Hello Contributors I know that 2.1 is about to be released, but I do need to bump this to keep visibility up. I am still intending to push this through once contributor feedback is given.
Main points that need addressing: 1) Any way (or benefit) in structuring the current singular graph node into multiple nodes? It has a whopping 25 parameters right now. I am a bit fuzzy on how the optimizations are supposed to work, so I would appreciate any help on this aspect. 2) Overall strategy for joining + resolving. This thread has much discourse between Jan and I between the current highwater mark proposal and a groupBy + reduce proposal. I am of the opinion that we need to strictly handle any chance of out-of-order data and leave none of it up to the consumer. Any comments or suggestions here would also help. 3) Anything else that you see that would prevent this from moving to a vote? Thanks Adam On Sun, Sep 30, 2018 at 10:23 AM Adam Bellemare <adam.bellem...@gmail.com> wrote: > Hi Jan > > With the Stores.windowStoreBuilder and Stores.persistentWindowStore, you > actually only need to specify the amount of segments you want and how large > they are. To the best of my understanding, what happens is that the > segments are automatically rolled over as new data with new timestamps are > created. We use this exact functionality in some of the work done > internally at my company. For reference, this is the hopping windowed store. > > https://kafka.apache.org/11/documentation/streams/developer-guide/dsl-api.html#id21 > > In the code that I have provided, there are going to be two 24h segments. > When a record is put into the windowStore, it will be inserted at time T in > both segments. The two segments will always overlap by 12h. As time goes on > and new records are added (say at time T+12h+), the oldest segment will be > automatically deleted and a new segment created. The records are by default > inserted with the context.timestamp(), such that it is the record time, not > the clock time, which is used. > > To the best of my understanding, the timestamps are retained when > restoring from the changelog. > > Basically, this is heavy-handed way to deal with TTL at a segment-level, > instead of at an individual record level. > > On Tue, Sep 25, 2018 at 5:18 PM Jan Filipiak <jan.filip...@trivago.com> > wrote: > >> Will that work? I expected it to blow up with ClassCastException or >> similar. >> >> You either would have to specify the window you fetch/put or iterate >> across all windows the key was found in right? >> >> I just hope the window-store doesn't check stream-time under the hoods >> that would be a questionable interface. >> >> If it does: did you see my comment on checking all the windows earlier? >> that would be needed to actually give reasonable time gurantees. >> >> Best >> >> >> >> On 25.09.2018 13:18, Adam Bellemare wrote: >> > Hi Jan >> > >> > Check for " highwaterMat " in the PR. I only changed the state store, >> not >> > the ProcessorSupplier. >> > >> > Thanks, >> > Adam >> > >> > On Mon, Sep 24, 2018 at 2:47 PM, Jan Filipiak <jan.filip...@trivago.com >> > >> > wrote: >> > >> >> >> >> >> >> On 24.09.2018 16:26, Adam Bellemare wrote: >> >> >> >>> @Guozhang >> >>> >> >>> Thanks for the information. This is indeed something that will be >> >>> extremely >> >>> useful for this KIP. >> >>> >> >>> @Jan >> >>> Thanks for your explanations. That being said, I will not be moving >> ahead >> >>> with an implementation using reshuffle/groupBy solution as you >> propose. >> >>> That being said, if you wish to implement it yourself off of my >> current PR >> >>> and submit it as a competitive alternative, I would be more than >> happy to >> >>> help vet that as an alternate solution. As it stands right now, I do >> not >> >>> really have more time to invest into alternatives without there being >> a >> >>> strong indication from the binding voters which they would prefer. >> >>> >> >>> >> >> Hey, total no worries. I think I personally gave up on the streams DSL >> for >> >> some time already, otherwise I would have pulled this KIP through >> already. >> >> I am currently reimplementing my own DSL based on PAPI. >> >> >> >> >> >>> I will look at finishing up my PR with the windowed state store in the >> >>> next >> >>> week or so, exercising it via tests, and then I will come back for >> final >> >>> discussions. In the meantime, I hope that any of the binding voters >> could >> >>> take a look at the KIP in the wiki. I have updated it according to the >> >>> latest plan: >> >>> >> >>> https://cwiki.apache.org/confluence/display/KAFKA/KIP-213+ >> >>> Support+non-key+joining+in+KTable >> >>> >> >>> I have also updated the KIP PR to use a windowed store. This could be >> >>> replaced by the results of KIP-258 whenever they are completed. >> >>> https://github.com/apache/kafka/pull/5527 >> >>> >> >>> Thanks, >> >>> >> >>> Adam >> >>> >> >> >> >> Is the HighWatermarkResolverProccessorsupplier already updated in the >> PR? >> >> expected it to change to Windowed<K>,Long Missing something? >> >> >> >> >> >> >> >>> >> >>> >> >>> On Fri, Sep 14, 2018 at 2:24 PM, Guozhang Wang <wangg...@gmail.com> >> >>> wrote: >> >>> >> >>> Correction on my previous email: KAFKA-5533 is the wrong link, as it >> is >> >>>> for >> >>>> corresponding changelog mechanisms. But as part of KIP-258 we do >> want to >> >>>> have "handling out-of-order data for source KTable" such that >> instead of >> >>>> blindly apply the updates to the materialized store, i.e. following >> >>>> offset >> >>>> ordering, we will reject updates that are older than the current >> key's >> >>>> timestamps, i.e. following timestamp ordering. >> >>>> >> >>>> >> >>>> Guozhang >> >>>> >> >>>> On Fri, Sep 14, 2018 at 11:21 AM, Guozhang Wang <wangg...@gmail.com> >> >>>> wrote: >> >>>> >> >>>> Hello Adam, >> >>>>> >> >>>>> Thanks for the explanation. Regarding the final step (i.e. the high >> >>>>> watermark store, now altered to be replaced with a window store), I >> >>>>> think >> >>>>> another current on-going KIP may actually help: >> >>>>> >> >>>>> https://cwiki.apache.org/confluence/display/KAFKA/KIP- >> >>>>> 258%3A+Allow+to+Store+Record+Timestamps+in+RocksDB >> >>>>> >> >>>>> >> >>>>> This is for adding the timestamp into a key-value store (i.e. only >> for >> >>>>> non-windowed KTable), and then one of its usage, as described in >> >>>>> https://issues.apache.org/jira/browse/KAFKA-5533, is that we can >> then >> >>>>> "reject" updates from the source topics if its timestamp is smaller >> than >> >>>>> the current key's latest update timestamp. I think it is very >> similar to >> >>>>> what you have in mind for high watermark based filtering, while you >> only >> >>>>> need to make sure that the timestamps of the joining records are >> >>>>> >> >>>> correctly >> >>>> >> >>>>> inherited though the whole topology to the final stage. >> >>>>> >> >>>>> Note that this KIP is for key-value store and hence non-windowed >> KTables >> >>>>> only, but for windowed KTables we do not really have a good support >> for >> >>>>> their joins anyways ( >> https://issues.apache.org/jira/browse/KAFKA-7107) >> >>>>> I >> >>>>> think we can just consider non-windowed KTable-KTable non-key joins >> for >> >>>>> now. In which case, KIP-258 should help. >> >>>>> >> >>>>> >> >>>>> >> >>>>> Guozhang >> >>>>> >> >>>>> >> >>>>> >> >>>>> On Wed, Sep 12, 2018 at 9:20 PM, Jan Filipiak < >> jan.filip...@trivago.com >> >>>>>> >> >>>>> wrote: >> >>>>> >> >>>>> >> >>>>>> On 11.09.2018 18:00, Adam Bellemare wrote: >> >>>>>> >> >>>>>> Hi Guozhang >> >>>>>>> >> >>>>>>> Current highwater mark implementation would grow endlessly based >> on >> >>>>>>> primary key of original event. It is a pair of (<this table >> primary >> >>>>>>> >> >>>>>> key>, >> >>>> >> >>>>> <highest offset seen for that key>). This is used to differentiate >> >>>>>>> >> >>>>>> between >> >>>> >> >>>>> late arrivals and new updates. My newest proposal would be to >> replace >> >>>>>>> >> >>>>>> it >> >>>> >> >>>>> with a Windowed state store of Duration N. This would allow the same >> >>>>>>> behaviour, but cap the size based on time. This should allow for >> all >> >>>>>>> late-arriving events to be processed, and should be customizable >> by >> >>>>>>> the >> >>>>>>> user to tailor to their own needs (ie: perhaps just 10 minutes of >> >>>>>>> >> >>>>>> window, >> >>>> >> >>>>> or perhaps 7 days...). >> >>>>>>> >> >>>>>>> Hi Adam, using time based retention can do the trick here. Even >> if I >> >>>>>> would still like to see the automatic repartitioning optional >> since I >> >>>>>> >> >>>>> would >> >>>> >> >>>>> just reshuffle again. With windowed store I am a little bit >> sceptical >> >>>>>> >> >>>>> about >> >>>> >> >>>>> how to determine the window. So esentially one could run into >> problems >> >>>>>> >> >>>>> when >> >>>> >> >>>>> the rapid change happens near a window border. I will check you >> >>>>>> implementation in detail, if its problematic, we could still check >> >>>>>> _all_ >> >>>>>> windows on read with not to bad performance impact I guess. Will >> let >> >>>>>> you >> >>>>>> know if the implementation would be correct as is. I wouldn't not >> like >> >>>>>> >> >>>>> to >> >>>> >> >>>>> assume that: offset(A) < offset(B) => timestamp(A) < timestamp(B). >> I >> >>>>>> >> >>>>> think >> >>>> >> >>>>> we can't expect that. >> >>>>>> >> >>>>>> >> >>>>>>> >> >>>>>>> @Jan >> >>>>>>> I believe I understand what you mean now - thanks for the >> diagram, it >> >>>>>>> did really help. You are correct that I do not have the original >> >>>>>>> >> >>>>>> primary >> >>>> >> >>>>> key available, and I can see that if it was available then you >> would be >> >>>>>>> able to add and remove events from the Map. That being said, I >> >>>>>>> >> >>>>>> encourage >> >>>> >> >>>>> you to finish your diagrams / charts just for clarity for everyone >> >>>>>>> >> >>>>>> else. >> >>>> >> >>>>> >> >>>>>>> Yeah 100%, this giphy thing is just really hard work. But I >> understand >> >>>>>>> >> >>>>>> the benefits for the rest. Sorry about the original primary key, We >> >>>>>> have >> >>>>>> join and Group by implemented our own in PAPI and basically not >> using >> >>>>>> >> >>>>> any >> >>>> >> >>>>> DSL (Just the abstraction). Completely missed that in original DSL >> its >> >>>>>> >> >>>>> not >> >>>> >> >>>>> there and just assumed it. total brain mess up on my end. Will >> finish >> >>>>>> >> >>>>> the >> >>>> >> >>>>> chart as soon as i get a quite evening this week. >> >>>>>> >> >>>>>> My follow up question for you is, won't the Map stay inside the >> State >> >>>>>> >> >>>>>>> Store indefinitely after all of the changes have propagated? Isn't >> >>>>>>> this >> >>>>>>> effectively the same as a highwater mark state store? >> >>>>>>> >> >>>>>>> Thing is that if the map is empty, substractor is gonna return >> `null` >> >>>>>> >> >>>>> and >> >>>> >> >>>>> the key is removed from the keyspace. But there is going to be a >> store >> >>>>>> 100%, the good thing is that I can use this store directly for >> >>>>>> materialize() / enableSendingOldValues() is a regular store, >> satisfying >> >>>>>> all gurantees needed for further groupby / join. The Windowed >> store is >> >>>>>> >> >>>>> not >> >>>> >> >>>>> keeping the values, so for the next statefull operation we would >> >>>>>> need to instantiate an extra store. or we have the window store >> also >> >>>>>> >> >>>>> have >> >>>> >> >>>>> the values then. >> >>>>>> >> >>>>>> Long story short. if we can flip in a custom group by before >> >>>>>> repartitioning to the original primary key i think it would help >> the >> >>>>>> >> >>>>> users >> >>>> >> >>>>> big time in building efficient apps. Given the original primary key >> >>>>>> >> >>>>> issue I >> >>>> >> >>>>> understand that we do not have a solid foundation to build on. >> >>>>>> Leaving primary key carry along to the user. very unfortunate. I >> could >> >>>>>> understand the decision goes like that. I do not think its a good >> >>>>>> >> >>>>> decision. >> >>>> >> >>>>> >> >>>>>> >> >>>>>> >> >>>>>>> >> >>>>>>> >> >>>>>>> Thanks >> >>>>>>> Adam >> >>>>>>> >> >>>>>>> >> >>>>>>> >> >>>>>>> >> >>>>>>> >> >>>>>>> >> >>>>>>> On Tue, Sep 11, 2018 at 10:07 AM, Prajakta Dumbre < >> >>>>>>> dumbreprajakta...@gmail.com <mailto:dumbreprajakta...@gmail.com>> >> >>>>>>> >> >>>>>> wrote: >> >>>> >> >>>>> >> >>>>>>> please remove me from this group >> >>>>>>> >> >>>>>>> On Tue, Sep 11, 2018 at 1:29 PM Jan Filipiak >> >>>>>>> <jan.filip...@trivago.com <mailto:jan.filip...@trivago.com >> >> >> >>>>>>> >> >>>>>>> wrote: >> >>>>>>> >> >>>>>>> > Hi Adam, >> >>>>>>> > >> >>>>>>> > give me some time, will make such a chart. last time i >> didn't >> >>>>>>> get along >> >>>>>>> > well with giphy and ruined all your charts. >> >>>>>>> > Hopefully i can get it done today >> >>>>>>> > >> >>>>>>> > On 08.09.2018 16:00, Adam Bellemare wrote: >> >>>>>>> > > Hi Jan >> >>>>>>> > > >> >>>>>>> > > I have included a diagram of what I attempted on the >> KIP. >> >>>>>>> > > >> >>>>>>> > >> >>>>>>> >> https://cwiki.apache.org/confluence/display/KAFKA/KIP-213+Su >> >>>>>>> pport+non-key+joining+in+KTable#KIP-213Supportnon-keyjoining >> >>>>>>> inKTable-GroupBy+Reduce/Aggregate >> >>>>>>> < >> https://cwiki.apache.org/confluence/display/KAFKA/KIP-213+S >> >>>>>>> upport+non-key+joining+in+KTable#KIP-213Supportnon-keyjoinin >> >>>>>>> ginKTable-GroupBy+Reduce/Aggregate> >> >>>>>>> > > >> >>>>>>> > > I attempted this back at the start of my own >> implementation >> >>>>>>> of >> >>>>>>> this >> >>>>>>> > > solution, and since I could not get it to work I have >> since >> >>>>>>> discarded the >> >>>>>>> > > code. At this point in time, if you wish to continue >> pursuing >> >>>>>>> for your >> >>>>>>> > > groupBy solution, I ask that you please create a >> diagram on >> >>>>>>> the KIP >> >>>>>>> > > carefully explaining your solution. Please feel free to >> use >> >>>>>>> the image I >> >>>>>>> > > just posted as a starting point. I am having trouble >> >>>>>>> understanding your >> >>>>>>> > > explanations but I think that a carefully constructed >> diagram >> >>>>>>> will clear >> >>>>>>> > up >> >>>>>>> > > any misunderstandings. Alternately, please post a >> >>>>>>> comprehensive PR with >> >>>>>>> > > your solution. I can only guess at what you mean, and >> since I >> >>>>>>> value my >> >>>>>>> > own >> >>>>>>> > > time as much as you value yours, I believe it is your >> >>>>>>> responsibility to >> >>>>>>> > > provide an implementation instead of me trying to guess. >> >>>>>>> > > >> >>>>>>> > > Adam >> >>>>>>> > > >> >>>>>>> > > >> >>>>>>> > > >> >>>>>>> > > >> >>>>>>> > > >> >>>>>>> > > >> >>>>>>> > > >> >>>>>>> > > >> >>>>>>> > > >> >>>>>>> > > On Sat, Sep 8, 2018 at 8:00 AM, Jan Filipiak >> >>>>>>> <jan.filip...@trivago.com <mailto:jan.filip...@trivago.com >> >> >> >>>>>>> >> >>>>>>> > > wrote: >> >>>>>>> > > >> >>>>>>> > >> Hi James, >> >>>>>>> > >> >> >>>>>>> > >> nice to see you beeing interested. kafka streams at >> this >> >>>>>>> point supports >> >>>>>>> > >> all sorts of joins as long as both streams have the >> same >> >>>>>>> key. >> >>>>>>> > >> Adam is currently implementing a join where a KTable >> and a >> >>>>>>> KTable can >> >>>>>>> > have >> >>>>>>> > >> a one to many relation ship (1:n). We exploit that >> rocksdb >> >>>>>>> is >> >>>>>>> >> >>>>>> a >> >>>> >> >>>>> > >> datastore that keeps data sorted (At least exposes an >> API to >> >>>>>>> access the >> >>>>>>> > >> stored data in a sorted fashion). >> >>>>>>> > >> >> >>>>>>> > >> I think the technical caveats are well understood now >> and we >> >>>>>>> >> >>>>>> are >> >>>> >> >>>>> > basically >> >>>>>>> > >> down to philosophy and API Design ( when Adam sees my >> newest >> >>>>>>> message). >> >>>>>>> > >> I have a lengthy track record of loosing those kinda >> >>>>>>> arguments within >> >>>>>>> > the >> >>>>>>> > >> streams community and I have no clue why. So I >> literally >> >>>>>>> can't wait for >> >>>>>>> > you >> >>>>>>> > >> to churn through this thread and give you opinion on >> how we >> >>>>>>> should >> >>>>>>> > design >> >>>>>>> > >> the return type of the oneToManyJoin and how many >> power we >> >>>>>>> want to give >> >>>>>>> > to >> >>>>>>> > >> the user vs "simplicity" (where simplicity isn't >> really that >> >>>>>>> as users >> >>>>>>> > still >> >>>>>>> > >> need to understand it I argue) >> >>>>>>> > >> >> >>>>>>> > >> waiting for you to join in on the discussion >> >>>>>>> > >> >> >>>>>>> > >> Best Jan >> >>>>>>> > >> >> >>>>>>> > >> >> >>>>>>> > >> >> >>>>>>> > >> On 07.09.2018 15:49, James Kwan wrote: >> >>>>>>> > >> >> >>>>>>> > >>> I am new to this group and I found this subject >> >>>>>>> interesting. Sounds >> >>>>>>> > like >> >>>>>>> > >>> you guys want to implement a join table of two >> streams? Is >> >>>>>>> there >> >>>>>>> > somewhere >> >>>>>>> > >>> I can see the original requirement or proposal? >> >>>>>>> > >>> >> >>>>>>> > >>> On Sep 7, 2018, at 8:13 AM, Jan Filipiak >> >>>>>>> <jan.filip...@trivago.com <mailto:jan.filip...@trivago.com >> >> >> >>>>>>> >> >>>>>>> > >>>> wrote: >> >>>>>>> > >>>> >> >>>>>>> > >>>> >> >>>>>>> > >>>> On 05.09.2018 22:17, Adam Bellemare wrote: >> >>>>>>> > >>>> >> >>>>>>> > >>>>> I'm currently testing using a Windowed Store to >> store the >> >>>>>>> highwater >> >>>>>>> > >>>>> mark. >> >>>>>>> > >>>>> By all indications this should work fine, with the >> caveat >> >>>>>>> being that >> >>>>>>> > it >> >>>>>>> > >>>>> can >> >>>>>>> > >>>>> only resolve out-of-order arrival for up to the >> size of >> >>>>>>> the window >> >>>>>>> > (ie: >> >>>>>>> > >>>>> 24h, 72h, etc). This would remove the possibility >> of it >> >>>>>>> >> >>>>>> being >> >>>> >> >>>>> > unbounded >> >>>>>>> > >>>>> in >> >>>>>>> > >>>>> size. >> >>>>>>> > >>>>> >> >>>>>>> > >>>>> With regards to Jan's suggestion, I believe this is >> where >> >>>>>>> we will >> >>>>>>> > have >> >>>>>>> > >>>>> to >> >>>>>>> > >>>>> remain in disagreement. While I do not disagree >> with your >> >>>>>>> statement >> >>>>>>> > >>>>> about >> >>>>>>> > >>>>> there likely to be additional joins done in a >> real-world >> >>>>>>> workflow, I >> >>>>>>> > do >> >>>>>>> > >>>>> not >> >>>>>>> > >>>>> see how you can conclusively deal with out-of-order >> >>>>>>> arrival >> >>>>>>> of >> >>>>>>> > >>>>> foreign-key >> >>>>>>> > >>>>> changes and subsequent joins. I have attempted what >> I >> >>>>>>> think you have >> >>>>>>> > >>>>> proposed (without a high-water, using groupBy and >> reduce) >> >>>>>>> and found >> >>>>>>> > >>>>> that if >> >>>>>>> > >>>>> the foreign key changes too quickly, or the load on >> a >> >>>>>>> stream thread >> >>>>>>> > is >> >>>>>>> > >>>>> too >> >>>>>>> > >>>>> high, the joined messages will arrive out-of-order >> and be >> >>>>>>> incorrectly >> >>>>>>> > >>>>> propagated, such that an intermediate event is >> >>>>>>> represented >> >>>>>>> as the >> >>>>>>> > final >> >>>>>>> > >>>>> event. >> >>>>>>> > >>>>> >> >>>>>>> > >>>> Can you shed some light on your groupBy >> implementation. >> >>>>>>> There must be >> >>>>>>> > >>>> some sort of flaw in it. >> >>>>>>> > >>>> I have a suspicion where it is, I would just like to >> >>>>>>> confirm. The idea >> >>>>>>> > >>>> is bullet proof and it must be >> >>>>>>> > >>>> an implementation mess up. I would like to clarify >> before >> >>>>>>> we draw a >> >>>>>>> > >>>> conclusion. >> >>>>>>> > >>>> >> >>>>>>> > >>>> Repartitioning the scattered events back to their >> >>>>>>> >> >>>>>> original >> >>>> >> >>>>> > >>>>> partitions is the only way I know how to conclusively >> deal >> >>>>>>> with >> >>>>>>> > >>>>> out-of-order events in a given time frame, and to >> ensure >> >>>>>>> that the >> >>>>>>> > data >> >>>>>>> > >>>>> is >> >>>>>>> > >>>>> eventually consistent with the input events. >> >>>>>>> > >>>>> >> >>>>>>> > >>>>> If you have some code to share that illustrates your >> >>>>>>> approach, I >> >>>>>>> > would >> >>>>>>> > >>>>> be >> >>>>>>> > >>>>> very grateful as it would remove any >> misunderstandings >> >>>>>>> that I may >> >>>>>>> > have. >> >>>>>>> > >>>>> >> >>>>>>> > >>>> ah okay you were looking for my code. I don't have >> >>>>>>> something easily >> >>>>>>> > >>>> readable here as its bloated with OO-patterns. >> >>>>>>> > >>>> >> >>>>>>> > >>>> its anyhow trivial: >> >>>>>>> > >>>> >> >>>>>>> > >>>> @Override >> >>>>>>> > >>>> public T apply(K aggKey, V value, T aggregate) >> >>>>>>> > >>>> { >> >>>>>>> > >>>> Map<U, V> currentStateAsMap = >> asMap(aggregate); >> >>>>>>> << >> >>>>>>> imaginary >> >>>>>>> > >>>> U toModifyKey = mapper.apply(value); >> >>>>>>> > >>>> << this is the place where people >> actually >> >>>>>>> gonna have >> >>>>>>> > issues >> >>>>>>> > >>>> and why you probably couldn't do it. we would need >> to find >> >>>>>>> a solution >> >>>>>>> > here. >> >>>>>>> > >>>> I didn't realize that yet. >> >>>>>>> > >>>> << we propagate the field in the >> joiner, so >> >>>>>>> that we can >> >>>>>>> > pick >> >>>>>>> > >>>> it up in an aggregate. Probably you have not thought >> of >> >>>>>>> this in your >> >>>>>>> > >>>> approach right? >> >>>>>>> > >>>> << I am very open to find a generic >> solution >> >>>>>>> here. In my >> >>>>>>> > >>>> honest opinion this is broken in KTableImpl.GroupBy >> that >> >>>>>>> it >> >>>>>>> looses >> >>>>>>> > the keys >> >>>>>>> > >>>> and only maintains the aggregate key. >> >>>>>>> > >>>> << I abstracted it away back then way >> before >> >>>>>>> i >> >>>>>>> was >> >>>>>>> > thinking >> >>>>>>> > >>>> of oneToMany join. That is why I didn't realize its >> >>>>>>> significance here. >> >>>>>>> > >>>> << Opinions? >> >>>>>>> > >>>> >> >>>>>>> > >>>> for (V m : current) >> >>>>>>> > >>>> { >> >>>>>>> > >>>> currentStateAsMap.put(mapper.apply(m), m); >> >>>>>>> > >>>> } >> >>>>>>> > >>>> if (isAdder) >> >>>>>>> > >>>> { >> >>>>>>> > >>>> currentStateAsMap.put(toModifyKey, value); >> >>>>>>> > >>>> } >> >>>>>>> > >>>> else >> >>>>>>> > >>>> { >> >>>>>>> > >>>> currentStateAsMap.remove(toModifyKey); >> >>>>>>> > >>>> if(currentStateAsMap.isEmpty()){ >> >>>>>>> > >>>> return null; >> >>>>>>> > >>>> } >> >>>>>>> > >>>> } >> >>>>>>> > >>>> retrun asAggregateType(currentStateAsMap) >> >>>>>>> > >>>> } >> >>>>>>> > >>>> >> >>>>>>> > >>>> >> >>>>>>> > >>>> >> >>>>>>> > >>>> >> >>>>>>> > >>>> >> >>>>>>> > >>>> Thanks, >> >>>>>>> > >>>>> Adam >> >>>>>>> > >>>>> >> >>>>>>> > >>>>> >> >>>>>>> > >>>>> >> >>>>>>> > >>>>> >> >>>>>>> > >>>>> >> >>>>>>> > >>>>> On Wed, Sep 5, 2018 at 3:35 PM, Jan Filipiak < >> >>>>>>> > jan.filip...@trivago.com <mailto:jan.filip...@trivago.com >> >> >> >>>>>>> >> >>>>>>> > >>>>> wrote: >> >>>>>>> > >>>>> >> >>>>>>> > >>>>> Thanks Adam for bringing Matthias to speed! >> >>>>>>> > >>>>>> about the differences. I think re-keying back >> should be >> >>>>>>> optional at >> >>>>>>> > >>>>>> best. >> >>>>>>> > >>>>>> I would say we return a KScatteredTable with >> reshuffle() >> >>>>>>> returning >> >>>>>>> > >>>>>> KTable<originalKey,Joined> to make the backwards >> >>>>>>> repartitioning >> >>>>>>> > >>>>>> optional. >> >>>>>>> > >>>>>> I am also in a big favour of doing the out of order >> >>>>>>> processing using >> >>>>>>> > >>>>>> group >> >>>>>>> > >>>>>> by instead high water mark tracking. >> >>>>>>> > >>>>>> Just because unbounded growth is just scary + It >> saves >> >>>>>>> us >> >>>>>>> the header >> >>>>>>> > >>>>>> stuff. >> >>>>>>> > >>>>>> >> >>>>>>> > >>>>>> I think the abstraction of always repartitioning >> back is >> >>>>>>> just not so >> >>>>>>> > >>>>>> strong. Like the work has been done before we >> partition >> >>>>>>> back and >> >>>>>>> > >>>>>> grouping >> >>>>>>> > >>>>>> by something else afterwards is really common. >> >>>>>>> > >>>>>> >> >>>>>>> > >>>>>> >> >>>>>>> > >>>>>> >> >>>>>>> > >>>>>> >> >>>>>>> > >>>>>> >> >>>>>>> > >>>>>> >> >>>>>>> > >>>>>> On 05.09.2018 13:49, Adam Bellemare wrote: >> >>>>>>> > >>>>>> >> >>>>>>> > >>>>>> Hi Matthias >> >>>>>>> > >>>>>>> Thank you for your feedback, I do appreciate it! >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> While name spacing would be possible, it would >> require >> >>>>>>> to >> >>>>>>> > deserialize >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>>> user headers what implies a runtime overhead. I >> would >> >>>>>>> suggest to >> >>>>>>> > no >> >>>>>>> > >>>>>>>> namespace for now to avoid the overhead. If this >> >>>>>>> >> >>>>>> becomes a >> >>>> >> >>>>> > problem in >> >>>>>>> > >>>>>>>> the future, we can still add name spacing later >> on. >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> Agreed. I will go with using a reserved string >> and >> >>>>>>> document it. >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> My main concern about the design it the type of >> the >> >>>>>>> result KTable: >> >>>>>>> > If >> >>>>>>> > >>>>>>> I >> >>>>>>> > >>>>>>> understood the proposal correctly, >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> In your example, you have table1 and table2 >> swapped. >> >>>>>>> Here is how it >> >>>>>>> > >>>>>>> works >> >>>>>>> > >>>>>>> currently: >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> 1) table1 has the records that contain the >> foreign key >> >>>>>>> within their >> >>>>>>> > >>>>>>> value. >> >>>>>>> > >>>>>>> table1 input stream: <a,(fk=A,bar=1)>, >> >>>>>>> <b,(fk=A,bar=2)>, >> >>>>>>> > >>>>>>> <c,(fk=B,bar=3)> >> >>>>>>> > >>>>>>> table2 input stream: <A,X>, <B,Y> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> 2) A Value mapper is required to extract the >> foreign >> >>>>>>> key. >> >>>>>>> > >>>>>>> table1 foreign key mapper: ( value => value.fk >> >>>>>>> <http://value.fk> ) >> >>>>>>> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> The mapper is applied to each element in table1, >> and a >> >>>>>>> new combined >> >>>>>>> > >>>>>>> key is >> >>>>>>> > >>>>>>> made: >> >>>>>>> > >>>>>>> table1 mapped: <A-a, (fk=A,bar=1)>, <A-b, >> >>>>>>> (fk=A,bar=2)>, >> >>>>>>> <B-c, >> >>>>>>> > >>>>>>> (fk=B,bar=3)> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> 3) The rekeyed events are copartitioned with >> table2: >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> a) Stream Thread with Partition 0: >> >>>>>>> > >>>>>>> RepartitionedTable1: <A-a, (fk=A,bar=1)>, <A-b, >> >>>>>>> (fk=A,bar=2)> >> >>>>>>> > >>>>>>> Table2: <A,X> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> b) Stream Thread with Partition 1: >> >>>>>>> > >>>>>>> RepartitionedTable1: <B-c, (fk=B,bar=3)> >> >>>>>>> > >>>>>>> Table2: <B,Y> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> 4) From here, they can be joined together locally >> by >> >>>>>>> applying the >> >>>>>>> > >>>>>>> joiner >> >>>>>>> > >>>>>>> function. >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> At this point, Jan's design and my design >> deviate. My >> >>>>>>> design goes >> >>>>>>> > on >> >>>>>>> > >>>>>>> to >> >>>>>>> > >>>>>>> repartition the data post-join and resolve >> out-of-order >> >>>>>>> arrival of >> >>>>>>> > >>>>>>> records, >> >>>>>>> > >>>>>>> finally returning the data keyed just the >> original key. >> >>>>>>> I do not >> >>>>>>> > >>>>>>> expose >> >>>>>>> > >>>>>>> the >> >>>>>>> > >>>>>>> CombinedKey or any of the internals outside of the >> >>>>>>> joinOnForeignKey >> >>>>>>> > >>>>>>> function. This does make for larger footprint, >> but it >> >>>>>>> removes all >> >>>>>>> > >>>>>>> agency >> >>>>>>> > >>>>>>> for resolving out-of-order arrivals and handling >> >>>>>>> CombinedKeys from >> >>>>>>> > the >> >>>>>>> > >>>>>>> user. I believe that this makes the function much >> >>>>>>> easier >> >>>>>>> to use. >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> Let me know if this helps resolve your questions, >> and >> >>>>>>> please feel >> >>>>>>> > >>>>>>> free to >> >>>>>>> > >>>>>>> add anything else on your mind. >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> Thanks again, >> >>>>>>> > >>>>>>> Adam >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> On Tue, Sep 4, 2018 at 8:36 PM, Matthias J. Sax < >> >>>>>>> > >>>>>>> matth...@confluent.io <mailto: >> matth...@confluent.io>> >> >>>>>>> >> >>>>>>> > >>>>>>> wrote: >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>> Hi, >> >>>>>>> > >>>>>>> >> >>>>>>> > >>>>>>>> I am just catching up on this thread. I did not >> read >> >>>>>>> everything so >> >>>>>>> > >>>>>>>> far, >> >>>>>>> > >>>>>>>> but want to share couple of initial thoughts: >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> Headers: I think there is a fundamental >> difference >> >>>>>>> between header >> >>>>>>> > >>>>>>>> usage >> >>>>>>> > >>>>>>>> in this KIP and KP-258. For 258, we add headers >> to >> >>>>>>> changelog topic >> >>>>>>> > >>>>>>>> that >> >>>>>>> > >>>>>>>> are owned by Kafka Streams and nobody else is >> supposed >> >>>>>>> to write >> >>>>>>> > into >> >>>>>>> > >>>>>>>> them. In fact, no user header are written into >> the >> >>>>>>> changelog topic >> >>>>>>> > >>>>>>>> and >> >>>>>>> > >>>>>>>> thus, there are not conflicts. >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> Nevertheless, I don't see a big issue with using >> >>>>>>> headers within >> >>>>>>> > >>>>>>>> Streams. >> >>>>>>> > >>>>>>>> As long as we document it, we can have some >> "reserved" >> >>>>>>> header keys >> >>>>>>> > >>>>>>>> and >> >>>>>>> > >>>>>>>> users are not allowed to use when processing >> data with >> >>>>>>> Kafka >> >>>>>>> > Streams. >> >>>>>>> > >>>>>>>> IMHO, this should be ok. >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> I think there is a safe way to avoid conflicts, >> since >> >>>>>>> these >> >>>>>>> > headers >> >>>>>>> > >>>>>>>> are >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>>> only needed in internal topics (I think): >> >>>>>>> > >>>>>>>>> For internal and changelog topics, we can >> namespace >> >>>>>>> all headers: >> >>>>>>> > >>>>>>>>> * user-defined headers are namespaced as >> "external." >> >>>>>>> + >> >>>>>>> headerKey >> >>>>>>> > >>>>>>>>> * internal headers are namespaced as >> "internal." + >> >>>>>>> headerKey >> >>>>>>> > >>>>>>>>> >> >>>>>>> > >>>>>>>>> While name spacing would be possible, it would >> >>>>>>> require >> >>>>>>> >> >>>>>> to >> >>>> >> >>>>> > >>>>>>>> deserialize >> >>>>>>> > >>>>>>>> user headers what implies a runtime overhead. I >> would >> >>>>>>> suggest to >> >>>>>>> > no >> >>>>>>> > >>>>>>>> namespace for now to avoid the overhead. If this >> >>>>>>> >> >>>>>> becomes a >> >>>> >> >>>>> > problem in >> >>>>>>> > >>>>>>>> the future, we can still add name spacing later >> on. >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> My main concern about the design it the type of >> the >> >>>>>>> result KTable: >> >>>>>>> > >>>>>>>> If I >> >>>>>>> > >>>>>>>> understood the proposal correctly, >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> KTable<K1,V1> table1 = ... >> >>>>>>> > >>>>>>>> KTable<K2,V2> table2 = ... >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> KTable<K1,V3> joinedTable = >> table1.join(table2,...); >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> implies that the `joinedTable` has the same key >> as the >> >>>>>>> left input >> >>>>>>> > >>>>>>>> table. >> >>>>>>> > >>>>>>>> IMHO, this does not work because if table2 >> contains >> >>>>>>> multiple rows >> >>>>>>> > >>>>>>>> that >> >>>>>>> > >>>>>>>> join with a record in table1 (what is the main >> purpose >> >>>>>>> >> >>>>>> of >> >>>> >> >>>>> a >> >>>>>>> > foreign >> >>>>>>> > >>>>>>>> key >> >>>>>>> > >>>>>>>> join), the result table would only contain a >> single >> >>>>>>> join result, >> >>>>>>> > but >> >>>>>>> > >>>>>>>> not >> >>>>>>> > >>>>>>>> multiple. >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> Example: >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> table1 input stream: <A,X> >> >>>>>>> > >>>>>>>> table2 input stream: <a,(A,1)>, <b,(A,2)> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> We use table2 value a foreign key to table1 key >> (ie, >> >>>>>>> "A" joins). >> >>>>>>> > If >> >>>>>>> > >>>>>>>> the >> >>>>>>> > >>>>>>>> result key is the same key as key of table1, this >> >>>>>>> implies that the >> >>>>>>> > >>>>>>>> result can either be <A, join(X,1)> or <A, >> join(X,2)> >> >>>>>>> but not >> >>>>>>> > both. >> >>>>>>> > >>>>>>>> Because the share the same key, whatever result >> record >> >>>>>>> we emit >> >>>>>>> > later, >> >>>>>>> > >>>>>>>> overwrite the previous result. >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> This is the reason why Jan originally proposed >> to use >> >>>>>>> a >> >>>>>>> > combination >> >>>>>>> > >>>>>>>> of >> >>>>>>> > >>>>>>>> both primary keys of the input tables as key of >> the >> >>>>>>> output table. >> >>>>>>> > >>>>>>>> This >> >>>>>>> > >>>>>>>> makes the keys of the output table unique and we >> can >> >>>>>>> store both in >> >>>>>>> > >>>>>>>> the >> >>>>>>> > >>>>>>>> output table: >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> Result would be <A-a, join(X,1)>, <A-b, >> join(X,2)> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> Thoughts? >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> -Matthias >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> On 9/4/18 1:36 PM, Jan Filipiak wrote: >> >>>>>>> > >>>>>>>> >> >>>>>>> > >>>>>>>> Just on remark here. >> >>>>>>> > >>>>>>>>> The high-watermark could be disregarded. The >> decision >> >>>>>>> about the >> >>>>>>> > >>>>>>>>> forward >> >>>>>>> > >>>>>>>>> depends on the size of the aggregated map. >> >>>>>>> > >>>>>>>>> Only 1 element long maps would be unpacked and >> >>>>>>> forwarded. 0 >> >>>>>>> > element >> >>>>>>> > >>>>>>>>> maps >> >>>>>>> > >>>>>>>>> would be published as delete. Any other count >> >>>>>>> > >>>>>>>>> of map entries is in "waiting for correct >> deletes to >> >>>>>>> > arrive"-state. >> >>>>>>> > >>>>>>>>> >> >>>>>>> > >>>>>>>>> On 04.09.2018 21:29, Adam Bellemare wrote: >> >>>>>>> > >>>>>>>>> >> >>>>>>> > >>>>>>>>> It does look like I could replace the second >> >>>>>>> repartition store >> >>>>>>> > and >> >>>>>>> > >>>>>>>>>> highwater store with a groupBy and reduce. >> However, >> >>>>>>> it looks >> >>>>>>> > like >> >>>>>>> > >>>>>>>>>> I >> >>>>>>> > >>>>>>>>>> would >> >>>>>>> > >>>>>>>>>> still need to store the highwater value within >> the >> >>>>>>> materialized >> >>>>>>> > >>>>>>>>>> store, >> >>>>>>> > >>>>>>>>>> >> >>>>>>> > >>>>>>>>>> to >> >>>>>>> > >>>>>>>>> compare the arrival of out-of-order records >> (assuming >> >>>>>>> >> >>>>>> my >> >>>> >> >>>>> > >>>>>>>>> understanding >> >>>>>>> > >>>>>>>>> of >> >>>>>>> > >>>>>>>>> THIS is correct...). This in effect is the same >> as >> >>>>>>> the >> >>>>>>> design I >> >>>>>>> > have >> >>>>>>> > >>>>>>>>> now, >> >>>>>>> > >>>>>>>>> just with the two tables merged together. >> >>>>>>> > >>>>>>>>> >> >>>>>>> > >> >>>>>>> > >> >>>>>>> >> >>>>>>> >> >>>>>>> >> >>>>>>> >> >>>>>> >> >>>>> >> >>>>> -- >> >>>>> -- Guozhang >> >>>>> >> >>>>> >> >>>> >> >>>> >> >>>> -- >> >>>> -- Guozhang >> >>>> >> >>>> >> >>> >> >> >> > >> >
