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
