Yea I really like that idea I'll see what I can do to update the kip and my pr when I have some time. I'm not sure how well creating the kstreamaggregates will go though because at that point I will have thrown away the type of the values. It will be type safe I just may need to do a little forcing.
Thanks, Kyle On May 24, 2017 3:28 PM, "Guozhang Wang" <wangg...@gmail.com> wrote: > Kyle, > > Thanks for the explanations, my previous read on the wiki examples was > wrong. > > So I guess my motivation should be "reduced" to: can we move the window > specs param from "KGroupedStream#cogroup(..)" to > "CogroupedKStream#aggregate(..)", and my motivations are: > > 1. minor: we can reduce the #.generics in CogroupedKStream from 3 to 2. > 2. major: this is for extensibility of the APIs, and since we are removing > the "Evolving" annotations on Streams it may be harder to change it again > in the future. The extended use cases are that people wanted to have > windowed running aggregates on different granularities, e.g. "give me the > counts per-minute, per-hour, per-day and per-week", and today in DSL we > need to specify that case in multiple aggregate operators, which gets a > state store / changelog, etc. And it is possible to optimize it as well to > a single state store. Its implementation would be tricky as you need to > contain different lengthed windows within your window store but just from > the public API point of view, it could be specified as: > > CogroupedKStream stream = stream1.cogroup(stream2, ... "state-store-name"); > > table1 = stream.aggregate(/*per-minute window*/) > table2 = stream.aggregate(/*per-hour window*/) > table3 = stream.aggregate(/*per-day window*/) > > while underlying we are only using a single store "state-store-name" for > it. > > > Although this feature is out of the scope of this KIP, I'd like to discuss > if we can "leave the door open" to make such changes without modifying the > public APIs . > > Guozhang > > > On Wed, May 24, 2017 at 3:57 AM, Kyle Winkelman <winkelman.k...@gmail.com> > wrote: > > > I allow defining a single window/sessionwindow one time when you make the > > cogroup call from a KGroupedStream. From then on you are using the > cogroup > > call from with in CogroupedKStream which doesnt accept any additional > > windows/sessionwindows. > > > > Is this what you meant by your question or did I misunderstand? > > > > On May 23, 2017 9:33 PM, "Guozhang Wang" <wangg...@gmail.com> wrote: > > > > Another question that came to me is on "window alignment": from the KIP > it > > seems you are allowing users to specify a (potentially different) window > > spec in each co-grouped input stream. So if these window specs are > > different how should we "align" them with different input streams? I > think > > it is more natural to only specify on window spec in the > > > > KTable<RK, V> CogroupedKStream#aggregate(Windows); > > > > > > And remove it from the cogroup() functions. WDYT? > > > > > > Guozhang > > > > On Tue, May 23, 2017 at 6:22 PM, Guozhang Wang <wangg...@gmail.com> > wrote: > > > > > Thanks for the proposal Kyle, this is a quite common use case to > support > > > such multi-way table join (i.e. N source tables with N aggregate func) > > with > > > a single store and N+1 serdes, I have seen lots of people using the > > > low-level PAPI to achieve this goal. > > > > > > > > > On Fri, May 19, 2017 at 10:04 AM, Kyle Winkelman < > > winkelman.k...@gmail.com > > > > wrote: > > > > > >> I like your point about not handling other cases such as count and > > reduce. > > >> > > >> I think that reduce may not make sense because reduce assumes that the > > >> input values are the same as the output values. With cogroup there may > > be > > >> multiple different input types and then your output type cant be > > multiple > > >> different things. In the case where you have all matching value types > > you > > >> can do KStreamBuilder#merge followed by the reduce. > > >> > > >> As for count I think it is possible to call count on all the > individual > > >> grouped streams and then do joins. Otherwise we could maybe make a > > special > > >> call in groupedstream for this case. Because in this case we dont need > > to > > >> do type checking on the values. It could be similar to the current > count > > >> methods but accept a var args of additonal grouped streams as well and > > >> make > > >> sure they have a key type of K. > > >> > > >> The way I have put the kip together is to ensure that we do type > > checking. > > >> I don't see a way we could group them all first and then make a call > to > > >> count, reduce, or aggregate because with aggregate they would need to > > pass > > >> a list of aggregators and we would have no way of type checking that > > they > > >> match the grouped streams. > > >> > > >> Thanks, > > >> Kyle > > >> > > >> On May 19, 2017 11:42 AM, "Xavier Léauté" <xav...@confluent.io> > wrote: > > >> > > >> > Sorry to jump on this thread so late. I agree this is a very useful > > >> > addition and wanted to provide an additional use-case and some more > > >> > comments. > > >> > > > >> > This is actually a very common analytics use-case in the ad-tech > > >> industry. > > >> > The typical setup will have an auction stream, an impression stream, > > >> and a > > >> > click stream. Those three streams need to be combined to compute > > >> aggregate > > >> > statistics (e.g. impression statistics, and click-through rates), > > since > > >> > most of the attributes of interest are only present the auction > > stream. > > >> > > > >> > A simple way to do this is to co-group all the streams by the > auction > > >> key, > > >> > and process updates to the co-group as events for each stream come > in, > > >> > keeping only one value from each stream before sending downstream > for > > >> > further processing / aggregation. > > >> > > > >> > One could view the result of that co-group operation as a "KTable" > > with > > >> > multiple values per key. The key being the grouping key, and the > > values > > >> > consisting of one value per stream. > > >> > > > >> > What I like about Kyle's approach is that allows elegant co-grouping > > of > > >> > multiple streams without having to worry about the number of > streams, > > >> and > > >> > avoids dealing with Tuple types or other generic interfaces that > could > > >> get > > >> > messy if we wanted to preserve all the value types in the resulting > > >> > co-grouped stream. > > >> > > > >> > My only concern is that we only allow the cogroup + aggregate > combined > > >> > operation. This forces the user to build their own tuple > serialization > > >> > format if they want to preserve the individual input stream values > as > > a > > >> > group. It also deviates quite a bit from our approach in > > KGroupedStream > > >> > which offers other operations, such as count and reduce, which > should > > >> also > > >> > be applicable to a co-grouped stream. > > >> > > > >> > Overall I still think this is a really useful addition, but I feel > we > > >> > haven't spend much time trying to explore alternative DSLs that > could > > >> maybe > > >> > generalize better or match our existing syntax more closely. > > >> > > > >> > On Tue, May 9, 2017 at 8:08 AM Kyle Winkelman < > > winkelman.k...@gmail.com > > >> > > > >> > wrote: > > >> > > > >> > > Eno, is there anyone else that is an expert in the kafka streams > > realm > > >> > that > > >> > > I should reach out to for input? > > >> > > > > >> > > I believe Damian Guy is still planning on reviewing this more in > > depth > > >> > so I > > >> > > will wait for his inputs before continuing. > > >> > > > > >> > > On May 9, 2017 7:30 AM, "Eno Thereska" <eno.there...@gmail.com> > > >> wrote: > > >> > > > > >> > > > Thanks Kyle, good arguments. > > >> > > > > > >> > > > Eno > > >> > > > > > >> > > > > On May 7, 2017, at 5:06 PM, Kyle Winkelman < > > >> winkelman.k...@gmail.com > > >> > > > > >> > > > wrote: > > >> > > > > > > >> > > > > *- minor: could you add an exact example (similar to what > Jay’s > > >> > example > > >> > > > is, > > >> > > > > or like your Spark/Pig pointers had) to make this super > > concrete?* > > >> > > > > I have added a more concrete example to the KIP. > > >> > > > > > > >> > > > > *- my main concern is that we’re exposing this optimization to > > the > > >> > DSL. > > >> > > > In > > >> > > > > an ideal world, an optimizer would take the existing DSL and > do > > >> the > > >> > > right > > >> > > > > thing under the covers (create just one state store, arrange > the > > >> > nodes > > >> > > > > etc). The original DSL had a bunch of small, composable pieces > > >> > (group, > > >> > > > > aggregate, join) that this proposal groups together. I’d like > to > > >> hear > > >> > > > your > > >> > > > > thoughts on whether it’s possible to do this optimization with > > the > > >> > > > current > > >> > > > > DSL, at the topology builder level.* > > >> > > > > You would have to make a lot of checks to understand if it is > > even > > >> > > > possible > > >> > > > > to make this optimization: > > >> > > > > 1. Make sure they are all KTableKTableOuterJoins > > >> > > > > 2. None of the intermediate KTables are used for anything > else. > > >> > > > > 3. None of the intermediate stores are used. (This may be > > >> impossible > > >> > > > > especially if they use KafkaStreams#store after the topology > has > > >> > > already > > >> > > > > been built.) > > >> > > > > You would then need to make decisions during the optimization: > > >> > > > > 1. Your new initializer would the composite of all the > > individual > > >> > > > > initializers and the valueJoiners. > > >> > > > > 2. I am having a hard time thinking about how you would turn > the > > >> > > > > aggregators and valueJoiners into an aggregator that would > work > > on > > >> > the > > >> > > > > final object, but this may be possible. > > >> > > > > 3. Which state store would you use? The ones declared would be > > for > > >> > the > > >> > > > > aggregate values. None of the declared ones would be > guaranteed > > to > > >> > hold > > >> > > > the > > >> > > > > final object. This would mean you must created a new state > store > > >> and > > >> > > not > > >> > > > > created any of the declared ones. > > >> > > > > > > >> > > > > The main argument I have against it is even if it could be > done > > I > > >> > don't > > >> > > > > know that we would want to have this be an optimization in the > > >> > > background > > >> > > > > because the user would still be required to think about all of > > the > > >> > > > > intermediate values that they shouldn't need to worry about if > > >> they > > >> > > only > > >> > > > > care about the final object. > > >> > > > > > > >> > > > > In my opinion cogroup is a common enough case that it should > be > > >> part > > >> > of > > >> > > > the > > >> > > > > composable pieces (group, aggregate, join) because we want to > > >> allow > > >> > > > people > > >> > > > > to join more than 2 or more streams in an easy way. Right now > I > > >> don't > > >> > > > think > > >> > > > > we give them ways of handling this use case easily. > > >> > > > > > > >> > > > > *-I think there will be scope for several such optimizations > in > > >> the > > >> > > > future > > >> > > > > and perhaps at some point we need to think about decoupling > the > > >> 1:1 > > >> > > > mapping > > >> > > > > from the DSL into the physical topology.* > > >> > > > > I would argue that cogroup is not just an optimization it is a > > new > > >> > way > > >> > > > for > > >> > > > > the users to look at accomplishing a problem that requires > > >> multiple > > >> > > > > streams. I may sound like a broken record but I don't think > > users > > >> > > should > > >> > > > > have to build the N-1 intermediate tables and deal with their > > >> > > > initializers, > > >> > > > > serdes and stores if all they care about is the final object. > > >> > > > > Now if for example someone uses cogroup but doesn't supply > > >> additional > > >> > > > > streams and aggregators this case is equivalent to a single > > >> grouped > > >> > > > stream > > >> > > > > making an aggregate call. This case is what I view an > > optimization > > >> > as, > > >> > > we > > >> > > > > could remove the KStreamCogroup and act as if there was just a > > >> call > > >> > to > > >> > > > > KGroupedStream#aggregate instead of calling > > >> KGroupedStream#cogroup. > > >> > (I > > >> > > > > would prefer to just write a warning saying that this is not > how > > >> > > cogroup > > >> > > > is > > >> > > > > to be used.) > > >> > > > > > > >> > > > > Thanks, > > >> > > > > Kyle > > >> > > > > > > >> > > > > On Sun, May 7, 2017 at 5:41 AM, Eno Thereska < > > >> eno.there...@gmail.com > > >> > > > > >> > > > wrote: > > >> > > > > > > >> > > > >> Hi Kyle, > > >> > > > >> > > >> > > > >> Thanks for the KIP again. A couple of comments: > > >> > > > >> > > >> > > > >> - minor: could you add an exact example (similar to what > Jay’s > > >> > example > > >> > > > is, > > >> > > > >> or like your Spark/Pig pointers had) to make this super > > concrete? > > >> > > > >> > > >> > > > >> - my main concern is that we’re exposing this optimization to > > the > > >> > DSL. > > >> > > > In > > >> > > > >> an ideal world, an optimizer would take the existing DSL and > do > > >> the > > >> > > > right > > >> > > > >> thing under the covers (create just one state store, arrange > > the > > >> > nodes > > >> > > > >> etc). The original DSL had a bunch of small, composable > pieces > > >> > (group, > > >> > > > >> aggregate, join) that this proposal groups together. I’d like > > to > > >> > hear > > >> > > > your > > >> > > > >> thoughts on whether it’s possible to do this optimization > with > > >> the > > >> > > > current > > >> > > > >> DSL, at the topology builder level. > > >> > > > >> > > >> > > > >> I think there will be scope for several such optimizations in > > the > > >> > > future > > >> > > > >> and perhaps at some point we need to think about decoupling > the > > >> 1:1 > > >> > > > mapping > > >> > > > >> from the DSL into the physical topology. > > >> > > > >> > > >> > > > >> Thanks > > >> > > > >> Eno > > >> > > > >> > > >> > > > >>> On May 5, 2017, at 4:39 PM, Jay Kreps <j...@confluent.io> > > wrote: > > >> > > > >>> > > >> > > > >>> I haven't digested the proposal but the use case is pretty > > >> common. > > >> > An > > >> > > > >>> example would be the "customer 360" or "unified customer > > >> profile" > > >> > use > > >> > > > >> case > > >> > > > >>> we often use. In that use case you have a dozen systems each > > of > > >> > which > > >> > > > has > > >> > > > >>> some information about your customer (account details, > > settings, > > >> > > > billing > > >> > > > >>> info, customer service contacts, purchase history, etc). > Your > > >> goal > > >> > is > > >> > > > to > > >> > > > >>> join/munge these into a single profile record for each > > customer > > >> > that > > >> > > > has > > >> > > > >>> all the relevant info in a usable form and is up-to-date > with > > >> all > > >> > the > > >> > > > >>> source systems. If you implement that with kstreams as a > > >> sequence > > >> > of > > >> > > > >> joins > > >> > > > >>> i think today we'd fully materialize N-1 intermediate > tables. > > >> But > > >> > > > clearly > > >> > > > >>> you only need a single stage to group all these things that > > are > > >> > > already > > >> > > > >>> co-partitioned. A distributed database would do this under > the > > >> > covers > > >> > > > >> which > > >> > > > >>> is arguably better (at least when it does the right thing) > and > > >> > > perhaps > > >> > > > we > > >> > > > >>> could do the same thing but I'm not sure we know the > > >> partitioning > > >> > so > > >> > > we > > >> > > > >> may > > >> > > > >>> need an explicit cogroup command that impllies they are > > already > > >> > > > >>> co-partitioned. > > >> > > > >>> > > >> > > > >>> -Jay > > >> > > > >>> > > >> > > > >>> On Fri, May 5, 2017 at 5:56 AM, Kyle Winkelman < > > >> > > > winkelman.k...@gmail.com > > >> > > > >>> > > >> > > > >>> wrote: > > >> > > > >>> > > >> > > > >>>> Yea thats a good way to look at it. > > >> > > > >>>> I have seen this type of functionality in a couple other > > >> platforms > > >> > > > like > > >> > > > >>>> spark and pig. > > >> > > > >>>> https://spark.apache.org/docs/0.6.2/api/core/spark/ > > >> > > > >> PairRDDFunctions.html > > >> > > > >>>> https://www.tutorialspoint.com/apache_pig/apache_pig_ > > >> > > > >> cogroup_operator.htm > > >> > > > >>>> > > >> > > > >>>> > > >> > > > >>>> On May 5, 2017 7:43 AM, "Damian Guy" <damian....@gmail.com > > > > >> > wrote: > > >> > > > >>>> > > >> > > > >>>>> Hi Kyle, > > >> > > > >>>>> > > >> > > > >>>>> If i'm reading this correctly it is like an N way outer > > join? > > >> So > > >> > an > > >> > > > >> input > > >> > > > >>>>> on any stream will always produce a new aggregated value - > > is > > >> > that > > >> > > > >>>> correct? > > >> > > > >>>>> Effectively, each Aggregator just looks up the current > > value, > > >> > > > >> aggregates > > >> > > > >>>>> and forwards the result. > > >> > > > >>>>> I need to look into it and think about it a bit more, but > it > > >> > seems > > >> > > > like > > >> > > > >>>> it > > >> > > > >>>>> could be a useful optimization. > > >> > > > >>>>> > > >> > > > >>>>> On Thu, 4 May 2017 at 23:21 Kyle Winkelman < > > >> > > winkelman.k...@gmail.com > > >> > > > > > > >> > > > >>>>> wrote: > > >> > > > >>>>> > > >> > > > >>>>>> I sure can. I have added the following description to my > > >> KIP. If > > >> > > > this > > >> > > > >>>>>> doesn't help let me know and I will take some more time > to > > >> > build a > > >> > > > >>>>> diagram > > >> > > > >>>>>> and make more of a step by step description: > > >> > > > >>>>>> > > >> > > > >>>>>> Example with Current API: > > >> > > > >>>>>> > > >> > > > >>>>>> KTable<K, V1> table1 = > > >> > > > >>>>>> builder.stream("topic1").groupByKey().aggregate( > > initializer1 > > >> , > > >> > > > >>>>> aggregator1, > > >> > > > >>>>>> aggValueSerde1, storeName1); > > >> > > > >>>>>> KTable<K, V2> table2 = > > >> > > > >>>>>> builder.stream("topic2").groupByKey().aggregate( > > initializer2 > > >> , > > >> > > > >>>>> aggregator2, > > >> > > > >>>>>> aggValueSerde2, storeName2); > > >> > > > >>>>>> KTable<K, V3> table3 = > > >> > > > >>>>>> builder.stream("topic3").groupByKey().aggregate( > > initializer3 > > >> , > > >> > > > >>>>> aggregator3, > > >> > > > >>>>>> aggValueSerde3, storeName3); > > >> > > > >>>>>> KTable<K, CG> cogrouped = table1.outerJoin(table2, > > >> > > > >>>>>> joinerOneAndTwo).outerJoin(table3, > joinerOneTwoAndThree); > > >> > > > >>>>>> > > >> > > > >>>>>> As you can see this creates 3 StateStores, requires 3 > > >> > > initializers, > > >> > > > >>>> and 3 > > >> > > > >>>>>> aggValueSerdes. This also adds the pressure to user to > > define > > >> > what > > >> > > > the > > >> > > > >>>>>> intermediate values are going to be (V1, V2, V3). They > are > > >> left > > >> > > > with a > > >> > > > >>>>>> couple choices, first to make V1, V2, and V3 all the same > > as > > >> CG > > >> > > and > > >> > > > >> the > > >> > > > >>>>> two > > >> > > > >>>>>> joiners are more like mergers, or second make them > > >> intermediate > > >> > > > states > > >> > > > >>>>> such > > >> > > > >>>>>> as Topic1Map, Topic2Map, and Topic3Map and the joiners > use > > >> those > > >> > > to > > >> > > > >>>> build > > >> > > > >>>>>> the final aggregate CG value. This is something the user > > >> could > > >> > > avoid > > >> > > > >>>>>> thinking about with this KIP. > > >> > > > >>>>>> > > >> > > > >>>>>> When a new input arrives lets say at "topic1" it will > first > > >> go > > >> > > > through > > >> > > > >>>> a > > >> > > > >>>>>> KStreamAggregate grabbing the current aggregate from > > >> storeName1. > > >> > > It > > >> > > > >>>> will > > >> > > > >>>>>> produce this in the form of the first intermediate value > > and > > >> get > > >> > > > sent > > >> > > > >>>>>> through a KTableKTableOuterJoin where it will look up the > > >> > current > > >> > > > >> value > > >> > > > >>>>> of > > >> > > > >>>>>> the key in storeName2. It will use the first joiner to > > >> calculate > > >> > > the > > >> > > > >>>>> second > > >> > > > >>>>>> intermediate value, which will go through an additional > > >> > > > >>>>>> KTableKTableOuterJoin. Here it will look up the current > > >> value of > > >> > > the > > >> > > > >>>> key > > >> > > > >>>>> in > > >> > > > >>>>>> storeName3 and use the second joiner to build the final > > >> > aggregate > > >> > > > >>>> value. > > >> > > > >>>>>> > > >> > > > >>>>>> If you think through all possibilities for incoming > topics > > >> you > > >> > > will > > >> > > > >> see > > >> > > > >>>>>> that no matter which topic it comes in through all three > > >> stores > > >> > > are > > >> > > > >>>>> queried > > >> > > > >>>>>> and all of the joiners must get used. > > >> > > > >>>>>> > > >> > > > >>>>>> Topology wise for N incoming streams this creates N > > >> > > > >>>>>> KStreamAggregates, 2*(N-1) KTableKTableOuterJoins, and > N-1 > > >> > > > >>>>>> KTableKTableJoinMergers. > > >> > > > >>>>>> > > >> > > > >>>>>> > > >> > > > >>>>>> > > >> > > > >>>>>> Example with Proposed API: > > >> > > > >>>>>> > > >> > > > >>>>>> KGroupedStream<K, V1> grouped1 = > builder.stream("topic1"). > > >> > > > >>>> groupByKey(); > > >> > > > >>>>>> KGroupedStream<K, V2> grouped2 = > builder.stream("topic2"). > > >> > > > >>>> groupByKey(); > > >> > > > >>>>>> KGroupedStream<K, V3> grouped3 = > builder.stream("topic3"). > > >> > > > >>>> groupByKey(); > > >> > > > >>>>>> KTable<K, CG> cogrouped = grouped1.cogroup(initializer1, > > >> > > > aggregator1, > > >> > > > >>>>>> aggValueSerde1, storeName1) > > >> > > > >>>>>> .cogroup(grouped2, aggregator2) > > >> > > > >>>>>> .cogroup(grouped3, aggregator3) > > >> > > > >>>>>> .aggregate(); > > >> > > > >>>>>> > > >> > > > >>>>>> As you can see this creates 1 StateStore, requires 1 > > >> > initializer, > > >> > > > and > > >> > > > >> 1 > > >> > > > >>>>>> aggValueSerde. The user no longer has to worry about the > > >> > > > intermediate > > >> > > > >>>>>> values and the joiners. All they have to think about is > how > > >> each > > >> > > > >> stream > > >> > > > >>>>>> impacts the creation of the final CG object. > > >> > > > >>>>>> > > >> > > > >>>>>> When a new input arrives lets say at "topic1" it will > first > > >> go > > >> > > > through > > >> > > > >>>> a > > >> > > > >>>>>> KStreamAggreagte and grab the current aggregate from > > >> storeName1. > > >> > > It > > >> > > > >>>> will > > >> > > > >>>>>> add its incoming object to the aggregate, update the > store > > >> and > > >> > > pass > > >> > > > >> the > > >> > > > >>>>> new > > >> > > > >>>>>> aggregate on. This new aggregate goes through the > > >> KStreamCogroup > > >> > > > which > > >> > > > >>>> is > > >> > > > >>>>>> pretty much just a pass through processor and you are > done. > > >> > > > >>>>>> > > >> > > > >>>>>> Topology wise for N incoming streams the new api will > only > > >> every > > >> > > > >>>> create N > > >> > > > >>>>>> KStreamAggregates and 1 KStreamCogroup. > > >> > > > >>>>>> > > >> > > > >>>>>> On Thu, May 4, 2017 at 4:42 PM, Matthias J. Sax < > > >> > > > >> matth...@confluent.io > > >> > > > >>>>> > > >> > > > >>>>>> wrote: > > >> > > > >>>>>> > > >> > > > >>>>>>> Kyle, > > >> > > > >>>>>>> > > >> > > > >>>>>>> thanks a lot for the KIP. Maybe I am a little slow, but > I > > >> could > > >> > > not > > >> > > > >>>>>>> follow completely. Could you maybe add a more concrete > > >> example, > > >> > > > like > > >> > > > >>>> 3 > > >> > > > >>>>>>> streams with 3 records each (plus expected result), and > > show > > >> > the > > >> > > > >>>>>>> difference between current way to to implement it and > the > > >> > > proposed > > >> > > > >>>> API? > > >> > > > >>>>>>> This could also cover the internal processing to see > what > > >> store > > >> > > > calls > > >> > > > >>>>>>> would be required for both approaches etc. > > >> > > > >>>>>>> > > >> > > > >>>>>>> I think, it's pretty advanced stuff you propose, and it > > >> would > > >> > > help > > >> > > > to > > >> > > > >>>>>>> understand it better. > > >> > > > >>>>>>> > > >> > > > >>>>>>> Thanks a lot! > > >> > > > >>>>>>> > > >> > > > >>>>>>> > > >> > > > >>>>>>> -Matthias > > >> > > > >>>>>>> > > >> > > > >>>>>>> > > >> > > > >>>>>>> > > >> > > > >>>>>>> On 5/4/17 11:39 AM, Kyle Winkelman wrote: > > >> > > > >>>>>>>> I have made a pull request. It can be found here. > > >> > > > >>>>>>>> > > >> > > > >>>>>>>> https://github.com/apache/kafka/pull/2975 > > >> > > > >>>>>>>> > > >> > > > >>>>>>>> I plan to write some more unit tests for my classes and > > get > > >> > > around > > >> > > > >>>> to > > >> > > > >>>>>>>> writing documentation for the public api additions. > > >> > > > >>>>>>>> > > >> > > > >>>>>>>> One thing I was curious about is during the > > >> > > > >>>>>>> KCogroupedStreamImpl#aggregate > > >> > > > >>>>>>>> method I pass null to the KGroupedStream# > > >> > repartitionIfRequired > > >> > > > >>>>> method. > > >> > > > >>>>>> I > > >> > > > >>>>>>>> can't supply the store name because if more than one > > >> grouped > > >> > > > stream > > >> > > > >>>>>>>> repartitions an error is thrown. Is there some name > that > > >> > someone > > >> > > > >>>> can > > >> > > > >>>>>>>> recommend or should I leave the null and allow it to > fall > > >> back > > >> > > to > > >> > > > >>>> the > > >> > > > >>>>>>>> KGroupedStream.name? > > >> > > > >>>>>>>> > > >> > > > >>>>>>>> Should this be expanded to handle grouped tables? This > > >> would > > >> > be > > >> > > > >>>>> pretty > > >> > > > >>>>>>> easy > > >> > > > >>>>>>>> for a normal aggregate but one allowing session stores > > and > > >> > > > windowed > > >> > > > >>>>>>> stores > > >> > > > >>>>>>>> would required KTableSessionWindowAggregate and > > >> > > > >>>> KTableWindowAggregate > > >> > > > >>>>>>>> implementations. > > >> > > > >>>>>>>> > > >> > > > >>>>>>>> Thanks, > > >> > > > >>>>>>>> Kyle > > >> > > > >>>>>>>> > > >> > > > >>>>>>>> On May 4, 2017 1:24 PM, "Eno Thereska" < > > >> > eno.there...@gmail.com> > > >> > > > >>>>> wrote: > > >> > > > >>>>>>>> > > >> > > > >>>>>>>>> I’ll look as well asap, sorry, been swamped. > > >> > > > >>>>>>>>> > > >> > > > >>>>>>>>> Eno > > >> > > > >>>>>>>>>> On May 4, 2017, at 6:17 PM, Damian Guy < > > >> > damian....@gmail.com> > > >> > > > >>>>> wrote: > > >> > > > >>>>>>>>>> > > >> > > > >>>>>>>>>> Hi Kyle, > > >> > > > >>>>>>>>>> > > >> > > > >>>>>>>>>> Thanks for the KIP. I apologize that i haven't had > the > > >> > chance > > >> > > to > > >> > > > >>>>> look > > >> > > > >>>>>>> at > > >> > > > >>>>>>>>>> the KIP yet, but will schedule some time to look into > > it > > >> > > > >>>> tomorrow. > > >> > > > >>>>>> For > > >> > > > >>>>>>>>> the > > >> > > > >>>>>>>>>> implementation, can you raise a PR against kafka > trunk > > >> and > > >> > > mark > > >> > > > >>>> it > > >> > > > >>>>> as > > >> > > > >>>>>>>>> WIP? > > >> > > > >>>>>>>>>> It will be easier to review what you have done. > > >> > > > >>>>>>>>>> > > >> > > > >>>>>>>>>> Thanks, > > >> > > > >>>>>>>>>> Damian > > >> > > > >>>>>>>>>> > > >> > > > >>>>>>>>>> On Thu, 4 May 2017 at 11:50 Kyle Winkelman < > > >> > > > >>>>> winkelman.k...@gmail.com > > >> > > > >>>>>>> > > >> > > > >>>>>>>>> wrote: > > >> > > > >>>>>>>>>> > > >> > > > >>>>>>>>>>> I am replying to this in hopes it will draw some > > >> attention > > >> > to > > >> > > > my > > >> > > > >>>>> KIP > > >> > > > >>>>>>> as > > >> > > > >>>>>>>>> I > > >> > > > >>>>>>>>>>> haven't heard from anyone in a couple days. This is > my > > >> > first > > >> > > > KIP > > >> > > > >>>>> and > > >> > > > >>>>>>> my > > >> > > > >>>>>>>>>>> first large contribution to the project so I'm sure > I > > >> did > > >> > > > >>>>> something > > >> > > > >>>>>>>>> wrong. > > >> > > > >>>>>>>>>>> ;) > > >> > > > >>>>>>>>>>> > > >> > > > >>>>>>>>>>> On May 1, 2017 4:18 PM, "Kyle Winkelman" < > > >> > > > >>>>> winkelman.k...@gmail.com> > > >> > > > >>>>>>>>> wrote: > > >> > > > >>>>>>>>>>> > > >> > > > >>>>>>>>>>>> Hello all, > > >> > > > >>>>>>>>>>>> > > >> > > > >>>>>>>>>>>> I have created KIP-150 to facilitate discussion > about > > >> > adding > > >> > > > >>>>>> cogroup > > >> > > > >>>>>>> to > > >> > > > >>>>>>>>>>>> the streams DSL. > > >> > > > >>>>>>>>>>>> > > >> > > > >>>>>>>>>>>> Please find the KIP here: > > >> > > > >>>>>>>>>>>> https://cwiki.apache.org/ > > confluence/display/KAFKA/KIP- > > >> > > > >>>>>>>>>>>> 150+-+Kafka-Streams+Cogroup > > >> > > > >>>>>>>>>>>> > > >> > > > >>>>>>>>>>>> Please find my initial implementation here: > > >> > > > >>>>>>>>>>>> https://github.com/KyleWinkelman/kafka > > >> > > > >>>>>>>>>>>> > > >> > > > >>>>>>>>>>>> Thanks, > > >> > > > >>>>>>>>>>>> Kyle Winkelman > > >> > > > >>>>>>>>>>>> > > >> > > > >>>>>>>>>>> > > >> > > > >>>>>>>>> > > >> > > > >>>>>>>>> > > >> > > > >>>>>>>> > > >> > > > >>>>>>> > > >> > > > >>>>>>> > > >> > > > >>>>>> > > >> > > > >>>>> > > >> > > > >>>> > > >> > > > >> > > >> > > > >> > > >> > > > > > >> > > > > > >> > > > > >> > > > >> > > > > > > > > > > > > -- > > > -- Guozhang > > > > > > > > > > > -- > > -- Guozhang > > > > > > -- > -- Guozhang >
