Hi Taher, I agree with this , if the state is becoming too large we should have an option of storing it in external state like File System or RocksDb.
@Vinoth Chandar <vin...@apache.org> can the state of HoodieBloomIndex go beyond 10-15 GB Regards, Vinay Patil On Fri, Sep 20, 2019 at 11:37 AM Taher Koitawala <taher...@gmail.com> wrote: > Hey Guys, Any thoughts on the above idea? To handle HoodieBloomIndex with > HeapState, RocksDBState and FsState but on Spark. > > On Tue, Sep 17, 2019 at 1:41 PM Taher Koitawala <taher...@gmail.com> > wrote: > > > Hi Vinoth, > > Having seen the doc and code. I understand the > > HoodieBloomIndex mainly caches key and partition path. Can we address how > > Flink does it? Like, have HeapState where the user chooses to cache the > > Index on heap, RockDBState where indexes are written to RocksDB and > finally > > FsState where indexes can be written to HDFS, S3, Azure Fs. And on top, > we > > can do an index Time To Live. > > > > Regards, > > Taher Koitawala > > > > On Mon, Sep 16, 2019 at 11:43 PM Vinoth Chandar <vin...@apache.org> > wrote: > > > >> I still feel the key thing here is reimplementing HoodieBloomIndex > without > >> needing spark caching. > >> > >> > https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=103093742#Design&Architecture-BloomIndex(non-global) > >> documents the spark DAG in detail. > >> > >> If everyone feels, it's best for me to scope the work out, then happy to > >> do > >> it! > >> > >> On Mon, Sep 16, 2019 at 10:23 AM Taher Koitawala <taher...@gmail.com> > >> wrote: > >> > >> > 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.> > >> > > > > > > > > > > >> > > > > > > > > > >> > > >> > > > > > > > > > >> > >> > > > > > > > > > > > >> > > > > > > > > > > >> > > > > > > > > > > >> > > > > > > > > > >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > > >
