Hi Jark, *About local keyed state:*
I object to moving it out of this FLIP. It's one of the ways we support Local aggregation on the implementation of operator level, though not the only one. I guess you have misunderstood my last reply. I just tell you the difference between `DataStream#process` and `KeyedStream#process`. Users who use `localKeyBy#process` API are completely unaware of the differences when using the Stateful API. The local keyed state we introduced is not exposed to the outside! It exists only internally. When calling localKeyBy to returns an instance of `KeyedStream`, we introduce `KeyScope` in `KeyedStream` to distinguish them. I suggest you take a look at our design documentation. *About your concerns:* 1) I agree that not all exposed APIs are meaningful if localKeyBy returns `KeyedStream`. I did not find a signature of timeWindow(long, long) API. IMO, all the window related APIs are useful and meaningful, It is one of the main means of our local aggregation, and we should not limit its flexibility. I am not against localKeyBy returns `LocalKeyedStream` if you agree `localKeyBy` is reasonable. 2) I have replied more than one times that we are trying to support a more general local aggregation. The meaning of aggregation here is not limited to the implementation of AggregateFunction. And that's exactly what we got from `KeyedStream#process`. Why do you need a "local process" concept? I don't think it is necessary at all. I don't want to say that the aggregation you think is narrow, but we want to use this API to provide enough flexibility. This is the primary focus of DataStream, as @Piotr Nowojski <pi...@ververica.com> also agrees. I also agree "local process" is more than "local aggregate", that's users' choice if they want to use. Again, it should not be removed from this FLIP because it is the added value of localKeyBy. Best, Vino Jark Wu <imj...@gmail.com> 于2019年6月27日周四 下午8:47写道： > Hi Vino, > > So the difference between `DataStream.localKeyBy().process()` with > `DataStream.process()` is that the former can access keyed state and the > latter can only access operator state. > I think it's out of the scope of designing a local aggregation API. It > might be an extension of state API, i.e. local keyed state. > The difference between local keyed state with operator state (if I > understand correctly) is local keyed state can be backed on RocksDB? or > making "keyed state" locally? > IMO, it's a larger topic than local aggregation and should be discussed > separately. I cc-ed people who works on states @Tzu-Li (Gordon) Tai > <tzuli...@apache.org> @Seth @Yu Li to give some feedback from the > perspective of state. > > Regarding to the API designing updated in your FLIP, I have some concerns: > > 1) The "localKeyBy()" method returns a "KeyedStream" which exposes all > method of it. > However, not every method makes sense or have a clear definition on local > stream. > For example, "countWindow(long, long)", "timeWindow(long, long)", > "window(WindowAssigner)", and "intervalJoin" Hequn mentioned before. > I would suggest we can expose the only APIs we needed for local > aggregation and leave the others later. > We can return a "LocalKeyedStream" and may expose only some dedicated > methods: for example, "aggregate()", "trigger()". > These APIs do not need to expose local keyed state to support local > aggregation. > > 2) I think `localKeyBy().process()` is something called "local process", > not just "local aggregate". > It needs more discussion about local keyed state, and I would like to put > it out of this FLIP. > > > Regards, > Jark > > > On Thu, 27 Jun 2019 at 13:03, vino yang <yanghua1...@gmail.com> wrote: > >> Hi all, >> >> I also think it's a good idea that we need to agree on the API level >> first. >> >> I am sorry, we did not give some usage examples of the API in the FLIP >> documentation before. This may have caused some misunderstandings about the >> discussion of this mail thread. >> >> So, now I have added some usage examples in the "Public Interfaces" >> section of the FLIP-44 documentation. >> >> Let us first know the API through its use examples. >> >> Any feedback and questions please let me know. >> >> Best, >> Vino >> >> vino yang <yanghua1...@gmail.com> 于2019年6月27日周四 下午12:51写道： >> >>> Hi Jark, >>> >>> `DataStream.localKeyBy().process()` has some key difference with >>> `DataStream.process()`. The former API receive `KeyedProcessFunction` >>> (sorry my previous reply may let you misunderstood), the latter receive API >>> receive `ProcessFunction`. When you read the java doc of ProcessFunction, >>> you can find a "*Note*" statement: >>> >>> Access to keyed state and timers (which are also scoped to a key) is >>>> only available if the ProcessFunction is applied on a KeyedStream. >>> >>> >>> In addition, you can also compare the two >>> implementations(`ProcessOperator` and `KeyedProcessOperator`) of them to >>> view the difference. >>> >>> IMO, the "Note" statement means a lot for many use scenarios. >>> For example, if we cannot access keyed state, we can only use heap memory >>> to buffer data while it does not guarantee the semantics of correctness! >>> And the timer is also very important in some scenarios. >>> >>> That's why we say our API is flexible, it can get most benefits (even >>> subsequent potential benefits in the future) from KeyedStream. >>> >>> I have added some instructions on the use of localKeyBy in the FLIP-44 >>> documentation. >>> >>> Best, >>> Vino >>> >>> >>> Jark Wu <imj...@gmail.com> 于2019年6月27日周四 上午10:44写道： >>> >>>> Hi Piotr, >>>> >>>> I think the state migration you raised is a good point. Having >>>> "stream.enableLocalAggregation(Trigger)” might add some implicit operators >>>> which users can't set uid and cause the state compatibility/evolution >>>> problems. >>>> So let's put this in rejected alternatives. >>>> >>>> Hi Vino, >>>> >>>> You mentioned several times that "DataStream.localKeyBy().process()" >>>> can solve the data skew problem of "DataStream.keyBy().process()". >>>> I'm curious about what's the differences between "DataStream.process()" >>>> and "DataStream.localKeyBy().process()"? >>>> Can't "DataStream.process()" solve the data skew problem? >>>> >>>> Best, >>>> Jark >>>> >>>> >>>> On Wed, 26 Jun 2019 at 18:20, Piotr Nowojski <pi...@ververica.com> >>>> wrote: >>>> >>>>> Hi Jark and Vino, >>>>> >>>>> I agree fully with Jark, that in order to have the discussion focused >>>>> and to limit the number of parallel topics, we should first focus on one >>>>> topic. We can first decide on the API and later we can discuss the runtime >>>>> details. At least as long as we keep the potential requirements of the >>>>> runtime part in mind while designing the API. >>>>> >>>>> Regarding the automatic optimisation and proposed by Jark: >>>>> >>>>> "stream.enableLocalAggregation(Trigger)” >>>>> >>>>> I would be against that in the DataStream API for the reasons that >>>>> Vino presented. There was a discussion thread about future directions of >>>>> Table API vs DataStream API and the consensus was that the automatic >>>>> optimisations are one of the dividing lines between those two, for at >>>>> least >>>>> a couple of reasons. Flexibility and full control over the program was one >>>>> of them. Another is state migration. Having >>>>> "stream.enableLocalAggregation(Trigger)” that might add some implicit >>>>> operators in the job graph can cause problems with savepoint/checkpoint >>>>> compatibility. >>>>> >>>>> However I haven’t thought about/looked into the details of the Vino’s >>>>> API proposal, so I can not fully judge it. >>>>> >>>>> Piotrek >>>>> >>>>> > On 26 Jun 2019, at 09:17, vino yang <yanghua1...@gmail.com> wrote: >>>>> > >>>>> > Hi Jark, >>>>> > >>>>> > Similar questions and responses have been repeated many times. >>>>> > >>>>> > Why didn't we spend more sections discussing the API? >>>>> > >>>>> > Because we try to reuse the ability of KeyedStream. The localKeyBy >>>>> API just returns the KeyedStream, that's our design, we can get all the >>>>> benefit from the KeyedStream and get further benefit from WindowedStream. >>>>> The APIs come from KeyedStream and WindowedStream is long-tested and >>>>> flexible. Yes, we spend much space discussing the local keyed state, >>>>> that's >>>>> not the goal and motivation, that's the way to implement local >>>>> aggregation. >>>>> It is much more complicated than the API we introduced, so we spent more >>>>> section. Of course, this is the implementation level of the Operator. We >>>>> also agreed to support the implementation of buffer+flush and added >>>>> related >>>>> instructions to the documentation. This needs to wait for the community to >>>>> recognize, and if the community agrees, we will give more instructions. >>>>> What's more, I have indicated before that we welcome state-related >>>>> commenters to participate in the discussion, but it is not wise to modify >>>>> the FLIP title. >>>>> > >>>>> > About the API of local aggregation: >>>>> > >>>>> > I don't object to ease of use is very important. But IMHO >>>>> flexibility is the most important at the DataStream API level. Otherwise, >>>>> what does DataStream mean? The significance of the DataStream API is that >>>>> it is more flexible than Table/SQL, if it cannot provide this point then >>>>> everyone would just use Table/SQL. >>>>> > >>>>> > The DataStream API should focus more on flexibility than on >>>>> automatic optimization, which allows users to have more possibilities to >>>>> implement complex programs and meet specific scenarios. There are a lot of >>>>> programs written using the DataStream API that are far more complex than >>>>> we >>>>> think. It is very difficult to optimize at the API level and the benefit >>>>> is >>>>> very low. >>>>> > >>>>> > I want to say that we support a more generalized local aggregation. >>>>> I mentioned in the previous reply that not only the UDF that implements >>>>> AggregateFunction is called aggregation. In some complex scenarios, we >>>>> have >>>>> to support local aggregation through ProcessFunction and >>>>> ProcessWindowFunction to solve the data skew problem. How do you support >>>>> them in the API implementation and optimization you mentioned? >>>>> > >>>>> > Flexible APIs are arbitrarily combined to result in erroneous >>>>> semantics, which does not prove that flexibility is meaningless because >>>>> the >>>>> user is the decision maker. I have been exemplified many times, for many >>>>> APIs in DataStream, if we arbitrarily combined them, they also do not have >>>>> much practical significance. So, users who use flexible APIs need to >>>>> understand what they are doing and what is the right choice. >>>>> > >>>>> > I think that if we discuss this, there will be no result. >>>>> > >>>>> > @Stephan Ewen <mailto:se...@apache.org> , @Aljoscha Krettek <mailto: >>>>> aljos...@apache.org> and @Piotr Nowojski <mailto:pi...@ververica.com> >>>>> Do you have further comments? >>>>> > >>>>> > >>>>> > Jark Wu <imj...@gmail.com <mailto:imj...@gmail.com>> 于2019年6月26日周三 >>>>> 上午11:46写道： >>>>> > Thanks for the long discussion Vino, Kurt, Hequn, Piotr and others, >>>>> > >>>>> > It seems that we still have some different ideas about the API >>>>> > (localKeyBy()?) and implementation details (reuse window operator? >>>>> local >>>>> > keyed state?). >>>>> > And the discussion is stalled and mixed with motivation and API and >>>>> > implementation discussion. >>>>> > >>>>> > In order to make some progress in this topic, I want to summarize the >>>>> > points (pls correct me if I'm wrong or missing sth) and would >>>>> suggest to >>>>> > split >>>>> > the topic into following aspects and discuss them one by one. >>>>> > >>>>> > 1) What's the main purpose of this FLIP? >>>>> > - From the title of this FLIP, it is to support local aggregate. >>>>> However >>>>> > from the content of the FLIP, 80% are introducing a new state called >>>>> local >>>>> > keyed state. >>>>> > - If we mainly want to introduce local keyed state, then we should >>>>> > re-title the FLIP and involve in more people who works on state. >>>>> > - If we mainly want to support local aggregate, then we can jump to >>>>> step 2 >>>>> > to discuss the API design. >>>>> > >>>>> > 2) What does the API look like? >>>>> > - Vino proposed to use "localKeyBy()" to do local process, the >>>>> output of >>>>> > local process is the result type of aggregate function. >>>>> > a) For non-windowed aggregate: >>>>> > input.localKeyBy(..).aggregate(agg1).keyBy(..).aggregate(agg2) >>>>> **NOT >>>>> > SUPPORT** >>>>> > b) For windowed aggregate: >>>>> > >>>>> input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2) >>>>> > >>>>> > 3) What's the implementation detail? >>>>> > - may reuse window operator or not. >>>>> > - may introduce a new state concepts or not. >>>>> > - may not have state in local operator by flushing buffers in >>>>> > prepareSnapshotPreBarrier >>>>> > - and so on... >>>>> > - we can discuss these later when we reach a consensus on API >>>>> > >>>>> > -------------------- >>>>> > >>>>> > Here are my thoughts: >>>>> > >>>>> > 1) Purpose of this FLIP >>>>> > - From the motivation section in the FLIP, I think the purpose is to >>>>> > support local aggregation to solve the data skew issue. >>>>> > Then I think we should focus on how to provide a easy to use and >>>>> clear >>>>> > API to support **local aggregation**. >>>>> > - Vino's point is centered around the local keyed state API (or >>>>> > localKeyBy()), and how to leverage the local keyed state API to >>>>> support >>>>> > local aggregation. >>>>> > But I'm afraid it's not a good way to design API for local >>>>> aggregation. >>>>> > >>>>> > 2) local aggregation API >>>>> > - IMO, the method call chain >>>>> > >>>>> "input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)" >>>>> > is not such easy to use. >>>>> > Because we have to provide two implementation for an aggregation >>>>> (one >>>>> > for partial agg, another for final agg). And we have to take care of >>>>> > the first window call, an inappropriate window call will break the >>>>> > sematics. >>>>> > - From my point of view, local aggregation is a mature concept which >>>>> > should output the intermediate accumulator (ACC) in the past period >>>>> of time >>>>> > (a trigger). >>>>> > And the downstream final aggregation will merge ACCs received >>>>> from local >>>>> > side, and output the current final result. >>>>> > - The current "AggregateFunction" API in DataStream already has the >>>>> > accumulator type and "merge" method. So the only thing user need to >>>>> do is >>>>> > how to enable >>>>> > local aggregation opimization and set a trigger. >>>>> > - One idea comes to my head is that, assume we have a windowed >>>>> aggregation >>>>> > stream: "val stream = input.keyBy().window(w).aggregate(agg)". We can >>>>> > provide an API on the stream. >>>>> > For exmaple, "stream.enableLocalAggregation(Trigger)", the >>>>> trigger can >>>>> > be "ContinuousEventTimeTrigger.of(Time.of(Time.minutes(1)))". Then >>>>> it will >>>>> > be optmized into >>>>> > local operator + final operator, and local operator will combine >>>>> records >>>>> > every minute on event time. >>>>> > - In this way, there is only one line added, and the output is the >>>>> same >>>>> > with before, because it is just an opimization. >>>>> > >>>>> > >>>>> > Regards, >>>>> > Jark >>>>> > >>>>> > >>>>> > >>>>> > On Tue, 25 Jun 2019 at 14:34, vino yang <yanghua1...@gmail.com >>>>> <mailto:yanghua1...@gmail.com>> wrote: >>>>> > >>>>> > > Hi Kurt, >>>>> > > >>>>> > > Answer your questions: >>>>> > > >>>>> > > a) Sorry, I just updated the Google doc, still have no time update >>>>> the >>>>> > > FLIP, will update FLIP as soon as possible. >>>>> > > About your description at this point, I have a question, what does >>>>> it mean: >>>>> > > how do we combine with >>>>> > > `AggregateFunction`? >>>>> > > >>>>> > > I have shown you the examples which Flink has supported: >>>>> > > >>>>> > > - input.localKeyBy(0).aggregate() >>>>> > > - input.localKeyBy(0).window().aggregate() >>>>> > > >>>>> > > You can show me a example about how do we combine with >>>>> `AggregateFuncion` >>>>> > > through your localAggregate API. >>>>> > > >>>>> > > About the example, how to do the local aggregation for AVG, >>>>> consider this >>>>> > > code: >>>>> > > >>>>> > > >>>>> > > >>>>> > > >>>>> > > >>>>> > > >>>>> > > >>>>> > > >>>>> > > >>>>> > > *DataStream<Tuple2<String, Long>> source = null; source >>>>> .localKeyBy(0) >>>>> > > .timeWindow(Time.seconds(60)) .aggregate(agg1, new >>>>> > > WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>, >>>>> String, >>>>> > > TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60)) >>>>> .aggregate(agg2, >>>>> > > new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, >>>>> String, >>>>> > > TimeWindow>());* >>>>> > > >>>>> > > *agg1:* >>>>> > > *signature : new AggregateFunction<Tuple2<String, Long>, >>>>> Tuple2<Long, >>>>> > > Long>, Tuple2<Long, Long>>() {}* >>>>> > > *input param type: Tuple2<String, Long> f0: key, f1: value* >>>>> > > *intermediate result type: Tuple2<Long, Long>, f0: local >>>>> aggregated sum; >>>>> > > f1: local aggregated count* >>>>> > > *output param type: Tuple2<Long, Long>, f0: local aggregated sum; >>>>> f1: >>>>> > > local aggregated count* >>>>> > > >>>>> > > *agg2:* >>>>> > > *signature: new AggregateFunction<Tuple3<String, Long, Long>, Long, >>>>> > > Tuple2<String, Long>>() {},* >>>>> > > *input param type: Tuple3<String, Long, Long>, f0: key, f1: local >>>>> > > aggregated sum; f2: local aggregated count* >>>>> > > >>>>> > > *intermediate result type: Long avg result* >>>>> > > *output param type: Tuple2<String, Long> f0: key, f1 avg result* >>>>> > > >>>>> > > For sliding window, we just need to change the window type if >>>>> users want to >>>>> > > do. >>>>> > > Again, we try to give the design and implementation in the >>>>> DataStream >>>>> > > level. So I believe we can match all the requirements(It's just >>>>> that the >>>>> > > implementation may be different) comes from the SQL level. >>>>> > > >>>>> > > b) Yes, Theoretically, your thought is right. But in reality, it >>>>> cannot >>>>> > > bring many benefits. >>>>> > > If we want to get the benefits from the window API, while we do >>>>> not reuse >>>>> > > the window operator? And just copy some many duplicated code to >>>>> another >>>>> > > operator? >>>>> > > >>>>> > > c) OK, I agree to let the state backend committers join this >>>>> discussion. >>>>> > > >>>>> > > Best, >>>>> > > Vino >>>>> > > >>>>> > > >>>>> > > Kurt Young <ykt...@gmail.com <mailto:ykt...@gmail.com>> >>>>> 于2019年6月24日周一 下午6:53写道： >>>>> > > >>>>> > > > Hi vino, >>>>> > > > >>>>> > > > One thing to add, for a), I think use one or two examples like >>>>> how to do >>>>> > > > local aggregation on a sliding window, >>>>> > > > and how do we do local aggregation on an unbounded aggregate, >>>>> will do a >>>>> > > lot >>>>> > > > help. >>>>> > > > >>>>> > > > Best, >>>>> > > > Kurt >>>>> > > > >>>>> > > > >>>>> > > > On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <ykt...@gmail.com >>>>> <mailto:ykt...@gmail.com>> wrote: >>>>> > > > >>>>> > > > > Hi vino, >>>>> > > > > >>>>> > > > > I think there are several things still need discussion. >>>>> > > > > >>>>> > > > > a) We all agree that we should first go with a unified >>>>> abstraction, but >>>>> > > > > the abstraction is not reflected by the FLIP. >>>>> > > > > If your answer is "locakKeyBy" API, then I would ask how do we >>>>> combine >>>>> > > > > with `AggregateFunction`, and how do >>>>> > > > > we do proper local aggregation for those have different >>>>> intermediate >>>>> > > > > result type, like AVG. Could you add these >>>>> > > > > to the document? >>>>> > > > > >>>>> > > > > b) From implementation side, reusing window operator is one of >>>>> the >>>>> > > > > possible solutions, but not we base on window >>>>> > > > > operator to have two different implementations. What I >>>>> understanding >>>>> > > is, >>>>> > > > > one of the possible implementations should >>>>> > > > > not touch window operator. >>>>> > > > > >>>>> > > > > c) 80% of your FLIP content is actually describing how do we >>>>> support >>>>> > > > local >>>>> > > > > keyed state. I don't know if this is necessary >>>>> > > > > to introduce at the first step and we should also involve >>>>> committers >>>>> > > work >>>>> > > > > on state backend to share their thoughts. >>>>> > > > > >>>>> > > > > Best, >>>>> > > > > Kurt >>>>> > > > > >>>>> > > > > >>>>> > > > > On Mon, Jun 24, 2019 at 5:17 PM vino yang < >>>>> yanghua1...@gmail.com <mailto:yanghua1...@gmail.com>> >>>>> > > wrote: >>>>> > > > > >>>>> > > > >> Hi Kurt, >>>>> > > > >> >>>>> > > > >> You did not give more further different opinions, so I >>>>> thought you >>>>> > > have >>>>> > > > >> agreed with the design after we promised to support two kinds >>>>> of >>>>> > > > >> implementation. >>>>> > > > >> >>>>> > > > >> In API level, we have answered your question about pass an >>>>> > > > >> AggregateFunction to do the aggregation. No matter introduce >>>>> > > localKeyBy >>>>> > > > >> API >>>>> > > > >> or not, we can support AggregateFunction. >>>>> > > > >> >>>>> > > > >> So what's your different opinion now? Can you share it with >>>>> us? >>>>> > > > >> >>>>> > > > >> Best, >>>>> > > > >> Vino >>>>> > > > >> >>>>> > > > >> Kurt Young <ykt...@gmail.com <mailto:ykt...@gmail.com>> >>>>> 于2019年6月24日周一 下午4:24写道： >>>>> > > > >> >>>>> > > > >> > Hi vino, >>>>> > > > >> > >>>>> > > > >> > Sorry I don't see the consensus about reusing window >>>>> operator and >>>>> > > keep >>>>> > > > >> the >>>>> > > > >> > API design of localKeyBy. But I think we should definitely >>>>> more >>>>> > > > thoughts >>>>> > > > >> > about this topic. >>>>> > > > >> > >>>>> > > > >> > I also try to loop in Stephan for this discussion. >>>>> > > > >> > >>>>> > > > >> > Best, >>>>> > > > >> > Kurt >>>>> > > > >> > >>>>> > > > >> > >>>>> > > > >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang < >>>>> yanghua1...@gmail.com <mailto:yanghua1...@gmail.com>> >>>>> > > > >> wrote: >>>>> > > > >> > >>>>> > > > >> > > Hi all, >>>>> > > > >> > > >>>>> > > > >> > > I am happy we have a wonderful discussion and received >>>>> many >>>>> > > valuable >>>>> > > > >> > > opinions in the last few days. >>>>> > > > >> > > >>>>> > > > >> > > Now, let me try to summarize what we have reached >>>>> consensus about >>>>> > > > the >>>>> > > > >> > > changes in the design. >>>>> > > > >> > > >>>>> > > > >> > > - provide a unified abstraction to support two kinds of >>>>> > > > >> > implementation; >>>>> > > > >> > > - reuse WindowOperator and try to enhance it so that >>>>> we can >>>>> > > make >>>>> > > > >> the >>>>> > > > >> > > intermediate result of the local aggregation can be >>>>> buffered >>>>> > > and >>>>> > > > >> > > flushed to >>>>> > > > >> > > support two kinds of implementation; >>>>> > > > >> > > - keep the API design of localKeyBy, but declare the >>>>> disabled >>>>> > > > some >>>>> > > > >> > APIs >>>>> > > > >> > > we cannot support currently, and provide a >>>>> configurable API for >>>>> > > > >> users >>>>> > > > >> > to >>>>> > > > >> > > choose how to handle intermediate result; >>>>> > > > >> > > >>>>> > > > >> > > The above three points have been updated in the design >>>>> doc. Any >>>>> > > > >> > > questions, please let me know. >>>>> > > > >> > > >>>>> > > > >> > > @Aljoscha Krettek <aljos...@apache.org <mailto: >>>>> aljos...@apache.org>> What do you think? Any >>>>> > > > >> further >>>>> > > > >> > > comments? >>>>> > > > >> > > >>>>> > > > >> > > Best, >>>>> > > > >> > > Vino >>>>> > > > >> > > >>>>> > > > >> > > vino yang <yanghua1...@gmail.com <mailto: >>>>> yanghua1...@gmail.com>> 于2019年6月20日周四 下午2:02写道： >>>>> > > > >> > > >>>>> > > > >> > > > Hi Kurt, >>>>> > > > >> > > > >>>>> > > > >> > > > Thanks for your comments. >>>>> > > > >> > > > >>>>> > > > >> > > > It seems we come to a consensus that we should >>>>> alleviate the >>>>> > > > >> > performance >>>>> > > > >> > > > degraded by data skew with local aggregation. In this >>>>> FLIP, our >>>>> > > > key >>>>> > > > >> > > > solution is to introduce local keyed partition to >>>>> achieve this >>>>> > > > goal. >>>>> > > > >> > > > >>>>> > > > >> > > > I also agree that we can benefit a lot from the usage of >>>>> > > > >> > > > AggregateFunction. In combination with localKeyBy, We >>>>> can easily >>>>> > > > >> use it >>>>> > > > >> > > to >>>>> > > > >> > > > achieve local aggregation: >>>>> > > > >> > > > >>>>> > > > >> > > > - input.localKeyBy(0).aggregate() >>>>> > > > >> > > > - input.localKeyBy(0).window().aggregate() >>>>> > > > >> > > > >>>>> > > > >> > > > >>>>> > > > >> > > > I think the only problem here is the choices between >>>>> > > > >> > > > >>>>> > > > >> > > > - (1) Introducing a new primitive called localKeyBy >>>>> and >>>>> > > > implement >>>>> > > > >> > > > local aggregation with existing operators, or >>>>> > > > >> > > > - (2) Introducing an operator called >>>>> localAggregation which >>>>> > > is >>>>> > > > >> > > > composed of a key selector, a window-like operator, >>>>> and an >>>>> > > > >> aggregate >>>>> > > > >> > > > function. >>>>> > > > >> > > > >>>>> > > > >> > > > >>>>> > > > >> > > > There may exist some optimization opportunities by >>>>> providing a >>>>> > > > >> > composited >>>>> > > > >> > > > interface for local aggregation. But at the same time, >>>>> in my >>>>> > > > >> opinion, >>>>> > > > >> > we >>>>> > > > >> > > > lose flexibility (Or we need certain efforts to achieve >>>>> the same >>>>> > > > >> > > > flexibility). >>>>> > > > >> > > > >>>>> > > > >> > > > As said in the previous mails, we have many use cases >>>>> where the >>>>> > > > >> > > > aggregation is very complicated and cannot be performed >>>>> with >>>>> > > > >> > > > AggregateFunction. For example, users may perform >>>>> windowed >>>>> > > > >> aggregations >>>>> > > > >> > > > according to time, data values, or even external >>>>> storage. >>>>> > > > Typically, >>>>> > > > >> > they >>>>> > > > >> > > > now use KeyedProcessFunction or customized triggers to >>>>> implement >>>>> > > > >> these >>>>> > > > >> > > > aggregations. It's not easy to address data skew in >>>>> such cases >>>>> > > > with >>>>> > > > >> a >>>>> > > > >> > > > composited interface for local aggregation. >>>>> > > > >> > > > >>>>> > > > >> > > > Given that Data Stream API is exactly targeted at these >>>>> cases >>>>> > > > where >>>>> > > > >> the >>>>> > > > >> > > > application logic is very complicated and optimization >>>>> does not >>>>> > > > >> > matter, I >>>>> > > > >> > > > think it's a better choice to provide a relatively >>>>> low-level and >>>>> > > > >> > > canonical >>>>> > > > >> > > > interface. >>>>> > > > >> > > > >>>>> > > > >> > > > The composited interface, on the other side, may be a >>>>> good >>>>> > > choice >>>>> > > > in >>>>> > > > >> > > > declarative interfaces, including SQL and Table API, as >>>>> it >>>>> > > allows >>>>> > > > >> more >>>>> > > > >> > > > optimization opportunities. >>>>> > > > >> > > > >>>>> > > > >> > > > Best, >>>>> > > > >> > > > Vino >>>>> > > > >> > > > >>>>> > > > >> > > > >>>>> > > > >> > > > Kurt Young <ykt...@gmail.com <mailto:ykt...@gmail.com>> >>>>> 于2019年6月20日周四 上午10:15写道： >>>>> > > > >> > > > >>>>> > > > >> > > >> Hi all, >>>>> > > > >> > > >> >>>>> > > > >> > > >> As vino said in previous emails, I think we should >>>>> first >>>>> > > discuss >>>>> > > > >> and >>>>> > > > >> > > >> decide >>>>> > > > >> > > >> what kind of use cases this FLIP want to >>>>> > > > >> > > >> resolve, and what the API should look like. From my >>>>> side, I >>>>> > > think >>>>> > > > >> this >>>>> > > > >> > > is >>>>> > > > >> > > >> probably the root cause of current divergence. >>>>> > > > >> > > >> >>>>> > > > >> > > >> My understand is (from the FLIP title and motivation >>>>> section of >>>>> > > > the >>>>> > > > >> > > >> document), we want to have a proper support of >>>>> > > > >> > > >> local aggregation, or pre aggregation. This is not a >>>>> very new >>>>> > > > idea, >>>>> > > > >> > most >>>>> > > > >> > > >> SQL engine already did this improvement. And >>>>> > > > >> > > >> the core concept about this is, there should be an >>>>> > > > >> AggregateFunction, >>>>> > > > >> > no >>>>> > > > >> > > >> matter it's a Flink runtime's AggregateFunction or >>>>> > > > >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation >>>>> have >>>>> > > concept >>>>> > > > >> of >>>>> > > > >> > > >> intermediate data type, sometimes we call it ACC. >>>>> > > > >> > > >> I quickly went through the POC piotr did before [1], >>>>> it also >>>>> > > > >> directly >>>>> > > > >> > > uses >>>>> > > > >> > > >> AggregateFunction. >>>>> > > > >> > > >> >>>>> > > > >> > > >> But the thing is, after reading the design of this >>>>> FLIP, I >>>>> > > can't >>>>> > > > >> help >>>>> > > > >> > > >> myself feeling that this FLIP is not targeting to have >>>>> a proper >>>>> > > > >> > > >> local aggregation support. It actually want to >>>>> introduce >>>>> > > another >>>>> > > > >> > > concept: >>>>> > > > >> > > >> LocalKeyBy, and how to split and merge local key >>>>> groups, >>>>> > > > >> > > >> and how to properly support state on local key. Local >>>>> > > aggregation >>>>> > > > >> just >>>>> > > > >> > > >> happened to be one possible use case of LocalKeyBy. >>>>> > > > >> > > >> But it lacks supporting the essential concept of local >>>>> > > > aggregation, >>>>> > > > >> > > which >>>>> > > > >> > > >> is intermediate data type. Without this, I really >>>>> don't thing >>>>> > > > >> > > >> it is a good fit of local aggregation. >>>>> > > > >> > > >> >>>>> > > > >> > > >> Here I want to make sure of the scope or the goal >>>>> about this >>>>> > > > FLIP, >>>>> > > > >> do >>>>> > > > >> > we >>>>> > > > >> > > >> want to have a proper local aggregation engine, or we >>>>> > > > >> > > >> just want to introduce a new concept called LocalKeyBy? >>>>> > > > >> > > >> >>>>> > > > >> > > >> [1]: https://github.com/apache/flink/pull/4626 < >>>>> https://github.com/apache/flink/pull/4626> >>>>> > > > >> > > >> >>>>> > > > >> > > >> Best, >>>>> > > > >> > > >> Kurt >>>>> > > > >> > > >> >>>>> > > > >> > > >> >>>>> > > > >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang < >>>>> > > yanghua1...@gmail.com <mailto:yanghua1...@gmail.com> >>>>> > > > > >>>>> > > > >> > > wrote: >>>>> > > > >> > > >> >>>>> > > > >> > > >> > Hi Hequn, >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > Thanks for your comments! >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > I agree that allowing local aggregation reusing >>>>> window API >>>>> > > and >>>>> > > > >> > > refining >>>>> > > > >> > > >> > window operator to make it match both requirements >>>>> (come from >>>>> > > > our >>>>> > > > >> > and >>>>> > > > >> > > >> Kurt) >>>>> > > > >> > > >> > is a good decision! >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > Concerning your questions: >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > 1. The result of >>>>> input.localKeyBy(0).sum(1).keyBy(0).sum(1) >>>>> > > may >>>>> > > > >> be >>>>> > > > >> > > >> > meaningless. >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > Yes, it does not make sense in most cases. However, >>>>> I also >>>>> > > want >>>>> > > > >> to >>>>> > > > >> > > note >>>>> > > > >> > > >> > users should know the right semantics of localKeyBy >>>>> and use >>>>> > > it >>>>> > > > >> > > >> correctly. >>>>> > > > >> > > >> > Because this issue also exists for the global keyBy, >>>>> consider >>>>> > > > >> this >>>>> > > > >> > > >> example: >>>>> > > > >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is >>>>> also >>>>> > > > >> > meaningless. >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > 2. About the semantics of >>>>> > > > >> > > >> > >>>>> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > Good catch! I agree with you that it's not good to >>>>> enable all >>>>> > > > >> > > >> > functionalities for localKeyBy from KeyedStream. >>>>> > > > >> > > >> > Currently, We do not support some APIs such as >>>>> > > > >> > > >> > connect/join/intervalJoin/coGroup. This is due to >>>>> that we >>>>> > > force >>>>> > > > >> the >>>>> > > > >> > > >> > operators on LocalKeyedStreams chained with the >>>>> inputs. >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > Best, >>>>> > > > >> > > >> > Vino >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > Hequn Cheng <chenghe...@gmail.com <mailto: >>>>> chenghe...@gmail.com>> 于2019年6月19日周三 下午3:42写道： >>>>> > > > >> > > >> > >>>>> > > > >> > > >> > > Hi, >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > Thanks a lot for your great discussion and great >>>>> to see >>>>> > > that >>>>> > > > >> some >>>>> > > > >> > > >> > agreement >>>>> > > > >> > > >> > > has been reached on the "local aggregate engine"! >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > ===> Considering the abstract engine, >>>>> > > > >> > > >> > > I'm thinking is it valuable for us to extend the >>>>> current >>>>> > > > >> window to >>>>> > > > >> > > >> meet >>>>> > > > >> > > >> > > both demands raised by Kurt and Vino? There are >>>>> some >>>>> > > benefits >>>>> > > > >> we >>>>> > > > >> > can >>>>> > > > >> > > >> get: >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > 1. The interfaces of the window are complete and >>>>> clear. >>>>> > > With >>>>> > > > >> > > windows, >>>>> > > > >> > > >> we >>>>> > > > >> > > >> > > can define a lot of ways to split the data and >>>>> perform >>>>> > > > >> different >>>>> > > > >> > > >> > > computations. >>>>> > > > >> > > >> > > 2. We can also leverage the window to do miniBatch >>>>> for the >>>>> > > > >> global >>>>> > > > >> > > >> > > aggregation, i.e, we can use the window to bundle >>>>> data >>>>> > > belong >>>>> > > > >> to >>>>> > > > >> > the >>>>> > > > >> > > >> same >>>>> > > > >> > > >> > > key, for every bundle we only need to read and >>>>> write once >>>>> > > > >> state. >>>>> > > > >> > > This >>>>> > > > >> > > >> can >>>>> > > > >> > > >> > > greatly reduce state IO and improve performance. >>>>> > > > >> > > >> > > 3. A lot of other use cases can also benefit from >>>>> the >>>>> > > window >>>>> > > > >> base >>>>> > > > >> > on >>>>> > > > >> > > >> > memory >>>>> > > > >> > > >> > > or stateless. >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > ===> As for the API, >>>>> > > > >> > > >> > > I think it is good to make our API more flexible. >>>>> However, >>>>> > > we >>>>> > > > >> may >>>>> > > > >> > > >> need to >>>>> > > > >> > > >> > > make our API meaningful. >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > Take my previous reply as an example, >>>>> > > > >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The >>>>> result may >>>>> > > be >>>>> > > > >> > > >> > meaningless. >>>>> > > > >> > > >> > > Another example I find is the intervalJoin, e.g., >>>>> > > > >> > > >> > > >>>>> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In >>>>> > > > >> this >>>>> > > > >> > > >> case, it >>>>> > > > >> > > >> > > will bring problems if input1 and input2 share >>>>> different >>>>> > > > >> > > parallelism. >>>>> > > > >> > > >> We >>>>> > > > >> > > >> > > don't know which input should the join chained >>>>> with? Even >>>>> > > if >>>>> > > > >> they >>>>> > > > >> > > >> share >>>>> > > > >> > > >> > the >>>>> > > > >> > > >> > > same parallelism, it's hard to tell what the join >>>>> is doing. >>>>> > > > >> There >>>>> > > > >> > > are >>>>> > > > >> > > >> > maybe >>>>> > > > >> > > >> > > some other problems. >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > From this point of view, it's at least not good to >>>>> enable >>>>> > > all >>>>> > > > >> > > >> > > functionalities for localKeyBy from KeyedStream? >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > Great to also have your opinions. >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > Best, Hequn >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang < >>>>> > > > >> yanghua1...@gmail.com <mailto:yanghua1...@gmail.com> >>>>> > > > >> > > >>>>> > > > >> > > >> > wrote: >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > > Hi Kurt and Piotrek, >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > > Thanks for your comments. >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > > I agree that we can provide a better abstraction >>>>> to be >>>>> > > > >> > compatible >>>>> > > > >> > > >> with >>>>> > > > >> > > >> > > two >>>>> > > > >> > > >> > > > different implementations. >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > > First of all, I think we should consider what >>>>> kind of >>>>> > > > >> scenarios >>>>> > > > >> > we >>>>> > > > >> > > >> need >>>>> > > > >> > > >> > > to >>>>> > > > >> > > >> > > > support in *API* level? >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > > We have some use cases which need to a customized >>>>> > > > aggregation >>>>> > > > >> > > >> through >>>>> > > > >> > > >> > > > KeyedProcessFunction, (in the usage of our >>>>> > > > localKeyBy.window >>>>> > > > >> > they >>>>> > > > >> > > >> can >>>>> > > > >> > > >> > use >>>>> > > > >> > > >> > > > ProcessWindowFunction). >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > > Shall we support these flexible use scenarios? >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > > Best, >>>>> > > > >> > > >> > > > Vino >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > > Kurt Young <ykt...@gmail.com <mailto: >>>>> ykt...@gmail.com>> 于2019年6月18日周二 下午8:37写道： >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > > > Hi Piotr, >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > > Thanks for joining the discussion. Make “local >>>>> > > > aggregation" >>>>> > > > >> > > >> abstract >>>>> > > > >> > > >> > > > enough >>>>> > > > >> > > >> > > > > sounds good to me, we could >>>>> > > > >> > > >> > > > > implement and verify alternative solutions for >>>>> use >>>>> > > cases >>>>> > > > of >>>>> > > > >> > > local >>>>> > > > >> > > >> > > > > aggregation. Maybe we will find both solutions >>>>> > > > >> > > >> > > > > are appropriate for different scenarios. >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > > Starting from a simple one sounds a practical >>>>> way to >>>>> > > go. >>>>> > > > >> What >>>>> > > > >> > do >>>>> > > > >> > > >> you >>>>> > > > >> > > >> > > > think, >>>>> > > > >> > > >> > > > > vino? >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > > Best, >>>>> > > > >> > > >> > > > > Kurt >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski >>>>> < >>>>> > > > >> > > >> pi...@ververica.com <mailto:pi...@ververica.com>> >>>>> > > > >> > > >> > > > > wrote: >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > > > Hi Kurt and Vino, >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > > I think there is a trade of hat we need to >>>>> consider >>>>> > > for >>>>> > > > >> the >>>>> > > > >> > > >> local >>>>> > > > >> > > >> > > > > > aggregation. >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > > Generally speaking I would agree with Kurt >>>>> about >>>>> > > local >>>>> > > > >> > > >> > > aggregation/pre >>>>> > > > >> > > >> > > > > > aggregation not using Flink's state flush the >>>>> > > operator >>>>> > > > >> on a >>>>> > > > >> > > >> > > checkpoint. >>>>> > > > >> > > >> > > > > > Network IO is usually cheaper compared to >>>>> Disks IO. >>>>> > > > This >>>>> > > > >> has >>>>> > > > >> > > >> > however >>>>> > > > >> > > >> > > > > couple >>>>> > > > >> > > >> > > > > > of issues: >>>>> > > > >> > > >> > > > > > 1. It can explode number of in-flight >>>>> records during >>>>> > > > >> > > checkpoint >>>>> > > > >> > > >> > > barrier >>>>> > > > >> > > >> > > > > > alignment, making checkpointing slower and >>>>> decrease >>>>> > > the >>>>> > > > >> > actual >>>>> > > > >> > > >> > > > > throughput. >>>>> > > > >> > > >> > > > > > 2. This trades Disks IO on the local >>>>> aggregation >>>>> > > > machine >>>>> > > > >> > with >>>>> > > > >> > > >> CPU >>>>> > > > >> > > >> > > (and >>>>> > > > >> > > >> > > > > > Disks IO in case of RocksDB) on the final >>>>> aggregation >>>>> > > > >> > machine. >>>>> > > > >> > > >> This >>>>> > > > >> > > >> > > is >>>>> > > > >> > > >> > > > > > fine, as long there is no huge data skew. If >>>>> there is >>>>> > > > >> only a >>>>> > > > >> > > >> > handful >>>>> > > > >> > > >> > > > (or >>>>> > > > >> > > >> > > > > > even one single) hot keys, it might be >>>>> better to keep >>>>> > > > the >>>>> > > > >> > > >> > persistent >>>>> > > > >> > > >> > > > > state >>>>> > > > >> > > >> > > > > > in the LocalAggregationOperator to offload >>>>> final >>>>> > > > >> aggregation >>>>> > > > >> > > as >>>>> > > > >> > > >> > much >>>>> > > > >> > > >> > > as >>>>> > > > >> > > >> > > > > > possible. >>>>> > > > >> > > >> > > > > > 3. With frequent checkpointing local >>>>> aggregation >>>>> > > > >> > effectiveness >>>>> > > > >> > > >> > would >>>>> > > > >> > > >> > > > > > degrade. >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > > I assume Kurt is correct, that in your use >>>>> cases >>>>> > > > >> stateless >>>>> > > > >> > > >> operator >>>>> > > > >> > > >> > > was >>>>> > > > >> > > >> > > > > > behaving better, but I could easily see >>>>> other use >>>>> > > cases >>>>> > > > >> as >>>>> > > > >> > > well. >>>>> > > > >> > > >> > For >>>>> > > > >> > > >> > > > > > example someone is already using RocksDB, >>>>> and his job >>>>> > > > is >>>>> > > > >> > > >> > bottlenecked >>>>> > > > >> > > >> > > > on >>>>> > > > >> > > >> > > > > a >>>>> > > > >> > > >> > > > > > single window operator instance because of >>>>> the data >>>>> > > > >> skew. In >>>>> > > > >> > > >> that >>>>> > > > >> > > >> > > case >>>>> > > > >> > > >> > > > > > stateful local aggregation would be probably >>>>> a better >>>>> > > > >> > choice. >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > > Because of that, I think we should >>>>> eventually provide >>>>> > > > >> both >>>>> > > > >> > > >> versions >>>>> > > > >> > > >> > > and >>>>> > > > >> > > >> > > > > in >>>>> > > > >> > > >> > > > > > the initial version we should at least make >>>>> the >>>>> > > “local >>>>> > > > >> > > >> aggregation >>>>> > > > >> > > >> > > > > engine” >>>>> > > > >> > > >> > > > > > abstract enough, that one could easily >>>>> provide >>>>> > > > different >>>>> > > > >> > > >> > > implementation >>>>> > > > >> > > >> > > > > > strategy. >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > > Piotrek >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young < >>>>> > > > ykt...@gmail.com <mailto:ykt...@gmail.com> >>>>> > > > >> > >>>>> > > > >> > > >> wrote: >>>>> > > > >> > > >> > > > > > > >>>>> > > > >> > > >> > > > > > > Hi, >>>>> > > > >> > > >> > > > > > > >>>>> > > > >> > > >> > > > > > > For the trigger, it depends on what >>>>> operator we >>>>> > > want >>>>> > > > to >>>>> > > > >> > use >>>>> > > > >> > > >> under >>>>> > > > >> > > >> > > the >>>>> > > > >> > > >> > > > > > API. >>>>> > > > >> > > >> > > > > > > If we choose to use window operator, >>>>> > > > >> > > >> > > > > > > we should also use window's trigger. >>>>> However, I >>>>> > > also >>>>> > > > >> think >>>>> > > > >> > > >> reuse >>>>> > > > >> > > >> > > > window >>>>> > > > >> > > >> > > > > > > operator for this scenario may not be >>>>> > > > >> > > >> > > > > > > the best choice. The reasons are the >>>>> following: >>>>> > > > >> > > >> > > > > > > >>>>> > > > >> > > >> > > > > > > 1. As a lot of people already pointed out, >>>>> window >>>>> > > > >> relies >>>>> > > > >> > > >> heavily >>>>> > > > >> > > >> > on >>>>> > > > >> > > >> > > > > state >>>>> > > > >> > > >> > > > > > > and it will definitely effect performance. >>>>> You can >>>>> > > > >> > > >> > > > > > > argue that one can use heap based >>>>> statebackend, but >>>>> > > > >> this >>>>> > > > >> > > will >>>>> > > > >> > > >> > > > introduce >>>>> > > > >> > > >> > > > > > > extra coupling. Especially we have a >>>>> chance to >>>>> > > > >> > > >> > > > > > > design a pure stateless operator. >>>>> > > > >> > > >> > > > > > > 2. The window operator is *the most* >>>>> complicated >>>>> > > > >> operator >>>>> > > > >> > > >> Flink >>>>> > > > >> > > >> > > > > currently >>>>> > > > >> > > >> > > > > > > have. Maybe we only need to pick a subset >>>>> of >>>>> > > > >> > > >> > > > > > > window operator to achieve the goal, but >>>>> once the >>>>> > > > user >>>>> > > > >> > wants >>>>> > > > >> > > >> to >>>>> > > > >> > > >> > > have >>>>> > > > >> > > >> > > > a >>>>> > > > >> > > >> > > > > > deep >>>>> > > > >> > > >> > > > > > > look at the localAggregation operator, >>>>> it's still >>>>> > > > >> > > >> > > > > > > hard to find out what's going on under the >>>>> window >>>>> > > > >> > operator. >>>>> > > > >> > > >> For >>>>> > > > >> > > >> > > > > > simplicity, >>>>> > > > >> > > >> > > > > > > I would also recommend we introduce a >>>>> dedicated >>>>> > > > >> > > >> > > > > > > lightweight operator, which also much >>>>> easier for a >>>>> > > > >> user to >>>>> > > > >> > > >> learn >>>>> > > > >> > > >> > > and >>>>> > > > >> > > >> > > > > use. >>>>> > > > >> > > >> > > > > > > >>>>> > > > >> > > >> > > > > > > For your question about increasing the >>>>> burden in >>>>> > > > >> > > >> > > > > > > >>>>> `StreamOperator::prepareSnapshotPreBarrier()`, the >>>>> > > > only >>>>> > > > >> > > thing >>>>> > > > >> > > >> > this >>>>> > > > >> > > >> > > > > > function >>>>> > > > >> > > >> > > > > > > need >>>>> > > > >> > > >> > > > > > > to do is output all the partial results, >>>>> it's >>>>> > > purely >>>>> > > > >> cpu >>>>> > > > >> > > >> > workload, >>>>> > > > >> > > >> > > > not >>>>> > > > >> > > >> > > > > > > introducing any IO. I want to point out >>>>> that even >>>>> > > if >>>>> > > > we >>>>> > > > >> > have >>>>> > > > >> > > >> this >>>>> > > > >> > > >> > > > > > > cost, we reduced another barrier align >>>>> cost of the >>>>> > > > >> > operator, >>>>> > > > >> > > >> > which >>>>> > > > >> > > >> > > is >>>>> > > > >> > > >> > > > > the >>>>> > > > >> > > >> > > > > > > sync flush stage of the state, if you >>>>> introduced >>>>> > > > state. >>>>> > > > >> > This >>>>> > > > >> > > >> > > > > > > flush actually will introduce disk IO, and >>>>> I think >>>>> > > > it's >>>>> > > > >> > > >> worthy to >>>>> > > > >> > > >> > > > > > exchange >>>>> > > > >> > > >> > > > > > > this cost with purely CPU workload. And we >>>>> do have >>>>> > > > some >>>>> > > > >> > > >> > > > > > > observations about these two behavior (as >>>>> i said >>>>> > > > >> before, >>>>> > > > >> > we >>>>> > > > >> > > >> > > actually >>>>> > > > >> > > >> > > > > > > implemented both solutions), the stateless >>>>> one >>>>> > > > actually >>>>> > > > >> > > >> performs >>>>> > > > >> > > >> > > > > > > better both in performance and barrier >>>>> align time. >>>>> > > > >> > > >> > > > > > > >>>>> > > > >> > > >> > > > > > > Best, >>>>> > > > >> > > >> > > > > > > Kurt >>>>> > > > >> > > >> > > > > > > >>>>> > > > >> > > >> > > > > > > >>>>> > > > >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang < >>>>> > > > >> > > >> yanghua1...@gmail.com <mailto:yanghua1...@gmail.com> >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > > > > wrote: >>>>> > > > >> > > >> > > > > > > >>>>> > > > >> > > >> > > > > > >> Hi Kurt, >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> Thanks for your example. Now, it looks >>>>> more >>>>> > > clearly >>>>> > > > >> for >>>>> > > > >> > me. >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> From your example code snippet, I saw the >>>>> > > > >> localAggregate >>>>> > > > >> > > API >>>>> > > > >> > > >> has >>>>> > > > >> > > >> > > > three >>>>> > > > >> > > >> > > > > > >> parameters: >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> 1. key field >>>>> > > > >> > > >> > > > > > >> 2. PartitionAvg >>>>> > > > >> > > >> > > > > > >> 3. CountTrigger: Does this trigger >>>>> comes from >>>>> > > > window >>>>> > > > >> > > >> package? >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> I will compare our and your design from >>>>> API and >>>>> > > > >> operator >>>>> > > > >> > > >> level: >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> *From the API level:* >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> As I replied to @dianfu in the old email >>>>> > > thread,[1] >>>>> > > > >> the >>>>> > > > >> > > >> Window >>>>> > > > >> > > >> > API >>>>> > > > >> > > >> > > > can >>>>> > > > >> > > >> > > > > > >> provide the second and the third >>>>> parameter right >>>>> > > > now. >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> If you reuse specified interface or >>>>> class, such as >>>>> > > > >> > > *Trigger* >>>>> > > > >> > > >> or >>>>> > > > >> > > >> > > > > > >> *CounterTrigger* provided by window >>>>> package, but >>>>> > > do >>>>> > > > >> not >>>>> > > > >> > use >>>>> > > > >> > > >> > window >>>>> > > > >> > > >> > > > > API, >>>>> > > > >> > > >> > > > > > >> it's not reasonable. >>>>> > > > >> > > >> > > > > > >> And if you do not reuse these interface >>>>> or class, >>>>> > > > you >>>>> > > > >> > would >>>>> > > > >> > > >> need >>>>> > > > >> > > >> > > to >>>>> > > > >> > > >> > > > > > >> introduce more things however they are >>>>> looked >>>>> > > > similar >>>>> > > > >> to >>>>> > > > >> > > the >>>>> > > > >> > > >> > > things >>>>> > > > >> > > >> > > > > > >> provided by window package. >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> The window package has provided several >>>>> types of >>>>> > > the >>>>> > > > >> > window >>>>> > > > >> > > >> and >>>>> > > > >> > > >> > > many >>>>> > > > >> > > >> > > > > > >> triggers and let users customize it. >>>>> What's more, >>>>> > > > the >>>>> > > > >> > user >>>>> > > > >> > > is >>>>> > > > >> > > >> > more >>>>> > > > >> > > >> > > > > > familiar >>>>> > > > >> > > >> > > > > > >> with Window API. >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> This is the reason why we just provide >>>>> localKeyBy >>>>> > > > API >>>>> > > > >> and >>>>> > > > >> > > >> reuse >>>>> > > > >> > > >> > > the >>>>> > > > >> > > >> > > > > > window >>>>> > > > >> > > >> > > > > > >> API. It reduces unnecessary components >>>>> such as >>>>> > > > >> triggers >>>>> > > > >> > and >>>>> > > > >> > > >> the >>>>> > > > >> > > >> > > > > > mechanism >>>>> > > > >> > > >> > > > > > >> of buffer (based on count num or time). >>>>> > > > >> > > >> > > > > > >> And it has a clear and easy to understand >>>>> > > semantics. >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> *From the operator level:* >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> We reused window operator, so we can get >>>>> all the >>>>> > > > >> benefits >>>>> > > > >> > > >> from >>>>> > > > >> > > >> > > state >>>>> > > > >> > > >> > > > > and >>>>> > > > >> > > >> > > > > > >> checkpoint. >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> From your design, you named the operator >>>>> under >>>>> > > > >> > > localAggregate >>>>> > > > >> > > >> > API >>>>> > > > >> > > >> > > > is a >>>>> > > > >> > > >> > > > > > >> *stateless* operator. IMO, it is still a >>>>> state, it >>>>> > > > is >>>>> > > > >> > just >>>>> > > > >> > > >> not >>>>> > > > >> > > >> > > Flink >>>>> > > > >> > > >> > > > > > >> managed state. >>>>> > > > >> > > >> > > > > > >> About the memory buffer (I think it's >>>>> still not >>>>> > > very >>>>> > > > >> > clear, >>>>> > > > >> > > >> if >>>>> > > > >> > > >> > you >>>>> > > > >> > > >> > > > > have >>>>> > > > >> > > >> > > > > > >> time, can you give more detail >>>>> information or >>>>> > > answer >>>>> > > > >> my >>>>> > > > >> > > >> > > questions), >>>>> > > > >> > > >> > > > I >>>>> > > > >> > > >> > > > > > have >>>>> > > > >> > > >> > > > > > >> some questions: >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> - if it just a raw JVM heap memory >>>>> buffer, how >>>>> > > to >>>>> > > > >> > support >>>>> > > > >> > > >> > fault >>>>> > > > >> > > >> > > > > > >> tolerance, if the job is configured >>>>> EXACTLY-ONCE >>>>> > > > >> > semantic >>>>> > > > >> > > >> > > > guarantee? >>>>> > > > >> > > >> > > > > > >> - if you thought the memory >>>>> buffer(non-Flink >>>>> > > > state), >>>>> > > > >> > has >>>>> > > > >> > > >> > better >>>>> > > > >> > > >> > > > > > >> performance. In our design, users can >>>>> also >>>>> > > config >>>>> > > > >> HEAP >>>>> > > > >> > > >> state >>>>> > > > >> > > >> > > > backend >>>>> > > > >> > > >> > > > > > to >>>>> > > > >> > > >> > > > > > >> provide the performance close to your >>>>> mechanism. >>>>> > > > >> > > >> > > > > > >> - >>>>> `StreamOperator::prepareSnapshotPreBarrier()` >>>>> > > > >> related >>>>> > > > >> > > to >>>>> > > > >> > > >> the >>>>> > > > >> > > >> > > > > timing >>>>> > > > >> > > >> > > > > > of >>>>> > > > >> > > >> > > > > > >> snapshot. IMO, the flush action should >>>>> be a >>>>> > > > >> > synchronized >>>>> > > > >> > > >> > action? >>>>> > > > >> > > >> > > > (if >>>>> > > > >> > > >> > > > > > >> not, >>>>> > > > >> > > >> > > > > > >> please point out my mistake) I still >>>>> think we >>>>> > > > should >>>>> > > > >> > not >>>>> > > > >> > > >> > depend >>>>> > > > >> > > >> > > on >>>>> > > > >> > > >> > > > > the >>>>> > > > >> > > >> > > > > > >> timing of checkpoint. Checkpoint related >>>>> > > > operations >>>>> > > > >> are >>>>> > > > >> > > >> > inherent >>>>> > > > >> > > >> > > > > > >> performance sensitive, we should not >>>>> increase >>>>> > > its >>>>> > > > >> > burden >>>>> > > > >> > > >> > > anymore. >>>>> > > > >> > > >> > > > > Our >>>>> > > > >> > > >> > > > > > >> implementation based on the mechanism >>>>> of Flink's >>>>> > > > >> > > >> checkpoint, >>>>> > > > >> > > >> > > which >>>>> > > > >> > > >> > > > > can >>>>> > > > >> > > >> > > > > > >> benefit from the asnyc snapshot and >>>>> incremental >>>>> > > > >> > > checkpoint. >>>>> > > > >> > > >> > IMO, >>>>> > > > >> > > >> > > > the >>>>> > > > >> > > >> > > > > > >> performance is not a problem, and we >>>>> also do not >>>>> > > > >> find >>>>> > > > >> > the >>>>> > > > >> > > >> > > > > performance >>>>> > > > >> > > >> > > > > > >> issue >>>>> > > > >> > > >> > > > > > >> in our production. >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> [1]: >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > >>>>> > > > >> > > >> >>>>> > > > >> > > >>>>> > > > >> > >>>>> > > > >> >>>>> > > > >>>>> > > >>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308 >>>>> < >>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308 >>>>> > >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> Best, >>>>> > > > >> > > >> > > > > > >> Vino >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >> Kurt Young <ykt...@gmail.com <mailto: >>>>> ykt...@gmail.com>> 于2019年6月18日周二 >>>>> > > > 下午2:27写道： >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >>> Yeah, sorry for not expressing myself >>>>> clearly. I >>>>> > > > will >>>>> > > > >> > try >>>>> > > > >> > > to >>>>> > > > >> > > >> > > > provide >>>>> > > > >> > > >> > > > > > more >>>>> > > > >> > > >> > > > > > >>> details to make sure we are on the same >>>>> page. >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>> For DataStream API, it shouldn't be >>>>> optimized >>>>> > > > >> > > automatically. >>>>> > > > >> > > >> > You >>>>> > > > >> > > >> > > > have >>>>> > > > >> > > >> > > > > > to >>>>> > > > >> > > >> > > > > > >>> explicitly call API to do local >>>>> aggregation >>>>> > > > >> > > >> > > > > > >>> as well as the trigger policy of the >>>>> local >>>>> > > > >> aggregation. >>>>> > > > >> > > Take >>>>> > > > >> > > >> > > > average >>>>> > > > >> > > >> > > > > > for >>>>> > > > >> > > >> > > > > > >>> example, the user program may look like >>>>> this >>>>> > > (just >>>>> > > > a >>>>> > > > >> > > draft): >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>> assuming the input type is >>>>> > > DataStream<Tupl2<String, >>>>> > > > >> > Int>> >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>> ds.localAggregate( >>>>> > > > >> > > >> > > > > > >>> 0, >>>>> > > // >>>>> > > > >> The >>>>> > > > >> > > local >>>>> > > > >> > > >> > key, >>>>> > > > >> > > >> > > > > which >>>>> > > > >> > > >> > > > > > >> is >>>>> > > > >> > > >> > > > > > >>> the String from Tuple2 >>>>> > > > >> > > >> > > > > > >>> PartitionAvg(1), >>>>> // The >>>>> > > > >> partial >>>>> > > > >> > > >> > > aggregation >>>>> > > > >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>, >>>>> indicating >>>>> > > > sum >>>>> > > > >> and >>>>> > > > >> > > >> count >>>>> > > > >> > > >> > > > > > >>> CountTrigger.of(1000L) // >>>>> Trigger >>>>> > > policy, >>>>> > > > >> note >>>>> > > > >> > > >> this >>>>> > > > >> > > >> > > > should >>>>> > > > >> > > >> > > > > be >>>>> > > > >> > > >> > > > > > >>> best effort, and also be composited with >>>>> time >>>>> > > based >>>>> > > > >> or >>>>> > > > >> > > >> memory >>>>> > > > >> > > >> > > size >>>>> > > > >> > > >> > > > > > based >>>>> > > > >> > > >> > > > > > >>> trigger >>>>> > > > >> > > >> > > > > > >>> ) >>>>> // >>>>> > > > The >>>>> > > > >> > > return >>>>> > > > >> > > >> > type >>>>> > > > >> > > >> > > > is >>>>> > > > >> > > >> > > > > > >> local >>>>> > > > >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, >>>>> Int>> >>>>> > > > >> > > >> > > > > > >>> .keyBy(0) >>>>> // >>>>> > > Further >>>>> > > > >> > keyby >>>>> > > > >> > > it >>>>> > > > >> > > >> > with >>>>> > > > >> > > >> > > > > > >> required >>>>> > > > >> > > >> > > > > > >>> key >>>>> > > > >> > > >> > > > > > >>> .aggregate(1) // >>>>> This >>>>> > > will >>>>> > > > >> merge >>>>> > > > >> > > all >>>>> > > > >> > > >> > the >>>>> > > > >> > > >> > > > > > partial >>>>> > > > >> > > >> > > > > > >>> results and get the final average. >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>> (This is only a draft, only trying to >>>>> explain >>>>> > > what >>>>> > > > it >>>>> > > > >> > > looks >>>>> > > > >> > > >> > > like. ) >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>> The local aggregate operator can be >>>>> stateless, we >>>>> > > > can >>>>> > > > >> > > keep a >>>>> > > > >> > > >> > > memory >>>>> > > > >> > > >> > > > > > >> buffer >>>>> > > > >> > > >> > > > > > >>> or other efficient data structure to >>>>> improve the >>>>> > > > >> > aggregate >>>>> > > > >> > > >> > > > > performance. >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>> Let me know if you have any other >>>>> questions. >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>> Best, >>>>> > > > >> > > >> > > > > > >>> Kurt >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino >>>>> yang < >>>>> > > > >> > > >> > yanghua1...@gmail.com <mailto:yanghua1...@gmail.com> >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > > > > wrote: >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>>> Hi Kurt, >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> Thanks for your reply. >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> Actually, I am not against you to raise >>>>> your >>>>> > > > design. >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> From your description before, I just >>>>> can imagine >>>>> > > > >> your >>>>> > > > >> > > >> > high-level >>>>> > > > >> > > >> > > > > > >>>> implementation is about SQL and the >>>>> optimization >>>>> > > > is >>>>> > > > >> > inner >>>>> > > > >> > > >> of >>>>> > > > >> > > >> > the >>>>> > > > >> > > >> > > > > API. >>>>> > > > >> > > >> > > > > > >> Is >>>>> > > > >> > > >> > > > > > >>> it >>>>> > > > >> > > >> > > > > > >>>> automatically? how to give the >>>>> configuration >>>>> > > > option >>>>> > > > >> > about >>>>> > > > >> > > >> > > trigger >>>>> > > > >> > > >> > > > > > >>>> pre-aggregation? >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> Maybe after I get more information, it >>>>> sounds >>>>> > > more >>>>> > > > >> > > >> reasonable. >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> IMO, first of all, it would be better >>>>> to make >>>>> > > your >>>>> > > > >> user >>>>> > > > >> > > >> > > interface >>>>> > > > >> > > >> > > > > > >>> concrete, >>>>> > > > >> > > >> > > > > > >>>> it's the basis of the discussion. >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> For example, can you give an example >>>>> code >>>>> > > snippet >>>>> > > > to >>>>> > > > >> > > >> introduce >>>>> > > > >> > > >> > > how >>>>> > > > >> > > >> > > > > to >>>>> > > > >> > > >> > > > > > >>> help >>>>> > > > >> > > >> > > > > > >>>> users to process data skew caused by >>>>> the jobs >>>>> > > > which >>>>> > > > >> > built >>>>> > > > >> > > >> with >>>>> > > > >> > > >> > > > > > >> DataStream >>>>> > > > >> > > >> > > > > > >>>> API? >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> If you give more details we can discuss >>>>> further >>>>> > > > >> more. I >>>>> > > > >> > > >> think >>>>> > > > >> > > >> > if >>>>> > > > >> > > >> > > > one >>>>> > > > >> > > >> > > > > > >>> design >>>>> > > > >> > > >> > > > > > >>>> introduces an exact interface and >>>>> another does >>>>> > > > not. >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> The implementation has an obvious >>>>> difference. >>>>> > > For >>>>> > > > >> > > example, >>>>> > > > >> > > >> we >>>>> > > > >> > > >> > > > > > introduce >>>>> > > > >> > > >> > > > > > >>> an >>>>> > > > >> > > >> > > > > > >>>> exact API in DataStream named >>>>> localKeyBy, about >>>>> > > > the >>>>> > > > >> > > >> > > > pre-aggregation >>>>> > > > >> > > >> > > > > we >>>>> > > > >> > > >> > > > > > >>> need >>>>> > > > >> > > >> > > > > > >>>> to define the trigger mechanism of local >>>>> > > > >> aggregation, >>>>> > > > >> > so >>>>> > > > >> > > we >>>>> > > > >> > > >> > find >>>>> > > > >> > > >> > > > > > reused >>>>> > > > >> > > >> > > > > > >>>> window API and operator is a good >>>>> choice. This >>>>> > > is >>>>> > > > a >>>>> > > > >> > > >> reasoning >>>>> > > > >> > > >> > > link >>>>> > > > >> > > >> > > > > > from >>>>> > > > >> > > >> > > > > > >>>> design to implementation. >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> What do you think? >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> Best, >>>>> > > > >> > > >> > > > > > >>>> Vino >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>> Kurt Young <ykt...@gmail.com <mailto: >>>>> ykt...@gmail.com>> 于2019年6月18日周二 >>>>> > > > >> 上午11:58写道： >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>>>> Hi Vino, >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>>> Now I feel that we may have different >>>>> > > > >> understandings >>>>> > > > >> > > about >>>>> > > > >> > > >> > what >>>>> > > > >> > > >> > > > > kind >>>>> > > > >> > > >> > > > > > >> of >>>>> > > > >> > > >> > > > > > >>>>> problems or improvements you want to >>>>> > > > >> > > >> > > > > > >>>>> resolve. Currently, most of the >>>>> feedback are >>>>> > > > >> focusing >>>>> > > > >> > on >>>>> > > > >> > > >> *how >>>>> > > > >> > > >> > > to >>>>> > > > >> > > >> > > > > do a >>>>> > > > >> > > >> > > > > > >>>>> proper local aggregation to improve >>>>> performance >>>>> > > > >> > > >> > > > > > >>>>> and maybe solving the data skew >>>>> issue*. And my >>>>> > > > gut >>>>> > > > >> > > >> feeling is >>>>> > > > >> > > >> > > > this >>>>> > > > >> > > >> > > > > is >>>>> > > > >> > > >> > > > > > >>>>> exactly what users want at the first >>>>> place, >>>>> > > > >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to >>>>> > > summarize >>>>> > > > >> here, >>>>> > > > >> > > >> please >>>>> > > > >> > > >> > > > > correct >>>>> > > > >> > > >> > > > > > >>> me >>>>> > > > >> > > >> > > > > > >>>> if >>>>> > > > >> > > >> > > > > > >>>>> i'm wrong). >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>>> But I still think the design is somehow >>>>> > > diverged >>>>> > > > >> from >>>>> > > > >> > > the >>>>> > > > >> > > >> > goal. >>>>> > > > >> > > >> > > > If >>>>> > > > >> > > >> > > > > we >>>>> > > > >> > > >> > > > > > >>>> want >>>>> > > > >> > > >> > > > > > >>>>> to have an efficient and powerful way >>>>> to >>>>> > > > >> > > >> > > > > > >>>>> have local aggregation, supporting >>>>> intermedia >>>>> > > > >> result >>>>> > > > >> > > type >>>>> > > > >> > > >> is >>>>> > > > >> > > >> > > > > > >> essential >>>>> > > > >> > > >> > > > > > >>>> IMO. >>>>> > > > >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and >>>>> > > > >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` >>>>> have a >>>>> > > > proper >>>>> > > > >> > > >> support of >>>>> > > > >> > > >> > > > > > >>>> intermediate >>>>> > > > >> > > >> > > > > > >>>>> result type and can do `merge` >>>>> operation >>>>> > > > >> > > >> > > > > > >>>>> on them. >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>>> Now, we have a lightweight >>>>> alternatives which >>>>> > > > >> performs >>>>> > > > >> > > >> well, >>>>> > > > >> > > >> > > and >>>>> > > > >> > > >> > > > > > >> have a >>>>> > > > >> > > >> > > > > > >>>>> nice fit with the local aggregate >>>>> requirements. >>>>> > > > >> > > >> > > > > > >>>>> Mostly importantly, it's much less >>>>> complex >>>>> > > > because >>>>> > > > >> > it's >>>>> > > > >> > > >> > > > stateless. >>>>> > > > >> > > >> > > > > > >> And >>>>> > > > >> > > >> > > > > > >>>> it >>>>> > > > >> > > >> > > > > > >>>>> can also achieve the similar >>>>> > > multiple-aggregation >>>>> > > > >> > > >> > > > > > >>>>> scenario. >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>>> I still not convinced why we shouldn't >>>>> consider >>>>> > > > it >>>>> > > > >> as >>>>> > > > >> > a >>>>> > > > >> > > >> first >>>>> > > > >> > > >> > > > step. >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>>> Best, >>>>> > > > >> > > >> > > > > > >>>>> Kurt >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino >>>>> yang < >>>>> > > > >> > > >> > > > yanghua1...@gmail.com <mailto: >>>>> yanghua1...@gmail.com>> >>>>> > > > >> > > >> > > > > > >>>> wrote: >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>>>> Hi Kurt, >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> Thanks for your comments. >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> It seems we both implemented local >>>>> aggregation >>>>> > > > >> > feature >>>>> > > > >> > > to >>>>> > > > >> > > >> > > > optimize >>>>> > > > >> > > >> > > > > > >>> the >>>>> > > > >> > > >> > > > > > >>>>>> issue of data skew. >>>>> > > > >> > > >> > > > > > >>>>>> However, IMHO, the API level of >>>>> optimizing >>>>> > > > >> revenue is >>>>> > > > >> > > >> > > different. >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> *Your optimization benefits from >>>>> Flink SQL and >>>>> > > > >> it's >>>>> > > > >> > not >>>>> > > > >> > > >> > user's >>>>> > > > >> > > >> > > > > > >>>> faces.(If >>>>> > > > >> > > >> > > > > > >>>>> I >>>>> > > > >> > > >> > > > > > >>>>>> understand it incorrectly, please >>>>> correct >>>>> > > > this.)* >>>>> > > > >> > > >> > > > > > >>>>>> *Our implementation employs it as an >>>>> > > > optimization >>>>> > > > >> > tool >>>>> > > > >> > > >> API >>>>> > > > >> > > >> > for >>>>> > > > >> > > >> > > > > > >>>>> DataStream, >>>>> > > > >> > > >> > > > > > >>>>>> it just like a local version of the >>>>> keyBy >>>>> > > API.* >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> Based on this, I want to say support >>>>> it as a >>>>> > > > >> > DataStream >>>>> > > > >> > > >> API >>>>> > > > >> > > >> > > can >>>>> > > > >> > > >> > > > > > >>> provide >>>>> > > > >> > > >> > > > > > >>>>>> these advantages: >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> - The localKeyBy API has a clear >>>>> semantic >>>>> > > and >>>>> > > > >> it's >>>>> > > > >> > > >> > flexible >>>>> > > > >> > > >> > > > not >>>>> > > > >> > > >> > > > > > >>> only >>>>> > > > >> > > >> > > > > > >>>>> for >>>>> > > > >> > > >> > > > > > >>>>>> processing data skew but also for >>>>> > > implementing >>>>> > > > >> some >>>>> > > > >> > > >> user >>>>> > > > >> > > >> > > > cases, >>>>> > > > >> > > >> > > > > > >>> for >>>>> > > > >> > > >> > > > > > >>>>>> example, if we want to calculate the >>>>> > > > >> multiple-level >>>>> > > > >> > > >> > > > aggregation, >>>>> > > > >> > > >> > > > > > >>> we >>>>> > > > >> > > >> > > > > > >>>>> can >>>>> > > > >> > > >> > > > > > >>>>>> do >>>>> > > > >> > > >> > > > > > >>>>>> multiple-level aggregation in the >>>>> local >>>>> > > > >> > aggregation: >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > input.localKeyBy("a").sum(1).localKeyBy("b").window(); >>>>> > > > >> > > >> // >>>>> > > > >> > > >> > > here >>>>> > > > >> > > >> > > > > > >> "a" >>>>> > > > >> > > >> > > > > > >>>> is >>>>> > > > >> > > >> > > > > > >>>>> a >>>>> > > > >> > > >> > > > > > >>>>>> sub-category, while "b" is a >>>>> category, here >>>>> > > we >>>>> > > > >> do >>>>> > > > >> > not >>>>> > > > >> > > >> need >>>>> > > > >> > > >> > > to >>>>> > > > >> > > >> > > > > > >>>> shuffle >>>>> > > > >> > > >> > > > > > >>>>>> data >>>>> > > > >> > > >> > > > > > >>>>>> in the network. >>>>> > > > >> > > >> > > > > > >>>>>> - The users of DataStream API will >>>>> benefit >>>>> > > > from >>>>> > > > >> > this. >>>>> > > > >> > > >> > > > Actually, >>>>> > > > >> > > >> > > > > > >> we >>>>> > > > >> > > >> > > > > > >>>>> have >>>>> > > > >> > > >> > > > > > >>>>>> a lot of scenes need to use >>>>> DataStream API. >>>>> > > > >> > > Currently, >>>>> > > > >> > > >> > > > > > >> DataStream >>>>> > > > >> > > >> > > > > > >>>> API >>>>> > > > >> > > >> > > > > > >>>>> is >>>>> > > > >> > > >> > > > > > >>>>>> the cornerstone of the physical >>>>> plan of >>>>> > > Flink >>>>> > > > >> SQL. >>>>> > > > >> > > >> With a >>>>> > > > >> > > >> > > > > > >>> localKeyBy >>>>> > > > >> > > >> > > > > > >>>>>> API, >>>>> > > > >> > > >> > > > > > >>>>>> the optimization of SQL at least >>>>> may use >>>>> > > this >>>>> > > > >> > > optimized >>>>> > > > >> > > >> > API, >>>>> > > > >> > > >> > > > > > >> this >>>>> > > > >> > > >> > > > > > >>>> is a >>>>> > > > >> > > >> > > > > > >>>>>> further topic. >>>>> > > > >> > > >> > > > > > >>>>>> - Based on the window operator, our >>>>> state >>>>> > > > would >>>>> > > > >> > > benefit >>>>> > > > >> > > >> > from >>>>> > > > >> > > >> > > > > > >> Flink >>>>> > > > >> > > >> > > > > > >>>>> State >>>>> > > > >> > > >> > > > > > >>>>>> and checkpoint, we do not need to >>>>> worry >>>>> > > about >>>>> > > > >> OOM >>>>> > > > >> > and >>>>> > > > >> > > >> job >>>>> > > > >> > > >> > > > > > >> failed. >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> Now, about your questions: >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> 1. About our design cannot change the >>>>> data >>>>> > > type >>>>> > > > >> and >>>>> > > > >> > > about >>>>> > > > >> > > >> > the >>>>> > > > >> > > >> > > > > > >>>>>> implementation of average: >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the >>>>> localKeyBy is >>>>> > > > an >>>>> > > > >> API >>>>> > > > >> > > >> > provides >>>>> > > > >> > > >> > > > to >>>>> > > > >> > > >> > > > > > >> the >>>>> > > > >> > > >> > > > > > >>>>> users >>>>> > > > >> > > >> > > > > > >>>>>> who use DataStream API to build their >>>>> jobs. >>>>> > > > >> > > >> > > > > > >>>>>> Users should know its semantics and >>>>> the >>>>> > > > difference >>>>> > > > >> > with >>>>> > > > >> > > >> > keyBy >>>>> > > > >> > > >> > > > API, >>>>> > > > >> > > >> > > > > > >> so >>>>> > > > >> > > >> > > > > > >>>> if >>>>> > > > >> > > >> > > > > > >>>>>> they want to the average aggregation, >>>>> they >>>>> > > > should >>>>> > > > >> > carry >>>>> > > > >> > > >> > local >>>>> > > > >> > > >> > > > sum >>>>> > > > >> > > >> > > > > > >>>> result >>>>> > > > >> > > >> > > > > > >>>>>> and local count result. >>>>> > > > >> > > >> > > > > > >>>>>> I admit that it will be convenient to >>>>> use >>>>> > > keyBy >>>>> > > > >> > > directly. >>>>> > > > >> > > >> > But >>>>> > > > >> > > >> > > we >>>>> > > > >> > > >> > > > > > >> need >>>>> > > > >> > > >> > > > > > >>>> to >>>>> > > > >> > > >> > > > > > >>>>>> pay a little price when we get some >>>>> benefits. >>>>> > > I >>>>> > > > >> think >>>>> > > > >> > > >> this >>>>> > > > >> > > >> > > price >>>>> > > > >> > > >> > > > > is >>>>> > > > >> > > >> > > > > > >>>>>> reasonable. Considering that the >>>>> DataStream >>>>> > > API >>>>> > > > >> > itself >>>>> > > > >> > > >> is a >>>>> > > > >> > > >> > > > > > >> low-level >>>>> > > > >> > > >> > > > > > >>>> API >>>>> > > > >> > > >> > > > > > >>>>>> (at least for now). >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> 2. About stateless operator and >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> `StreamOperator::prepareSnapshotPreBarrier()`: >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> Actually, I have discussed this >>>>> opinion with >>>>> > > > >> @dianfu >>>>> > > > >> > in >>>>> > > > >> > > >> the >>>>> > > > >> > > >> > > old >>>>> > > > >> > > >> > > > > > >> mail >>>>> > > > >> > > >> > > > > > >>>>>> thread. I will copy my opinion from >>>>> there: >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> - for your design, you still need >>>>> somewhere >>>>> > > to >>>>> > > > >> give >>>>> > > > >> > > the >>>>> > > > >> > > >> > > users >>>>> > > > >> > > >> > > > > > >>>>> configure >>>>> > > > >> > > >> > > > > > >>>>>> the trigger threshold (maybe memory >>>>> > > > >> availability?), >>>>> > > > >> > > >> this >>>>> > > > >> > > >> > > > design >>>>> > > > >> > > >> > > > > > >>>> cannot >>>>> > > > >> > > >> > > > > > >>>>>> guarantee a deterministic semantics >>>>> (it will >>>>> > > > >> bring >>>>> > > > >> > > >> trouble >>>>> > > > >> > > >> > > for >>>>> > > > >> > > >> > > > > > >>>> testing >>>>> > > > >> > > >> > > > > > >>>>>> and >>>>> > > > >> > > >> > > > > > >>>>>> debugging). >>>>> > > > >> > > >> > > > > > >>>>>> - if the implementation depends on >>>>> the >>>>> > > timing >>>>> > > > of >>>>> > > > >> > > >> > checkpoint, >>>>> > > > >> > > >> > > > it >>>>> > > > >> > > >> > > > > > >>>> would >>>>> > > > >> > > >> > > > > > >>>>>> affect the checkpoint's progress, >>>>> and the >>>>> > > > >> buffered >>>>> > > > >> > > data >>>>> > > > >> > > >> > may >>>>> > > > >> > > >> > > > > > >> cause >>>>> > > > >> > > >> > > > > > >>>> OOM >>>>> > > > >> > > >> > > > > > >>>>>> issue. In addition, if the operator >>>>> is >>>>> > > > >> stateless, >>>>> > > > >> > it >>>>> > > > >> > > >> can >>>>> > > > >> > > >> > not >>>>> > > > >> > > >> > > > > > >>> provide >>>>> > > > >> > > >> > > > > > >>>>>> fault >>>>> > > > >> > > >> > > > > > >>>>>> tolerance. >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> Best, >>>>> > > > >> > > >> > > > > > >>>>>> Vino >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> Kurt Young <ykt...@gmail.com <mailto: >>>>> ykt...@gmail.com>> 于2019年6月18日周二 >>>>> > > > >> > 上午9:22写道： >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> Hi Vino, >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the >>>>> general >>>>> > > > idea >>>>> > > > >> and >>>>> > > > >> > > IMO >>>>> > > > >> > > >> > it's >>>>> > > > >> > > >> > > > > > >> very >>>>> > > > >> > > >> > > > > > >>>>> useful >>>>> > > > >> > > >> > > > > > >>>>>>> feature. >>>>> > > > >> > > >> > > > > > >>>>>>> But after reading through the >>>>> document, I >>>>> > > feel >>>>> > > > >> that >>>>> > > > >> > we >>>>> > > > >> > > >> may >>>>> > > > >> > > >> > > over >>>>> > > > >> > > >> > > > > > >>>> design >>>>> > > > >> > > >> > > > > > >>>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>> required >>>>> > > > >> > > >> > > > > > >>>>>>> operator for proper local >>>>> aggregation. The >>>>> > > main >>>>> > > > >> > reason >>>>> > > > >> > > >> is >>>>> > > > >> > > >> > we >>>>> > > > >> > > >> > > > want >>>>> > > > >> > > >> > > > > > >>> to >>>>> > > > >> > > >> > > > > > >>>>>> have a >>>>> > > > >> > > >> > > > > > >>>>>>> clear definition and behavior about >>>>> the >>>>> > > "local >>>>> > > > >> keyed >>>>> > > > >> > > >> state" >>>>> > > > >> > > >> > > > which >>>>> > > > >> > > >> > > > > > >>> in >>>>> > > > >> > > >> > > > > > >>>> my >>>>> > > > >> > > >> > > > > > >>>>>>> opinion is not >>>>> > > > >> > > >> > > > > > >>>>>>> necessary for local aggregation, at >>>>> least for >>>>> > > > >> start. >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> Another issue I noticed is the local >>>>> key by >>>>> > > > >> operator >>>>> > > > >> > > >> cannot >>>>> > > > >> > > >> > > > > > >> change >>>>> > > > >> > > >> > > > > > >>>>>> element >>>>> > > > >> > > >> > > > > > >>>>>>> type, it will >>>>> > > > >> > > >> > > > > > >>>>>>> also restrict a lot of use cases >>>>> which can be >>>>> > > > >> > benefit >>>>> > > > >> > > >> from >>>>> > > > >> > > >> > > > local >>>>> > > > >> > > >> > > > > > >>>>>>> aggregation, like "average". >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and >>>>> the only >>>>> > > > >> thing >>>>> > > > >> > > >> need to >>>>> > > > >> > > >> > > be >>>>> > > > >> > > >> > > > > > >> done >>>>> > > > >> > > >> > > > > > >>>> is >>>>> > > > >> > > >> > > > > > >>>>>>> introduce >>>>> > > > >> > > >> > > > > > >>>>>>> a stateless lightweight operator >>>>> which is >>>>> > > > >> *chained* >>>>> > > > >> > > >> before >>>>> > > > >> > > >> > > > > > >>> `keyby()`. >>>>> > > > >> > > >> > > > > > >>>>> The >>>>> > > > >> > > >> > > > > > >>>>>>> operator will flush all buffered >>>>> > > > >> > > >> > > > > > >>>>>>> elements during >>>>> > > > >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()` >>>>> > > > >> > > >> > > > and >>>>> > > > >> > > >> > > > > > >>>> make >>>>> > > > >> > > >> > > > > > >>>>>>> himself stateless. >>>>> > > > >> > > >> > > > > > >>>>>>> By the way, in the earlier version >>>>> we also >>>>> > > did >>>>> > > > >> the >>>>> > > > >> > > >> similar >>>>> > > > >> > > >> > > > > > >> approach >>>>> > > > >> > > >> > > > > > >>>> by >>>>> > > > >> > > >> > > > > > >>>>>>> introducing a stateful >>>>> > > > >> > > >> > > > > > >>>>>>> local aggregation operator but it's >>>>> not >>>>> > > > >> performed as >>>>> > > > >> > > >> well >>>>> > > > >> > > >> > as >>>>> > > > >> > > >> > > > the >>>>> > > > >> > > >> > > > > > >>>> later >>>>> > > > >> > > >> > > > > > >>>>>> one, >>>>> > > > >> > > >> > > > > > >>>>>>> and also effect the barrie >>>>> > > > >> > > >> > > > > > >>>>>>> alignment time. The later one is >>>>> fairly >>>>> > > simple >>>>> > > > >> and >>>>> > > > >> > > more >>>>> > > > >> > > >> > > > > > >> efficient. >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> I would highly suggest you to >>>>> consider to >>>>> > > have >>>>> > > > a >>>>> > > > >> > > >> stateless >>>>> > > > >> > > >> > > > > > >> approach >>>>> > > > >> > > >> > > > > > >>>> at >>>>> > > > >> > > >> > > > > > >>>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>> first step. >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> Best, >>>>> > > > >> > > >> > > > > > >>>>>>> Kurt >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark >>>>> Wu < >>>>> > > > >> > > >> imj...@gmail.com <mailto:imj...@gmail.com>> >>>>> > > > >> > > >> > > > > > >> wrote: >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> Hi Vino, >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> Thanks for the proposal. >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> Regarding to the >>>>> "input.keyBy(0).sum(1)" vs >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > >>>>> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)", >>>>> > > > >> > > >> > > > > > >> have >>>>> > > > >> > > >> > > > > > >>>> you >>>>> > > > >> > > >> > > > > > >>>>>>> done >>>>> > > > >> > > >> > > > > > >>>>>>>> some benchmark? >>>>> > > > >> > > >> > > > > > >>>>>>>> Because I'm curious about how much >>>>> > > performance >>>>> > > > >> > > >> improvement >>>>> > > > >> > > >> > > can >>>>> > > > >> > > >> > > > > > >> we >>>>> > > > >> > > >> > > > > > >>>> get >>>>> > > > >> > > >> > > > > > >>>>>> by >>>>> > > > >> > > >> > > > > > >>>>>>>> using count window as the local >>>>> operator. >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> Best, >>>>> > > > >> > > >> > > > > > >>>>>>>> Jark >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino >>>>> yang < >>>>> > > > >> > > >> > > > yanghua1...@gmail.com <mailto: >>>>> yanghua1...@gmail.com> >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >>>>> wrote: >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> Hi Hequn, >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> Thanks for your reply. >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to >>>>> > > provide a >>>>> > > > >> tool >>>>> > > > >> > > >> which >>>>> > > > >> > > >> > > can >>>>> > > > >> > > >> > > > > > >>> let >>>>> > > > >> > > >> > > > > > >>>>>> users >>>>> > > > >> > > >> > > > > > >>>>>>> do >>>>> > > > >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The >>>>> behavior >>>>> > > of >>>>> > > > >> the >>>>> > > > >> > > >> > > > > > >>> pre-aggregation >>>>> > > > >> > > >> > > > > > >>>>> is >>>>> > > > >> > > >> > > > > > >>>>>>>>> similar to keyBy API. >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> So the three cases are different, >>>>> I will >>>>> > > > >> describe >>>>> > > > >> > > them >>>>> > > > >> > > >> > one >>>>> > > > >> > > >> > > by >>>>> > > > >> > > >> > > > > > >>>> one: >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1) >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the result is >>>>> event-driven, >>>>> > > > each >>>>> > > > >> > > event >>>>> > > > >> > > >> can >>>>> > > > >> > > >> > > > > > >>> produce >>>>> > > > >> > > >> > > > > > >>>>> one >>>>> > > > >> > > >> > > > > > >>>>>>> sum >>>>> > > > >> > > >> > > > > > >>>>>>>>> aggregation result and it is the >>>>> latest one >>>>> > > > >> from >>>>> > > > >> > the >>>>> > > > >> > > >> > source >>>>> > > > >> > > >> > > > > > >>>> start.* >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> 2. >>>>> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1) >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may >>>>> have a >>>>> > > > >> problem, it >>>>> > > > >> > > >> would >>>>> > > > >> > > >> > do >>>>> > > > >> > > >> > > > > > >> the >>>>> > > > >> > > >> > > > > > >>>>> local >>>>> > > > >> > > >> > > > > > >>>>>>> sum >>>>> > > > >> > > >> > > > > > >>>>>>>>> aggregation and will produce the >>>>> latest >>>>> > > > partial >>>>> > > > >> > > result >>>>> > > > >> > > >> > from >>>>> > > > >> > > >> > > > > > >> the >>>>> > > > >> > > >> > > > > > >>>>>> source >>>>> > > > >> > > >> > > > > > >>>>>>>>> start for every event. * >>>>> > > > >> > > >> > > > > > >>>>>>>>> *These latest partial results from >>>>> the same >>>>> > > > key >>>>> > > > >> > are >>>>> > > > >> > > >> > hashed >>>>> > > > >> > > >> > > to >>>>> > > > >> > > >> > > > > > >>> one >>>>> > > > >> > > >> > > > > > >>>>>> node >>>>> > > > >> > > >> > > > > > >>>>>>> to >>>>> > > > >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.* >>>>> > > > >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it >>>>> > > received >>>>> > > > >> > > multiple >>>>> > > > >> > > >> > > partial >>>>> > > > >> > > >> > > > > > >>>>> results >>>>> > > > >> > > >> > > > > > >>>>>>>> (they >>>>> > > > >> > > >> > > > > > >>>>>>>>> are all calculated from the source >>>>> start) >>>>> > > and >>>>> > > > >> sum >>>>> > > > >> > > them >>>>> > > > >> > > >> > will >>>>> > > > >> > > >> > > > > > >> get >>>>> > > > >> > > >> > > > > > >>>> the >>>>> > > > >> > > >> > > > > > >>>>>>> wrong >>>>> > > > >> > > >> > > > > > >>>>>>>>> result.* >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> 3. >>>>> > > > >> > > >> > > >>>>> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1) >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a >>>>> partial >>>>> > > > >> > > aggregation >>>>> > > > >> > > >> > > result >>>>> > > > >> > > >> > > > > > >>> for >>>>> > > > >> > > >> > > > > > >>>>>> the 5 >>>>> > > > >> > > >> > > > > > >>>>>>>>> records in the count window. The >>>>> partial >>>>> > > > >> > aggregation >>>>> > > > >> > > >> > > results >>>>> > > > >> > > >> > > > > > >>> from >>>>> > > > >> > > >> > > > > > >>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>>> same >>>>> > > > >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.* >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> So the first case and the third >>>>> case can >>>>> > > get >>>>> > > > >> the >>>>> > > > >> > > >> *same* >>>>> > > > >> > > >> > > > > > >> result, >>>>> > > > >> > > >> > > > > > >>>> the >>>>> > > > >> > > >> > > > > > >>>>>>>>> difference is the output-style and >>>>> the >>>>> > > > latency. >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key >>>>> API is >>>>> > > just >>>>> > > > >> an >>>>> > > > >> > > >> > > optimization >>>>> > > > >> > > >> > > > > > >>>> API. >>>>> > > > >> > > >> > > > > > >>>>> We >>>>> > > > >> > > >> > > > > > >>>>>>> do >>>>> > > > >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but >>>>> the user >>>>> > > has >>>>> > > > to >>>>> > > > >> > > >> > understand >>>>> > > > >> > > >> > > > > > >> its >>>>> > > > >> > > >> > > > > > >>>>>>> semantics >>>>> > > > >> > > >> > > > > > >>>>>>>>> and use it correctly. >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> Best, >>>>> > > > >> > > >> > > > > > >>>>>>>>> Vino >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> Hequn Cheng <chenghe...@gmail.com >>>>> <mailto:chenghe...@gmail.com>> >>>>> > > > >> 于2019年6月17日周一 >>>>> > > > >> > > >> > 下午4:18写道： >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> Hi Vino, >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think >>>>> it is a >>>>> > > > very >>>>> > > > >> > good >>>>> > > > >> > > >> > > feature! >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is >>>>> the >>>>> > > > semantics >>>>> > > > >> > for >>>>> > > > >> > > >> the >>>>> > > > >> > > >> > > > > > >>>>>> `localKeyBy`. >>>>> > > > >> > > >> > > > > > >>>>>>>> From >>>>> > > > >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` >>>>> API returns >>>>> > > > an >>>>> > > > >> > > >> instance >>>>> > > > >> > > >> > of >>>>> > > > >> > > >> > > > > > >>>>>>> `KeyedStream` >>>>> > > > >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so >>>>> in this >>>>> > > > case, >>>>> > > > >> > > what's >>>>> > > > >> > > >> > the >>>>> > > > >> > > >> > > > > > >>>>> semantics >>>>> > > > >> > > >> > > > > > >>>>>>> for >>>>> > > > >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will >>>>> the >>>>> > > > >> following >>>>> > > > >> > > code >>>>> > > > >> > > >> > share >>>>> > > > >> > > >> > > > > > >>> the >>>>> > > > >> > > >> > > > > > >>>>> same >>>>> > > > >> > > >> > > > > > >>>>>>>>> result? >>>>> > > > >> > > >> > > > > > >>>>>>>>>> and what're the differences >>>>> between them? >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1) >>>>> > > > >> > > >> > > > > > >>>>>>>>>> 2. >>>>> > > > input.localKeyBy(0).sum(1).keyBy(0).sum(1) >>>>> > > > >> > > >> > > > > > >>>>>>>>>> 3. >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1) >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add >>>>> this >>>>> > > into >>>>> > > > >> the >>>>> > > > >> > > >> > document. >>>>> > > > >> > > >> > > > > > >>> Thank >>>>> > > > >> > > >> > > > > > >>>>> you >>>>> > > > >> > > >> > > > > > >>>>>>>> very >>>>> > > > >> > > >> > > > > > >>>>>>>>>> much. >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> Best, Hequn >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM >>>>> vino >>>>> > > yang < >>>>> > > > >> > > >> > > > > > >>>>> yanghua1...@gmail.com <mailto: >>>>> yanghua1...@gmail.com>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> wrote: >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha, >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*" >>>>> section >>>>> > > of >>>>> > > > >> FLIP >>>>> > > > >> > > >> wiki >>>>> > > > >> > > >> > > > > > >>>> page.[1] >>>>> > > > >> > > >> > > > > > >>>>>> This >>>>> > > > >> > > >> > > > > > >>>>>>>>> mail >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has >>>>> proceeded to >>>>> > > > the >>>>> > > > >> > > third >>>>> > > > >> > > >> > step. >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth >>>>> step(vote >>>>> > > > step), >>>>> > > > >> I >>>>> > > > >> > > >> didn't >>>>> > > > >> > > >> > > > > > >> find >>>>> > > > >> > > >> > > > > > >>>> the >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the >>>>> voting >>>>> > > > >> process. >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion >>>>> of this >>>>> > > > >> feature >>>>> > > > >> > > has >>>>> > > > >> > > >> > been >>>>> > > > >> > > >> > > > > > >>> done >>>>> > > > >> > > >> > > > > > >>>>> in >>>>> > > > >> > > >> > > > > > >>>>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>>>> old >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me >>>>> when >>>>> > > should >>>>> > > > I >>>>> > > > >> > start >>>>> > > > >> > > >> > > > > > >> voting? >>>>> > > > >> > > >> > > > > > >>>> Can >>>>> > > > >> > > >> > > > > > >>>>> I >>>>> > > > >> > > >> > > > > > >>>>>>>> start >>>>> > > > >> > > >> > > > > > >>>>>>>>>> now? >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Best, >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Vino >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> [1]: >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > >>>>> > > > >> > > >> >>>>> > > > >> > > >>>>> > > > >> > >>>>> > > > >> >>>>> > > > >>>>> > > >>>>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up >>>>> < >>>>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up >>>>> > >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> [2]: >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > >>>>> > > > >> > > >> >>>>> > > > >> > > >>>>> > > > >> > >>>>> > > > >> >>>>> > > > >>>>> > > >>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308 >>>>> < >>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308 >>>>> > >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> leesf <leesf0...@gmail.com >>>>> <mailto:leesf0...@gmail.com>> >>>>> > > 于2019年6月13日周四 >>>>> > > > >> > > 上午9:19写道： >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for >>>>> your >>>>> > > > >> efforts. >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> Best, >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> Leesf >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> vino yang < >>>>> yanghua1...@gmail.com <mailto:yanghua1...@gmail.com>> >>>>> > > > >> > 于2019年6月12日周三 >>>>> > > > >> > > >> > > > > > >>> 下午5:46写道： >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Hi folks, >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP >>>>> > > discussion >>>>> > > > >> > thread >>>>> > > > >> > > >> > > > > > >> about >>>>> > > > >> > > >> > > > > > >>>>>>> supporting >>>>> > > > >> > > >> > > > > > >>>>>>>>>> local >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink. >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can >>>>> effectively >>>>> > > > >> > alleviate >>>>> > > > >> > > >> data >>>>> > > > >> > > >> > > > > > >>>> skew. >>>>> > > > >> > > >> > > > > > >>>>>>> This >>>>> > > > >> > > >> > > > > > >>>>>>>> is >>>>> > > > >> > > >> > > > > > >>>>>>>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> FLIP: >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > >>>>> > > > >> > > >> >>>>> > > > >> > > >>>>> > > > >> > >>>>> > > > >> >>>>> > > > >>>>> > > >>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink >>>>> < >>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink >>>>> > >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP) >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are >>>>> widely >>>>> > > used >>>>> > > > to >>>>> > > > >> > > >> perform >>>>> > > > >> > > >> > > > > > >>>>>> aggregating >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> operations >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) >>>>> on the >>>>> > > > >> elements >>>>> > > > >> > > >> that >>>>> > > > >> > > >> > > > > > >>> have >>>>> > > > >> > > >> > > > > > >>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>> same >>>>> > > > >> > > >> > > > > > >>>>>>>>>> key. >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> When >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the >>>>> elements with >>>>> > > > the >>>>> > > > >> > same >>>>> > > > >> > > >> key >>>>> > > > >> > > >> > > > > > >>> will >>>>> > > > >> > > >> > > > > > >>>> be >>>>> > > > >> > > >> > > > > > >>>>>>> sent >>>>> > > > >> > > >> > > > > > >>>>>>>> to >>>>> > > > >> > > >> > > > > > >>>>>>>>>> and >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task. >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> The performance of these >>>>> aggregating >>>>> > > > >> > operations >>>>> > > > >> > > is >>>>> > > > >> > > >> > > > > > >> very >>>>> > > > >> > > >> > > > > > >>>>>>> sensitive >>>>> > > > >> > > >> > > > > > >>>>>>>>> to >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the >>>>> cases >>>>> > > where >>>>> > > > >> the >>>>> > > > >> > > >> > > > > > >>> distribution >>>>> > > > >> > > >> > > > > > >>>>> of >>>>> > > > >> > > >> > > > > > >>>>>>> keys >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> follows a >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance >>>>> will be >>>>> > > > >> > > >> significantly >>>>> > > > >> > > >> > > > > > >>>>>> downgraded. >>>>> > > > >> > > >> > > > > > >>>>>>>>> More >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the >>>>> degree of >>>>> > > > >> > parallelism >>>>> > > > >> > > >> does >>>>> > > > >> > > >> > > > > > >>> not >>>>> > > > >> > > >> > > > > > >>>>> help >>>>> > > > >> > > >> > > > > > >>>>>>>> when >>>>> > > > >> > > >> > > > > > >>>>>>>>> a >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> task >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key. >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a >>>>> widely-adopted >>>>> > > > >> method >>>>> > > > >> > to >>>>> > > > >> > > >> > > > > > >> reduce >>>>> > > > >> > > >> > > > > > >>>> the >>>>> > > > >> > > >> > > > > > >>>>>>>>>> performance >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can >>>>> decompose >>>>> > > > the >>>>> > > > >> > > >> > > > > > >> aggregating >>>>> > > > >> > > >> > > > > > >>>>>>>> operations >>>>> > > > >> > > >> > > > > > >>>>>>>>>> into >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> two >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we >>>>> > > aggregate >>>>> > > > >> the >>>>> > > > >> > > >> elements >>>>> > > > >> > > >> > > > > > >>> of >>>>> > > > >> > > >> > > > > > >>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>> same >>>>> > > > >> > > >> > > > > > >>>>>>>>> key >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> at >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain >>>>> partial >>>>> > > > results. >>>>> > > > >> > Then >>>>> > > > >> > > at >>>>> > > > >> > > >> > > > > > >> the >>>>> > > > >> > > >> > > > > > >>>>> second >>>>> > > > >> > > >> > > > > > >>>>>>>>> phase, >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> these >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to >>>>> receivers >>>>> > > > >> > according >>>>> > > > >> > > to >>>>> > > > >> > > >> > > > > > >>> their >>>>> > > > >> > > >> > > > > > >>>>> keys >>>>> > > > >> > > >> > > > > > >>>>>>> and >>>>> > > > >> > > >> > > > > > >>>>>>>>> are >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final >>>>> result. >>>>> > > > Since >>>>> > > > >> the >>>>> > > > >> > > >> number >>>>> > > > >> > > >> > > > > > >>> of >>>>> > > > >> > > >> > > > > > >>>>>>> partial >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> results >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is >>>>> limited by >>>>> > > > the >>>>> > > > >> > > >> number of >>>>> > > > >> > > >> > > > > > >>>>>> senders, >>>>> > > > >> > > >> > > > > > >>>>>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can >>>>> be >>>>> > > reduced. >>>>> > > > >> > > >> Besides, by >>>>> > > > >> > > >> > > > > > >>>>>> reducing >>>>> > > > >> > > >> > > > > > >>>>>>>> the >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> amount >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the >>>>> performance can >>>>> > > > be >>>>> > > > >> > > further >>>>> > > > >> > > >> > > > > > >>>>> improved. >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *More details*: >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Design documentation: >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > >>>>> > > > >> > > >> >>>>> > > > >> > > >>>>> > > > >> > >>>>> > > > >> >>>>> > > > >>>>> > > >>>>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing >>>>> < >>>>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing >>>>> > >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread: >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > >>>>> > > > >> > > >> >>>>> > > > >> > > >>>>> > > > >> > >>>>> > > > >> >>>>> > > > >>>>> > > >>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308 >>>>> < >>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308 >>>>> > >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 < >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> https://issues.apache.org/jira/browse/FLINK-12786 < >>>>> https://issues.apache.org/jira/browse/FLINK-12786> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your >>>>> > > feedback! >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Best, >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Vino >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>>> >>>>> > > > >> > > >> > > > > > >>>>>> >>>>> > > > >> > > >> > > > > > >>>>> >>>>> > > > >> > > >> > > > > > >>>> >>>>> > > > >> > > >> > > > > > >>> >>>>> > > > >> > > >> > > > > > >> >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > > >>>>> > > > >> > > >> > > > > >>>>> > > > >> > > >> > > > >>>>> > > > >> > > >> > > >>>>> > > > >> > > >> > >>>>> > > > >> > > >> >>>>> > > > >> > > > >>>>> > > > >> > > >>>>> > > > >> > >>>>> > > > >> >>>>> > > > > >>>>> > > > >>>>> > > >>>>> >>>>>
