Guys I think we are slowing down on this again. We need to start planning small small tasks towards this VC please can you help fast track this?
Regards, Taher Koitawala On Thu, Aug 15, 2019, 10:07 AM Vinoth Chandar <vin...@apache.org> wrote: > Look forward to the analysis. A key class to read would be > HoodieBloomIndex, which uses a lot of spark caching and shuffles. > > On Tue, Aug 13, 2019 at 7:52 PM vino yang <yanghua1...@gmail.com> wrote: > > > >> 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.> > > > > > > > > > > > > > > > > >> > > > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
