>> Currently Spark Streaming micro batching fits well with Hudi, since it amortizes the cost of indexing, workload profiling etc. 1 spark micro batch = 1 hudi commit With the per-record model in Flink, I am not sure how useful it will be to support hudi.. for e.g, 1 input record cannot be 1 hudi commit, it will be inefficient..
Yes, if 1 input record = 1 hudi commit, it would be inefficient. About Flink streaming, we can also implement the "batch" and "micro-batch" model when process data. For example: - aggregation: use flexibility window mechanism; - non-aggregation: use Flink stateful state API cache a batch data >> On first focussing on decoupling of Spark and Hudi alone, yes a full summary of how Spark is being used in a wiki page is a good start IMO. We can then hash out what can be generalized and what cannot be and needs to be left in hudi-client-spark vs hudi-client-core agree Vinoth Chandar <vin...@apache.org> 于2019年8月14日周三 上午8:35写道: > >> We should only stick to Flink Streaming. Furthermore if there is a > requirement for batch then users > >> should use Spark or then we will anyway have a beam integration coming > up. > > Currently Spark Streaming micro batching fits well with Hudi, since it > amortizes the cost of indexing, workload profiling etc. 1 spark micro batch > = 1 hudi commit > With the per-record model in Flink, I am not sure how useful it will be to > support hudi.. for e.g, 1 input record cannot be 1 hudi commit, it will be > inefficient.. > > On first focussing on decoupling of Spark and Hudi alone, yes a full > summary of how Spark is being used in a wiki page is a good start IMO. We > can then hash out what can be generalized and what cannot be and needs to > be left in hudi-client-spark vs hudi-client-core > > > > On Tue, Aug 13, 2019 at 3:57 AM vino yang <yanghua1...@gmail.com> wrote: > > > Hi Nick and Taher, > > > > I just want to answer Nishith's question. Reference his old description > > here: > > > > > You can do a parallel investigation while we are deciding on the module > > structure. You could be looking at all the patterns in Hudi's Spark APIs > > usage (RDD/DataSource/SparkContext) and see if such support can be > achieved > > in theory with Flink. If not, what is the workaround. Documenting such > > patterns would be valuable when multiple engineers are working on it. For > > e:g, Hudi relies on (a) custom partitioning logic for upserts, > (b) > > caching RDDs to avoid reruns of costly stages (c) A Spark upsert task > > knowing its spark partition/task/attempt ids > > > > And just like the title of this thread, we are going to try to decouple > > Hudi and Spark. That means we can run the whole Hudi without depending > > Spark. So we need to analyze all the usage of Spark in Hudi. > > > > Here we are not discussing the integration of Hudi and Flink in the > > application layer. Instead, I want Hudi to be decoupled from Spark and > > allow other engines (such as Flink) to replace Spark. > > > > It can be divided into long-term goals and short-term goals. As Nishith > > stated in a recent email. > > > > I mentioned the Flink Batch API here because Hudi can connect with many > > different Source/Sinks. Some file-based reads are not appropriate for > Flink > > Streaming. > > > > Therefore, this is a comprehensive survey of the use of Spark in Hudi. > > > > Best, > > Vino > > > > > > taher koitawala <taher...@gmail.com> 于2019年8月13日周二 下午5:43写道: > > > > > Hi Vino, > > > According to what I've seen Hudi has a lot of spark component > > flowing > > > throwing it. Like Taskcontexts, JavaSparkContexts etc. The main > classes I > > > guess we should focus upon is HoodieTable and Hoodie write clients. > > > > > > Also Vino, I don't think we should be providing Flink dataset > > > implementation. We should only stick to Flink Streaming. > > > Furthermore if there is a requirement for batch then > users > > > should use Spark or then we will anyway have a beam integration coming > > up. > > > > > > As of cache, How about we write our stateful Flink function and use > > > RocksDbStateBackend with some state TTL. > > > > > > On Tue, Aug 13, 2019, 2:28 PM vino yang <yanghua1...@gmail.com> wrote: > > > > > > > Hi all, > > > > > > > > After doing some research, let me share my information: > > > > > > > > > > > > - Limitation of computing engine capabilities: Hudi uses Spark's > > > > RDD#persist, and Flink currently has no API to cache datasets. > Maybe > > > we > > > > can > > > > only choose to use external storage or do not use cache? For the > use > > > of > > > > other APIs, the two currently offer almost equivalent > capabilities. > > > > - The abstraction of the computing engine is different: > Considering > > > the > > > > different usage scenarios of the computing engine in Hudi, Flink > has > > > not > > > > yet implemented stream batch unification, so we may use both > Flink's > > > > DataSet API (batch processing) and DataStream API (stream > > processing). > > > > > > > > Best, > > > > Vino > > > > > > > > nishith agarwal <n3.nas...@gmail.com> 于2019年8月8日周四 上午12:57写道: > > > > > > > > > Nick, > > > > > > > > > > You bring up a good point about the non-trivial programming model > > > > > differences between these different technologies. From a > theoretical > > > > > perspective, I'd say considering a higher level abstraction makes > > > sense. > > > > I > > > > > think we have to decouple some objectives and concerns here. > > > > > > > > > > a) The immediate desire is to have Hudi be able to run on a Flink > (or > > > > > non-spark) engine. This naturally begs the question of decoupling > > Hudi > > > > > concepts from direct Spark dependencies. > > > > > > > > > > b) If we do want to initiate the above effort, would it make sense > to > > > > just > > > > > have a higher level abstraction, building on other technologies > like > > > beam > > > > > (euphoria etc) and provide single, clean API's that may be more > > > > > maintainable from a code perspective. But at the same time this > will > > > > > introduce challenges on how to maintain efficiency and optimized > > > runtime > > > > > dags for Hudi (since the code would move away from point > integrations > > > and > > > > > whenever this happens, tuning natively for specific engines becomes > > > more > > > > > and more difficult). > > > > > > > > > > My general opinion is that, as the community grows over time with > > more > > > > > folks having an in-depth understanding of Hudi, going from > > > current_state > > > > -> > > > > > (a) -> (b) might be the most reliable and adoptable path for this > > > > project. > > > > > > > > > > Thanks, > > > > > Nishith > > > > > > > > > > On Tue, Aug 6, 2019 at 1:30 PM Semantic Beeng < > > n...@semanticbeeng.com> > > > > > wrote: > > > > > > > > > > > There are some not trivial difference between programming model > and > > > > > > runtime semantics between Beam, Spark and Flink. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > https://beam.apache.org/documentation/runners/capability-matrix/#cap-full-how > > > > > > > > > > > > Nitish, Vino - thoughts? > > > > > > > > > > > > Does it feel to consider a higher level abstraction / DSL instead > > of > > > > > > maintaining different code with same functionality but different > > > > > > programming models ? > > > > > > > > > > > > https://beam.apache.org/documentation/sdks/java/euphoria/ > > > > > > > > > > > > Nick > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > On August 6, 2019 at 4:04 PM nishith agarwal < > n3.nas...@gmail.com> > > > > > wrote: > > > > > > > > > > > > > > > > > > +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.> > > > > > > > > > > > > >> > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >