo.java > > > > > > > >(common > > > >transform expansion) > > > >- SplittableParDo uses two runner-specific primitive transforms: > > > >"GBKIntoKeyedWorkItems" and "SplittableProcessElements". Direct runner > > > >overrides the first one like this > > > >< > > > > https://github.com/apache/beam/blob/cc28f0cb4c44169f933475ae29a32599024d3a1f/runners/direct-java/src/main/java/org/apache/beam/runners/direct/DirectGBKIntoKeyedWorkItemsOverrideFactory.java > > > >, > > > >and directly implements evaluation of the second one like this > > > >< > > > > https://github.com/apache/beam/blob/cc28f0cb4c44169f933475ae29a32599024d3a1f/runners/direct-java/src/main/java/org/apache/beam/runners/direct/SplittableProcessElementsEvaluatorFactory.java > > > >, > > > >using runner hooks introduced in this PR > > > ><https://github.com/apache/beam/pull/1824>. At the core of the hooks > is > > > >"ProcessFn" which is like a regular DoFn but has to be prepared at > > > >runtime > > > >with some hooks (state, timers, and runner access to > > > >RestrictionTracker) > > > >before you invoke it. I added a convenience implementation of the hook > > > >mimicking behavior of UnboundedSource. > > > >- The relevant runner-agnostic tests are in SplittableDoFnTest > > > >< > > > > https://github.com/apache/beam/blob/cc28f0cb4c44169f933475ae29a32599024d3a1f/sdks/java/core/src/test/java/org/apache/beam/sdk/transforms/SplittableDoFnTest.java > > > > > > > >. > > > > > > > >That's all it takes, really - the runner has to implement these two > > > >transforms. When I looked at Spark and Flink runners, it was not quite > > > >clear to me how to implement the GBKIntoKeyedWorkItems transform, e.g. > > > >Spark runner currently doesn't use KeyedWorkItem at all - but it seems > > > >definitely possible. > > > > > > > >Thanks! > > > > > > > > > > > > > > > -- > > > Data Artisans GmbH | Stresemannstr. 121A | 10963 Berlin > > > > > > [email protected] > > > +49-(0)30-55599146 <+49%2030%2055599146> > > > > > > Registered at Amtsgericht Charlottenburg - HRB 158244 B > > > Managing Directors: Kostas Tzoumas, Stephan Ewen > > > > > > > > > >
