+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.> > > > >> > > > >> > > > > >> > > > > > > > > > >
