+1 for Approach 1 Point integration with each framework. Pros for point integration - Hudi community is already familiar with spark and spark based actions/shuffles etc. Since both modules can be decoupled, this enables us to have a steady release for Hudi for 1 execution engine (spark) while we hone our skills and iterate on making flink dag optimized, performant with the right configuration. - This might be a stepping stone towards rewriting the entire code base being agnostic of spark/flink. This approach will help us fix tests, intricacies and help make the code base ready for a larger rework. - Seems like the easiest way to add flink support
Cons - More code paths to maintain and reason since the spark and flink integrations will naturally diverge over time. Theoretically, I do like the idea of being able to run the hudi dag on beam more than point integrations, where there is one API/logic to reason about. But practically, that may not be the right direction. Pros - Lesser cognitive burden in maintaining, evolving and releasing the project with one API to reason with. - Theoretically, going forward assuming beam is adopted as a standard programming paradigm for stream/batch, this would enable consumers leverage the power of hudi more easily. Cons - Massive rewrite of the code base. Additionally, since we would have moved away from directly using spark APIs, there is a bigger risk of regression. We would have to be very thorough with all the intricacies and ensure the same stability of new releases. - Managing future features (which may be very spark driven) will either clash or pause or will need to be reworked. - Tuning jobs for Spark/Flink type execution frameworks individually might be difficult and will get difficult over time as the project evolves, where some beam integrations with spark/flink may not work as expected. - Also, as pointed above, need to probably support the hoodie-spark module as a first-class. Thank, Nishith On Tue, Aug 6, 2019 at 9:48 AM taher koitawala <taher...@gmail.com> wrote: > Hi Vinoth, > Are there some tasks I can take up to ramp up the code? Want to get > more used to the code and understand the existing implementation better. > > Thanks, > Taher Koitawala > > On Tue, Aug 6, 2019, 10:02 PM Vinoth Chandar <vin...@apache.org> wrote: > > > Let's see if others have any thoughts as well. We can plan to fix the > > approach by EOW. > > > > On Mon, Aug 5, 2019 at 7:06 PM vino yang <yanghua1...@gmail.com> wrote: > > > > > 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.> > > > > > > > >> > > > > > > > >> > > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
