1) I believe that the resolution mechanism John has proposed is sufficient - it is clean and easy and doesn't require additional RocksDB stores, which reduces the footprint greatly. I don't think we need to resolve based on timestamp or offset anymore, but if we decide to do to that would be within the bounds of the existing API.
2) Is the current API sufficient, or does it need to be altered to go back to vote? 3) KScatteredTable implementation can always be added in a future revision. This API does not rule it out. This implementation of this function would simply be replaced with `KScatteredTable.resolve()` while still maintaining the existing API, thereby giving both features as Jan outlined earlier. Would this work? Thanks Guozhang, John and Jan On Mon, Dec 10, 2018 at 10:39 AM John Roesler <j...@confluent.io> wrote: > Hi, all, > > >> In fact, we > >> can just keep a single final-result store with timestamps and reject > values > >> that have a smaller timestamp, is that right? > > > Which is the correct output should at least be decided on the offset of > > the original message. > > Thanks for this point, Jan. > > KIP-258 is merely to allow embedding the record timestamp in the k/v > store, > as well as providing a storage-format upgrade path. > > I might have missed it, but I think we have yet to discuss whether it's > safe > or desirable just to swap topic-ordering our for timestamp-ordering. This > is > a very deep topic, and I think it would only pollute the current > discussion. > > What Adam has proposed is safe, given the *current* ordering semantics > of the system. If we can agree on his proposal, I think we can merge the > feature well before the conversation about timestamp ordering even takes > place, much less reaches a conclusion. In the mean time, it would seem to > be unfortunate to have one join operator with different ordering semantics > from every other KTable operator. > > If and when that timestamp discussion takes place, many (all?) KTable > operations > will need to be updated, rendering the many:one join a small marginal cost. > > And, just to plug it again, I proposed an algorithm above that I believe > provides > correct ordering without any additional metadata, and regardless of the > ordering semantics. I didn't bring it up further, because I felt the KIP > only needs > to agree on the public API, and we can discuss the implementation at > leisure in > a PR... > > Thanks, > -John > > > On Mon, Dec 10, 2018 at 2:28 AM Jan Filipiak <jan.filip...@trivago.com> > wrote: > > > > > > > On 10.12.2018 07:42, Guozhang Wang wrote: > > > Hello Adam / Jan / John, > > > > > > Sorry for being late on this thread! I've finally got some time this > > > weekend to cleanup a load of tasks on my queue (actually I've also > > realized > > > there are a bunch of other things I need to enqueue while cleaning them > > up > > > --- sth I need to improve on my side). So here are my thoughts: > > > > > > Regarding the APIs: I like the current written API in the KIP. More > > > generally I'd prefer to keep the 1) one-to-many join functionalities as > > > well as 2) other join types than inner as separate KIPs since 1) may > > worth > > > a general API refactoring that can benefit not only foreignkey joins > but > > > collocate joins as well (e.g. an extended proposal of > > > > > > https://cwiki.apache.org/confluence/display/KAFKA/KIP-150+-+Kafka-Streams+Cogroup > > ), > > > and I'm not sure if other join types would actually be needed (maybe > left > > > join still makes sense), so it's better to > wait-for-people-to-ask-and-add > > > than add-sth-that-no-one-uses. > > > > > > Regarding whether we enforce step 3) / 4) v.s. introducing a > > > KScatteredTable for users to inject their own optimization: I'd prefer > to > > > do the current option as-is, and my main rationale is for optimization > > > rooms inside the Streams internals and the API succinctness. For > advanced > > > users who may indeed prefer KScatteredTable and do their own > > optimization, > > > while it is too much of the work to use Processor API directly, I think > > we > > > can still extend the current API to support it in the future if it > > becomes > > > necessary. > > > > no internal optimization potential. it's a myth > > > > ¯\_(ツ)_/¯ > > > > :-) > > > > > > > > Another note about step 4) resolving out-of-ordering data, as I > mentioned > > > before I think with KIP-258 (embedded timestamp with key-value store) > we > > > can actually make this step simpler than the current proposal. In fact, > > we > > > can just keep a single final-result store with timestamps and reject > > values > > > that have a smaller timestamp, is that right? > > > > Which is the correct output should at least be decided on the offset of > > the original message. > > > > > > > > > > > That's all I have in mind now. Again, great appreciation to Adam to > make > > > such HUGE progress on this KIP! > > > > > > > > > Guozhang > > > > > > On Wed, Dec 5, 2018 at 2:40 PM Jan Filipiak <jan.filip...@trivago.com> > > > wrote: > > > > > >> If they don't find the time: > > >> They usually take the opposite path from me :D > > >> so the answer would be clear. > > >> > > >> hence my suggestion to vote. > > >> > > >> > > >> On 04.12.2018 21:06, Adam Bellemare wrote: > > >>> Hi Guozhang and Matthias > > >>> > > >>> I know both of you are quite busy, but we've gotten this KIP to a > point > > >>> where we need more guidance on the API (perhaps a bit of a > tie-breaker, > > >> if > > >>> you will). If you have anyone else you may think should look at this, > > >>> please tag them accordingly. > > >>> > > >>> The scenario is as such: > > >>> > > >>> Current Option: > > >>> API: > > >>> > > >> > > > https://cwiki.apache.org/confluence/display/KAFKA/KIP-213+Support+non-key+joining+in+KTable#KIP-213Supportnon-keyjoininginKTable-PublicInterfaces > > >>> 1) Rekey the data to CombinedKey, and shuffles it to the partition > with > > >> the > > >>> foreignKey (repartition 1) > > >>> 2) Join the data > > >>> 3) Shuffle the data back to the original node (repartition 2) > > >>> 4) Resolve out-of-order arrival / race condition due to foreign-key > > >> changes. > > >>> > > >>> Alternate Option: > > >>> Perform #1 and #2 above, and return a KScatteredTable. > > >>> - It would be keyed on a wrapped key function: <CombinedKey<KO, K>, > VR> > > >> (KO > > >>> = Other Table Key, K = This Table Key, VR = Joined Result) > > >>> - KScatteredTable.resolve() would perform #3 and #4 but otherwise a > > user > > >>> would be able to perform additional functions directly from the > > >>> KScatteredTable (TBD - currently out of scope). > > >>> - John's analysis 2-emails up is accurate as to the tradeoffs. > > >>> > > >>> Current Option is coded as-is. Alternate option is possible, but will > > >>> require for implementation details to be made in the API and some > > >> exposure > > >>> of new data structures into the API (ie: CombinedKey). > > >>> > > >>> I appreciate any insight into this. > > >>> > > >>> Thanks. > > >>> > > >>> On Tue, Dec 4, 2018 at 2:59 PM Adam Bellemare < > > adam.bellem...@gmail.com> > > >>> wrote: > > >>> > > >>>> Hi John > > >>>> > > >>>> Thanks for your feedback and assistance. I think your summary is > > >> accurate > > >>>> from my perspective. Additionally, I would like to add that there > is a > > >> risk > > >>>> of inconsistent final states without performing the resolution. This > > is > > >> a > > >>>> major concern for me as most of the data I have dealt with is > produced > > >> by > > >>>> relational databases. We have seen a number of cases where a user in > > the > > >>>> Rails UI has modified the field (foreign key), realized they made a > > >>>> mistake, and then updated the field again with a new key. The events > > are > > >>>> propagated out as they are produced, and as such we have had > > real-world > > >>>> cases where these inconsistencies were propagated downstream as the > > >> final > > >>>> values due to the race conditions in the fanout of the data. > > >>>> > > >>>> This solution that I propose values correctness of the final result > > over > > >>>> other factors. > > >>>> > > >>>> We could always move this function over to using a KScatteredTable > > >>>> implementation in the future, and simply deprecate it this join API > in > > >>>> time. I think I would like to hear more from some of the other major > > >>>> committers on which course of action they would think is best before > > any > > >>>> more coding is done. > > >>>> > > >>>> Thanks again > > >>>> > > >>>> Adam > > >>>> > > >>>> > > >>>> On Mon, Dec 3, 2018 at 8:24 PM John Roesler <j...@confluent.io> > > wrote: > > >>>> > > >>>>> Hi Jan and Adam, > > >>>>> > > >>>>> Wow, thanks for doing that test, Adam. Those results are > encouraging. > > >>>>> > > >>>>> Thanks for your performance experience as well, Jan. I agree that > > >> avoiding > > >>>>> unnecessary join outputs is especially important when the fan-out > is > > so > > >>>>> high. I suppose this could also be built into the implementation > > we're > > >>>>> discussing, but it wouldn't have to be specified in the KIP (since > > >> it's an > > >>>>> API-transparent optimization). > > >>>>> > > >>>>> As far as whether or not to re-repartition the data, I didn't bring > > it > > >> up > > >>>>> because it sounded like the two of you agreed to leave the KIP > as-is, > > >>>>> despite the disagreement. > > >>>>> > > >>>>> If you want my opinion, I feel like both approaches are reasonable. > > >>>>> It sounds like Jan values more the potential for developers to > > optimize > > >>>>> their topologies to re-use the intermediate nodes, whereas Adam > > places > > >>>>> more > > >>>>> value on having a single operator that people can use without extra > > >> steps > > >>>>> at the end. > > >>>>> > > >>>>> Personally, although I do find it exceptionally annoying when a > > >> framework > > >>>>> gets in my way when I'm trying to optimize something, it seems > better > > >> to > > >>>>> go > > >>>>> for a single operation. > > >>>>> * Encapsulating the internal transitions gives us significant > > latitude > > >> in > > >>>>> the implementation (for example, joining only at the end, not in > the > > >>>>> middle > > >>>>> to avoid extra data copying and out-of-order resolution; how we > > >> represent > > >>>>> the first repartition keys (combined keys vs. value vectors), > etc.). > > >> If we > > >>>>> publish something like a KScatteredTable with the right-partitioned > > >> joined > > >>>>> data, then the API pretty much locks in the implementation as well. > > >>>>> * The API seems simpler to understand and use. I do mean "seems"; > if > > >>>>> anyone > > >>>>> wants to make the case that KScatteredTable is actually simpler, I > > >> think > > >>>>> hypothetical usage code would help. From a relational algebra > > >> perspective, > > >>>>> it seems like KTable.join(KTable) should produce a new KTable in > all > > >>>>> cases. > > >>>>> * That said, there might still be room in the API for a different > > >>>>> operation > > >>>>> like what Jan has proposed to scatter a KTable, and then do things > > like > > >>>>> join, re-group, etc from there... I'm not sure; I haven't thought > > >> through > > >>>>> all the consequences yet. > > >>>>> > > >>>>> This is all just my opinion after thinking over the discussion so > > >> far... > > >>>>> -John > > >>>>> > > >>>>> On Mon, Dec 3, 2018 at 2:56 PM Adam Bellemare < > > >> adam.bellem...@gmail.com> > > >>>>> wrote: > > >>>>> > > >>>>>> Updated the PR to take into account John's feedback. > > >>>>>> > > >>>>>> I did some preliminary testing for the performance of the > > prefixScan. > > >> I > > >>>>>> have attached the file, but I will also include the text in the > body > > >>>>> here > > >>>>>> for archival purposes (I am not sure what happens to attached > > files). > > >> I > > >>>>>> also updated the PR and the KIP accordingly. > > >>>>>> > > >>>>>> Summary: It scales exceptionally well for scanning large values of > > >>>>>> records. As Jan mentioned previously, the real issue would be more > > >>>>> around > > >>>>>> processing the resulting records after obtaining them. For > instance, > > >> it > > >>>>>> takes approximately ~80-120 mS to flush the buffer and a further > > >>>>> ~35-85mS > > >>>>>> to scan 27.5M records, obtaining matches for 2.5M of them. > Iterating > > >>>>>> through the records just to generate a simple count takes ~ 40 > times > > >>>>> longer > > >>>>>> than the flush + scan combined. > > >>>>>> > > >>>>>> > > >>>>> > > >> > > > ============================================================================================ > > >>>>>> Setup: > > >>>>>> > > >>>>>> > > >>>>> > > >> > > > ============================================================================================ > > >>>>>> Java 9 with default settings aside from a 512 MB heap (Xmx512m, > > >> Xms512m) > > >>>>>> CPU: i7 2.2 Ghz. > > >>>>>> > > >>>>>> Note: I am using a slightly-modified, directly-accessible Kafka > > >> Streams > > >>>>>> RocksDB > > >>>>>> implementation (RocksDB.java, basically just avoiding the > > >>>>>> ProcessorContext). > > >>>>>> There are no modifications to the default RocksDB values provided > in > > >> the > > >>>>>> 2.1/trunk release. > > >>>>>> > > >>>>>> > > >>>>>> keysize = 128 bytes > > >>>>>> valsize = 512 bytes > > >>>>>> > > >>>>>> Step 1: > > >>>>>> Write X positive matching events: (key = prefix + left-padded > > >>>>>> auto-incrementing integer) > > >>>>>> Step 2: > > >>>>>> Write 10X negative matching events (key = left-padded > > >> auto-incrementing > > >>>>>> integer) > > >>>>>> Step 3: > > >>>>>> Perform flush > > >>>>>> Step 4: > > >>>>>> Perform prefixScan > > >>>>>> Step 5: > > >>>>>> Iterate through return Iterator and validate the count of expected > > >>>>> events. > > >>>>>> > > >>>>>> > > >>>>>> > > >>>>> > > >> > > > ============================================================================================ > > >>>>>> Results: > > >>>>>> > > >>>>>> > > >>>>> > > >> > > > ============================================================================================ > > >>>>>> X = 1k (11k events total) > > >>>>>> Flush Time = 39 mS > > >>>>>> Scan Time = 7 mS > > >>>>>> 6.9 MB disk > > >>>>>> > > >>>>>> > > >>>>> > > >> > > > -------------------------------------------------------------------------------------------- > > >>>>>> X = 10k (110k events total) > > >>>>>> Flush Time = 45 mS > > >>>>>> Scan Time = 8 mS > > >>>>>> 127 MB > > >>>>>> > > >>>>>> > > >>>>> > > >> > > > -------------------------------------------------------------------------------------------- > > >>>>>> X = 100k (1.1M events total) > > >>>>>> Test1: > > >>>>>> Flush Time = 60 mS > > >>>>>> Scan Time = 12 mS > > >>>>>> 678 MB > > >>>>>> > > >>>>>> Test2: > > >>>>>> Flush Time = 45 mS > > >>>>>> Scan Time = 7 mS > > >>>>>> 576 MB > > >>>>>> > > >>>>>> > > >>>>> > > >> > > > -------------------------------------------------------------------------------------------- > > >>>>>> X = 1MB (11M events total) > > >>>>>> Test1: > > >>>>>> Flush Time = 52 mS > > >>>>>> Scan Time = 19 mS > > >>>>>> 7.2 GB > > >>>>>> > > >>>>>> Test2: > > >>>>>> Flush Time = 84 mS > > >>>>>> Scan Time = 34 mS > > >>>>>> 9.1 GB > > >>>>>> > > >>>>>> > > >>>>> > > >> > > > -------------------------------------------------------------------------------------------- > > >>>>>> X = 2.5M (27.5M events total) > > >>>>>> Test1: > > >>>>>> Flush Time = 82 mS > > >>>>>> Scan Time = 63 mS > > >>>>>> 17GB - 276 sst files > > >>>>>> > > >>>>>> Test2: > > >>>>>> Flush Time = 116 mS > > >>>>>> Scan Time = 35 mS > > >>>>>> 23GB - 361 sst files > > >>>>>> > > >>>>>> Test3: > > >>>>>> Flush Time = 103 mS > > >>>>>> Scan Time = 82 mS > > >>>>>> 19 GB - 300 sst files > > >>>>>> > > >>>>>> > > >>>>> > > >> > > > -------------------------------------------------------------------------------------------- > > >>>>>> > > >>>>>> I had to limit my testing on my laptop to X = 2.5M events. I tried > > to > > >> go > > >>>>>> to X = 10M (110M events) but RocksDB was going into the 100GB+ > range > > >>>>> and my > > >>>>>> laptop ran out of disk. More extensive testing could be done but I > > >>>>> suspect > > >>>>>> that it would be in line with what we're seeing in the results > > above. > > >>>>>> > > >>>>>> > > >>>>>> > > >>>>>> > > >>>>>> > > >>>>>> > > >>>>>> At this point in time, I think the only major discussion point is > > >> really > > >>>>>> around what Jan and I have disagreed on: repartitioning back + > > >> resolving > > >>>>>> potential out of order issues or leaving that up to the client to > > >>>>> handle. > > >>>>>> > > >>>>>> > > >>>>>> Thanks folks, > > >>>>>> > > >>>>>> Adam > > >>>>>> > > >>>>>> > > >>>>>> On Mon, Dec 3, 2018 at 4:34 AM Jan Filipiak < > > jan.filip...@trivago.com > > >>> > > >>>>>> wrote: > > >>>>>> > > >>>>>>> > > >>>>>>> > > >>>>>>> On 29.11.2018 15:14, John Roesler wrote: > > >>>>>>>> Hi all, > > >>>>>>>> > > >>>>>>>> Sorry that this discussion petered out... I think the 2.1 > release > > >>>>>>> caused an > > >>>>>>>> extended distraction that pushed it off everyone's radar (which > > was > > >>>>>>>> precisely Adam's concern). Personally, I've also had some extend > > >>>>>>>> distractions of my own that kept (and continue to keep) me > > >>>>> preoccupied. > > >>>>>>>> > > >>>>>>>> However, calling for a vote did wake me up, so I guess Jan was > on > > >> the > > >>>>>>> right > > >>>>>>>> track! > > >>>>>>>> > > >>>>>>>> I've gone back and reviewed the whole KIP document and the prior > > >>>>>>>> discussion, and I'd like to offer a few thoughts: > > >>>>>>>> > > >>>>>>>> API Thoughts: > > >>>>>>>> > > >>>>>>>> 1. If I read the KIP right, you are proposing a many-to-one > join. > > >>>>> Could > > >>>>>>> we > > >>>>>>>> consider naming it manyToOneJoin? Or, if you prefer, flip the > > design > > >>>>>>> around > > >>>>>>>> and make it a oneToManyJoin? > > >>>>>>>> > > >>>>>>>> The proposed name "joinOnForeignKey" disguises the join type, > and > > it > > >>>>>>> seems > > >>>>>>>> like it might trick some people into using it for a one-to-one > > join. > > >>>>>>> This > > >>>>>>>> would work, of course, but it would be super inefficient > compared > > to > > >>>>> a > > >>>>>>>> simple rekey-and-join. > > >>>>>>>> > > >>>>>>>> 2. I might have missed it, but I don't think it's specified > > whether > > >>>>>>> it's an > > >>>>>>>> inner, outer, or left join. I'm guessing an outer join, as > > >>>>> (neglecting > > >>>>>>> IQ), > > >>>>>>>> the rest can be achieved by filtering or by handling it in the > > >>>>>>> ValueJoiner. > > >>>>>>>> > > >>>>>>>> 3. The arg list to joinOnForeignKey doesn't look quite right. > > >>>>>>>> 3a. Regarding Serialized: There are a few different paradigms in > > >>>>> play in > > >>>>>>>> the Streams API, so it's confusing, but instead of three > > Serialized > > >>>>>>> args, I > > >>>>>>>> think it would be better to have one that allows (optionally) > > >> setting > > >>>>>>> the 4 > > >>>>>>>> incoming serdes. The result serde is defined by the > Materialized. > > >> The > > >>>>>>>> incoming serdes can be optional because they might already be > > >>>>> available > > >>>>>>> on > > >>>>>>>> the source KTables, or the default serdes from the config might > be > > >>>>>>>> applicable. > > >>>>>>>> > > >>>>>>>> 3b. Is the StreamPartitioner necessary? The other joins don't > > allow > > >>>>>>> setting > > >>>>>>>> one, and it seems like it might actually be harmful, since the > > rekey > > >>>>>>>> operation needs to produce results that are co-partitioned with > > the > > >>>>>>> "other" > > >>>>>>>> KTable. > > >>>>>>>> > > >>>>>>>> 4. I'm fine with the "reserved word" header, but I didn't > actually > > >>>>>>> follow > > >>>>>>>> what Matthias meant about namespacing requiring "deserializing" > > the > > >>>>>>> record > > >>>>>>>> header. The headers are already Strings, so I don't think that > > >>>>>>>> deserialization is required. If we applied the namespace at > source > > >>>>> nodes > > >>>>>>>> and stripped it at sink nodes, this would be practically no > > >> overhead. > > >>>>>>> The > > >>>>>>>> advantage of the namespace idea is that no public API change wrt > > >>>>> headers > > >>>>>>>> needs to happen, and no restrictions need to be placed on users' > > >>>>>>> headers. > > >>>>>>>> > > >>>>>>>> (Although I'm wondering if we can get away without the header at > > >>>>> all... > > >>>>>>>> stay tuned) > > >>>>>>>> > > >>>>>>>> 5. I also didn't follow the discussion about the HWM table > growing > > >>>>>>> without > > >>>>>>>> bound. As I read it, the HWM table is effectively implementing > OCC > > >> to > > >>>>>>>> resolve the problem you noted with disordering when the rekey is > > >>>>>>>> reversed... particularly notable when the FK changes. As such, > it > > >>>>> only > > >>>>>>>> needs to track the most recent "version" (the offset in the > source > > >>>>>>>> partition) of each key. Therefore, it should have the same > number > > of > > >>>>>>> keys > > >>>>>>>> as the source table at all times. > > >>>>>>>> > > >>>>>>>> I see that you are aware of KIP-258, which I think might be > > relevant > > >>>>> in > > >>>>>>> a > > >>>>>>>> couple of ways. One: it's just about storing the timestamp in > the > > >>>>> state > > >>>>>>>> store, but the ultimate idea is to effectively use the timestamp > > as > > >>>>> an > > >>>>>>> OCC > > >>>>>>>> "version" to drop disordered updates. You wouldn't want to use > the > > >>>>>>>> timestamp for this operation, but if you were to use a similar > > >>>>>>> mechanism to > > >>>>>>>> store the source offset in the store alongside the re-keyed > > values, > > >>>>> then > > >>>>>>>> you could avoid a separate table. > > >>>>>>>> > > >>>>>>>> 6. You and Jan have been thinking about this for a long time, so > > >> I've > > >>>>>>>> probably missed something here, but I'm wondering if we can > avoid > > >> the > > >>>>>>> HWM > > >>>>>>>> tracking at all and resolve out-of-order during a final join > > >>>>> instead... > > >>>>>>>> > > >>>>>>>> Let's say we're joining a left table (Integer K: Letter FK, > (other > > >>>>>>> data)) > > >>>>>>>> to a right table (Letter K: (some data)). > > >>>>>>>> > > >>>>>>>> Left table: > > >>>>>>>> 1: (A, xyz) > > >>>>>>>> 2: (B, asd) > > >>>>>>>> > > >>>>>>>> Right table: > > >>>>>>>> A: EntityA > > >>>>>>>> B: EntityB > > >>>>>>>> > > >>>>>>>> We could do a rekey as you proposed with a combined key, but not > > >>>>>>>> propagating the value at all.. > > >>>>>>>> Rekey table: > > >>>>>>>> A-1: (dummy value) > > >>>>>>>> B-2: (dummy value) > > >>>>>>>> > > >>>>>>>> Which we then join with the right table to produce: > > >>>>>>>> A-1: EntityA > > >>>>>>>> B-2: EntityB > > >>>>>>>> > > >>>>>>>> Which gets rekeyed back: > > >>>>>>>> 1: A, EntityA > > >>>>>>>> 2: B, EntityB > > >>>>>>>> > > >>>>>>>> And finally we do the actual join: > > >>>>>>>> Result table: > > >>>>>>>> 1: ((A, xyz), EntityA) > > >>>>>>>> 2: ((B, asd), EntityB) > > >>>>>>>> > > >>>>>>>> The thing is that in that last join, we have the opportunity to > > >>>>> compare > > >>>>>>> the > > >>>>>>>> current FK in the left table with the incoming PK of the right > > >>>>> table. If > > >>>>>>>> they don't match, we just drop the event, since it must be > > outdated. > > >>>>>>>> > > >>>>>>> > > >>>>>>>> In your KIP, you gave an example in which (1: A, xyz) gets > updated > > >> to > > >>>>>>> (1: > > >>>>>>>> B, xyz), ultimately yielding a conundrum about whether the final > > >>>>> state > > >>>>>>>> should be (1: null) or (1: joined-on-B). With the algorithm > above, > > >>>>> you > > >>>>>>>> would be considering (1: (B, xyz), (A, null)) vs (1: (B, xyz), > (B, > > >>>>>>>> EntityB)). It seems like this does give you enough information > to > > >>>>> make > > >>>>>>> the > > >>>>>>>> right choice, regardless of disordering. > > >>>>>>> > > >>>>>>> Will check Adams patch, but this should work. As mentioned often > I > > am > > >>>>>>> not convinced on partitioning back for the user automatically. I > > >> think > > >>>>>>> this is the real performance eater ;) > > >>>>>>> > > >>>>>>>> > > >>>>>>>> > > >>>>>>>> 7. Last thought... I'm a little concerned about the performance > of > > >>>>> the > > >>>>>>>> range scans when records change in the right table. You've said > > that > > >>>>>>> you've > > >>>>>>>> been using the algorithm you presented in production for a > while. > > >> Can > > >>>>>>> you > > >>>>>>>> give us a sense of the performance characteristics you've > > observed? > > >>>>>>>> > > >>>>>>> > > >>>>>>> Make it work, make it fast, make it beautiful. The topmost thing > > here > > >>>>> is > > >>>>>>> / was correctness. In practice I do not measure the performance > of > > >> the > > >>>>>>> range scan. Usual cases I run this with is emitting 500k - 1kk > rows > > >>>>>>> on a left hand side change. The range scan is just the work you > > gotta > > >>>>>>> do, also when you pack your data into different formats, usually > > the > > >>>>>>> rocks performance is very tight to the size of the data and we > > can't > > >>>>>>> really change that. It is more important for users to prevent > > useless > > >>>>>>> updates to begin with. My left hand side is guarded to drop > changes > > >>>>> that > > >>>>>>> are not going to change my join output. > > >>>>>>> > > >>>>>>> usually it's: > > >>>>>>> > > >>>>>>> drop unused fields and then don't forward if old.equals(new) > > >>>>>>> > > >>>>>>> regarding to the performance of creating an iterator for smaller > > >>>>>>> fanouts, users can still just do a group by first then anyways. > > >>>>>>> > > >>>>>>> > > >>>>>>> > > >>>>>>>> I could only think of one alternative, but I'm not sure if it's > > >>>>> better > > >>>>>>> or > > >>>>>>>> worse... If the first re-key only needs to preserve the original > > >> key, > > >>>>>>> as I > > >>>>>>>> proposed in #6, then we could store a vector of keys in the > value: > > >>>>>>>> > > >>>>>>>> Left table: > > >>>>>>>> 1: A,... > > >>>>>>>> 2: B,... > > >>>>>>>> 3: A,... > > >>>>>>>> > > >>>>>>>> Gets re-keyed: > > >>>>>>>> A: [1, 3] > > >>>>>>>> B: [2] > > >>>>>>>> > > >>>>>>>> Then, the rhs part of the join would only need a regular > > single-key > > >>>>>>> lookup. > > >>>>>>>> Of course we have to deal with the problem of large values, as > > >>>>> there's > > >>>>>>> no > > >>>>>>>> bound on the number of lhs records that can reference rhs > records. > > >>>>>>> Offhand, > > >>>>>>>> I'd say we could page the values, so when one row is past the > > >>>>>>> threshold, we > > >>>>>>>> append the key for the next page. Then in most cases, it would > be > > a > > >>>>>>> single > > >>>>>>>> key lookup, but for large fan-out updates, it would be one per > > (max > > >>>>>>> value > > >>>>>>>> size)/(avg lhs key size). > > >>>>>>>> > > >>>>>>>> This seems more complex, though... Plus, I think there's some > > extra > > >>>>>>>> tracking we'd need to do to know when to emit a retraction. For > > >>>>> example, > > >>>>>>>> when record 1 is deleted, the re-key table would just have (A: > > [3]). > > >>>>>>> Some > > >>>>>>>> kind of tombstone is needed so that the join result for 1 can > also > > >> be > > >>>>>>>> retracted. > > >>>>>>>> > > >>>>>>>> That's all! > > >>>>>>>> > > >>>>>>>> Thanks so much to both Adam and Jan for the thoughtful KIP. > Sorry > > >> the > > >>>>>>>> discussion has been slow. > > >>>>>>>> -John > > >>>>>>>> > > >>>>>>>> > > >>>>>>>> On Fri, Oct 12, 2018 at 2:20 AM Jan Filipiak < > > >>>>> jan.filip...@trivago.com> > > >>>>>>>> wrote: > > >>>>>>>> > > >>>>>>>>> Id say you can just call the vote. > > >>>>>>>>> > > >>>>>>>>> that happens all the time, and if something comes up, it just > > goes > > >>>>> back > > >>>>>>>>> to discuss. > > >>>>>>>>> > > >>>>>>>>> would not expect to much attention with another another email > in > > >>>>> this > > >>>>>>>>> thread. > > >>>>>>>>> > > >>>>>>>>> best Jan > > >>>>>>>>> > > >>>>>>>>> On 09.10.2018 13:56, Adam Bellemare wrote: > > >>>>>>>>>> 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 > > >>>>>>>>>>>>>>>> > > >>>>>>>>>>>>>>>> > > >>>>>>>>>>>>>>> > > >>>>>>>>>>>>>> > > >>>>>>>>>>>>> > > >>>>>>>>>>>> > > >>>>>>>>>>> > > >>>>>>>>>> > > >>>>>>>>> > > >>>>>>>> > > >>>>>>> > > >>>>>> > > >>>>> > > >>>> > > >>> > > >> > > > > > > > > >
