Hi guys, Also, +1 for Approach 1 like Taher.
> If we can do a comprehensive analysis of this model and come up with. means > to refactor this cleanly, this would be promising. Yes, when we get the conclusion, we could start this work. Best, Vino taher koitawala <taher...@gmail.com> 于2019年8月6日周二 上午12:28写道： > +1 for Approch 1 Point integration with each framework > > Approach 2 has a problem as you said "Developers need to think about > what-if-this-piece-of-code-ran-as-spark-vs-flink.. So in the end, this may > not be the panacea that it seems to be" > > We have seen various pipelines in the beam dag being expressed differently > then we had them in our original usecase. And also switching between spark > and Flink runners in beam have various impact on the pipelines like some > features available in Flink are not available on the spark runner etc. > Refer to this compatible matrix -> > https://beam.apache.org/documentation/runners/capability-matrix/ > > Hence my vote on Approch 1 let's decouple and build the abstract for each > framework. That is a much better option. We will also have more control > over each framework's implement. > > On Mon, Aug 5, 2019, 9:28 PM Vinoth Chandar <vin...@apache.org> wrote: > > > Would like to highlight that there are two distinct approaches here with > > different tradeoffs. Think of this as my braindump, as I have been > thinking > > about this quite a bit in the past. > > > > > > *Approach 1 : Point integration with each framework * > > > > >>We may need a pure client module named for example > > hoodie-client-core(common) > > >> Then we could have: hoodie-client-spark, hoodie-client-flink > > and hoodie-client-beam > > > > (+) This is the safest to do IMO, since we can isolate the current Spark > > execution (hoodie-spark, hoodie-client-spark) from the changes for flink, > > while it stabilizes over few releases. > > (-) Downside is that the utilities needs to be redone : > > hoodie-utilities-spark and hoodie-utilities-flink and > > hoodie-utilities-core ? hoodie-cli? > > > > If we can do a comprehensive analysis of this model and come up with. > means > > to refactor this cleanly, this would be promising. > > > > > > *Approach 2: Beam as the compute abstraction* > > > > Another more drastic approach is to remove Spark as the compute > abstraction > > for writing data and replace it with Beam. > > > > (+) All of the code remains more or less similar and there is one compute > > API to reason about. > > > > (-) The (very big) assumption here is that we are able to tune the spark > > runtime the same way using Beam : custom partitioners, support for all > RDD > > operations we invoke, caching etc etc. > > (-) It will be a massive rewrite and testing of such a large rewrite > would > > also be really challenging, since we need to pay attention to all > intricate > > details to ensure the spark users today experience no > > regressions/side-effects > > (-) Note that we still need to probably support the hoodie-spark module > and > > may be a first-class such integration with flink, for native flink/spark > > pipeline authoring. Users of say DeltaStreamer need to pass in Spark or > > Flink configs anyway.. Developers need to think about > > what-if-this-piece-of-code-ran-as-spark-vs-flink.. So in the end, this > may > > not be the panacea that it seems to be. > > > > > > > > One goal for the HIP is to get us all to agree as a community which one > to > > pick, with sufficient investigation, testing, benchmarking.. > > > > On Sat, Aug 3, 2019 at 7:56 PM vino yang <yanghua1...@gmail.com> wrote: > > > > > +1 for both Beam and Flink > > > > > > > First step here is to probably draw out current hierrarchy and figure > > out > > > > what the abstraction points are.. > > > > In my opinion, the runtime (spark, flink) should be done at the > > > > hoodie-client level and just used by hoodie-utilties seamlessly.. > > > > > > +1 for Vinoth's opinion, it should be the first step. > > > > > > No matter we hope Hudi to integrate with which computing framework. > > > We need to decouple Hudi client and Spark. > > > > > > We may need a pure client module named for example > > > hoodie-client-core(common) > > > > > > Then we could have: hoodie-client-spark, hoodie-client-flink and > > > hoodie-client-beam > > > > > > Suneel Marthi <smar...@apache.org> 于2019年8月4日周日 上午10:45写道： > > > > > > > +1 for Beam -- agree with Semantic Beeng's analysis. > > > > > > > > On Sat, Aug 3, 2019 at 10:30 PM taher koitawala <taher...@gmail.com> > > > > wrote: > > > > > > > > > So the way to go around this is that file a hip. Chalk all th > classes > > > our > > > > > and start moving towards Pure client. > > > > > > > > > > Secondly should we want to try beam? > > > > > > > > > > I think there is to much going on here and I'm not able to follow. > If > > > we > > > > > want to try out beam all along I don't think it makes sense to do > > > > anything > > > > > on Flink then. > > > > > > > > > > On Sun, Aug 4, 2019, 2:30 AM Semantic Beeng < > n...@semanticbeeng.com> > > > > > wrote: > > > > > > > > > >> +1 My money is on this approach. > > > > >> > > > > >> The existing abstractions from Beam seem enough for the use cases > > as I > > > > >> imagine them. > > > > >> > > > > >> Flink also has "dynamic table", "table source" and "table sink" > > which > > > > >> seem very useful abstractions where Hudi might fit nicely. > > > > >> > > > > >> > > > > >> > > > > > > > > > > https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/streaming/dynamic_tables.html > > > > >> > > > > >> > > > > >> Attached a screen shot. > > > > >> > > > > >> This seems to fit with the original premise of Hudi as well. > > > > >> > > > > >> Am exploring this venue with a use case that involves "temporal > > joins > > > on > > > > >> streams" which I need for feature extraction. > > > > >> > > > > >> Anyone is interested in this or has concrete enough needs and use > > > cases > > > > >> please let me know. > > > > >> > > > > >> Best to go from an agreed upon set of 2-3 use cases. > > > > >> > > > > >> Cheers > > > > >> > > > > >> Nick > > > > >> > > > > >> > > > > >> > Also, we do have some Beam experts on the mailing list.. Can you > > > > please > > > > >> weigh on viability of using Beam as the intermediate abstraction > > here > > > > >> between Spark/Flink? > > > > >> Hudi uses RDD apis like groupBy, mapToPair, sortAndRepartition, > > > > >> reduceByKey, countByKey and also does custom partitioning a lot.> > > > > >> > > > > >> > > > > > >> > > > > > > > > > > > > > > >
