Although we have seen this request commonly being asked in the community, to me people are actually requesting slightly different things when they mention "one final output", or "trigger", or "window closure" terms, etc. So I'd like to try summarizing them here based on my own understanding before discussing further:
1). "Not caching globally": I would like to have "de-dupped" outputs on some of the stateful operators but not on others, while currently the caching config is global meaning that once turned on all stores will be covered with a cache. To me this is a valid use case request that is not supported today; for example, we could consider having such per-store-caching in the state store supplier API. 2). "Final output of the window": I want to know which output is the final result for this windowed state updates (it could be the ONLY output for this window or there can be some early "partial" outputs) such that no more outputs will be generated afterwards. To me this is a semi-valid use case and semi-confusion, since theoretically there should be no "final output" and in practice the final output depends on the implementation details of the window retention period, which is usually not related to the computational logic at all, but more for the monitoring / operational purposes. Today this can still be done through Interactive Queries feature to check which window stores are still available and then respectively respond that some state store values will not change any more. 3). "No bytes-based triggers": The current mechanism seems just like a "bytes-based triggering" mechanism to me, whereas I want a "time-based triggering". I think Paolo's use case falls into this category, but there are still some sub-categories: 3.1). "Process-time based triggers": I think this is what Jay's proposed solution tries to tackle. E.g. even if we are in the catching-up mode that we processed a day's data in 5 min, we will only output 5 times if the output interval is 1 min. This is where users want to have some partial results to reduce the application's end2end latency, while bytes-based triggers cannot guarantee that; 3.2). "Event-time based triggers": this is what Paolo's use case falls into. E.g. if we processed a day's data in 5 min, we will output 24 * 60 / 5 times as that much time has advanced. This is where --- like Paolo said --- users want to maintain some history of the updates beside the final output, while bytes-based triggers cannot guarantee that. For both of these cases, with KIP-138 these should be addressable in the lower-level Processor API: you can punctuate every one minute to send out results, based either on stream time or event time. Question is whether / how we want to support such syntax in the higher-level DSL: personally I think both sub-categories are valid cases but 3.1) may be more commonly motivated than 3.2). In terms of importance / priority I think 1) > 3.1) > 3.2) >> 2). Guozhang On Sun, Jun 18, 2017 at 3:04 AM, Paolo Patierno <ppatie...@live.com> wrote: > I'm just thinking that having output into a topic every X seconds thanks > to the windowing could be a useful feature without using something > interactive queries that are really powerful (I love them) but aren't so > useful in this scenario. > > Using the caching parameter isn't useful in such scenario because it's in > terms of bytes not in terms of time. > > > Let's consider another scenario ... > > > I have a sensor sending data every 1 seconds. Let's assume that our stream > processing application is not online and the source topic is filled by > sensor data with related event time. > > When the stream processing application comes online I'd like to have a > record in the final topic every 5 seconds in order to have an history as > well (because the application was offline). To be clear ... > > Imagine that starting from t = 0, the sensor starts to send data but > application is offline and the topic is filled from t = 0 to t = 12 (with > 12 events, one per second). > > At t = 12 application comes back online and processes the stream in order > to process data from t = 0 to t = 4 (so first 5 seconds) putting the result > into the destination queue. Then from t = 5 to t = 9 (other 5 seconds) > putting the result into the destination queue and so on. If sensor rate > isn't so fast then the application will start to process in real time at > some point (it seems to me something like a batch processing which becomes > real time processing). > > This scenario, for example, isn't possible with Spark today because when > the application comes back online it process all data from t = 0 to t = 12 > immediately as they were a whole burst of data without considering t as > event time to take into account for processing. > > > I'm thinking aloud, considering some scenario that could have a value in > the IoT space ... > > > Thanks, > > Paolo. > > > *Paolo Patierno* > > *Senior Software Engineer (IoT) @ Red Hat **Microsoft MVP on **Windows > Embedded & IoT* > *Microsoft Azure Advisor* > > Twitter : @ppatierno <http://twitter.com/ppatierno> > Linkedin : paolopatierno <http://it.linkedin.com/in/paolopatierno> > Blog : DevExperience <http://paolopatierno.wordpress.com/> > > > ------------------------------ > *From:* Michal Borowiecki <michal.borowie...@openbet.com> > *Sent:* Sunday, June 18, 2017 9:34 AM > *To:* d...@kafka.apache.org; Jay Kreps > *Cc:* users@kafka.apache.org; Matthias J. Sax > > *Subject:* Re: Kafka Streams vs Spark Streaming : reduce by window > > > If confusion is the problem, then totally agree no point adding more > knobs. Perhaps you're right that users don't *really* want > processing-time semantics. Just *think* they want them until they start > considering replay/catch-up scenarios. I guess people rarely think about > those from the start (I sure didn't). > > Cheers, > > Michał > > On 16/06/17 17:54, Jay Kreps wrote: > > I think the question is when do you actually *want* processing time > semantics? There are definitely times when its safe to assume the two are > close enough that a little lossiness doesn't matter much but it is pretty > hard to make assumptions about when the processing time is and has been > hard for us to think of a use case where its actually desirable. > > I think mostly what we've seen is confusion about the core concepts: > > - stream -- immutable events that occur > - tables (including windows) -- current state of the world > > If the root problem is confusion adding knobs never makes it better. If > the root problem is we're missing important use cases that justify the > additional knobs then i think it's good to try to really understand them. I > think there could be use cases around systems that don't take updates, > example would be email, twitter, and some metrics stores. > > One solution that would be less complexity inducing than allowing new > semantics, but might help with the use cases we need to collect, would be > to add a new operator in the DSL. Something like .freezeAfter(30, > TimeUnit.SECONDS) that collects all updates for a given window and both > emits and enforces a single output after 30 seconds after the advancement > of stream time and remembers that it is omitted, suppressing all further > output (so the output is actually a KStream). This might or might not > depend on wall clock time. Perhaps this is in fact what you are proposing? > > -Jay > > > > On Fri, Jun 16, 2017 at 2:38 AM, Michal Borowiecki < > michal.borowie...@openbet.com> wrote: > >> I wonder if it's a frequent enough use case that Kafka Streams should >> consider providing this out of the box - this was asked for multiple times, >> right? >> >> Personally, I agree totally with the philosophy of "no final >> aggregation", as expressed by Eno's post, but IMO that is predicated >> totally on event-time semantics. >> >> If users want processing-time semantics then, as the docs already point >> out, there is no such thing as a late-arriving record - every record just >> falls in the currently open window(s), hence the notion of final >> aggregation makes perfect sense, from the usability point of view. >> >> The single abstraction of "stream time" proves leaky in some cases (e.g. >> for punctuate method - being addressed in KIP-138). Perhaps this is another >> case where processing-time semantics warrant explicit handling in the api - >> but of course, only if there's sufficient user demand for this. >> >> What I could imagine is a new type of time window >> (ProcessingTimeWindow?), that if used in an aggregation, the underlying >> processor would force the WallclockTimestampExtractor (KAFKA-4144 enables >> that) and would use the system-time punctuation (KIP-138) to send the final >> aggregation value once the window has expired and could be configured to >> not send intermediate updates while the window was open. >> >> Of course this is just a helper for the users, since they can implement >> it all themselves using the low-level API, as Matthias pointed out already. >> Just seems there's recurring interest in this. >> >> Again, this only makes sense for processing time semantics. For >> event-time semantics I find the arguments for "no final aggregation" >> totally convincing. >> >> >> Cheers, >> >> Michał >> >> On 16/06/17 00:08, Matthias J. Sax wrote: >> >> Hi Paolo, >> >> This SO question might help, >> too:https://stackoverflow.com/questions/38935904/how-to-send-final-kafka-streams-aggregation-result-of-a-time-windowed-ktable >> >> For Streams, the basic model is based on "change" and we report updates >> to the "current" result immediately reducing latency to a minimum. >> >> Last, if you say it's going to fall into the next window, you won't get >> event time semantics but you fall back processing time semantics, that >> cannot provide exact results.... >> >> If you really want to trade-off correctness version getting (late) >> updates and want to use processing time semantics, you should configure >> WallclockTimestampExtractor and implement a "update deduplication" >> operator using table.toStream().transform(). You can attached a state to >> your transformer and store all update there (ie, newer update overwrite >> older updates). Punctuations allow you to emit "final" results for >> windows for which "window end time" passed. >> >> >> -Matthias >> >> On 6/15/17 9:21 AM, Paolo Patierno wrote: >> >> Hi Eno, >> >> >> regarding closing window I think that it's up to the streaming application. >> I mean ... >> >> If I want something like I described, I know that a value outside my 5 >> seconds window will be taken into account for the next processing (in the >> next 5 seconds). I don't think I'm losing a record, I am ware that this >> record will fall in the next "processing" window. Btw I'll take a look at >> your article ! Thanks ! >> >> >> Paolo >> >> >> Paolo Patierno >> Senior Software Engineer (IoT) @ Red Hat >> Microsoft MVP on Windows Embedded & IoT >> Microsoft Azure Advisor >> >> Twitter : @ppatierno<http://twitter.com/ppatierno> >> <http://twitter.com/ppatierno> >> Linkedin : paolopatierno<http://it.linkedin.com/in/paolopatierno> >> <http://it.linkedin.com/in/paolopatierno> >> Blog : DevExperience<http://paolopatierno.wordpress.com/> >> <http://paolopatierno.wordpress.com/> >> >> >> ________________________________ >> From: Eno Thereska <eno.there...@gmail.com> <eno.there...@gmail.com> >> Sent: Thursday, June 15, 2017 3:57 PM >> To: users@kafka.apache.org >> Subject: Re: Kafka Streams vs Spark Streaming : reduce by window >> >> Hi Paolo, >> >> Yeah, so if you want fewer records, you should actually "not" disable cache. >> If you disable cache you'll get all the records as you described. >> >> About closing windows: if you close a window and a late record arrives that >> should have been in that window, you basically lose the ability to process >> that record. In Kafka Streams we are robust to that, in that we handle late >> arriving records. There is a comparison here for example when we compare it >> to other methods that depend on watermarks or triggers: >> https://www.confluent.io/blog/watermarks-tables-event-time-dataflow-model/ >> <https://www.confluent.io/blog/watermarks-tables-event-time-dataflow-model/> >> <https://www.confluent.io/blog/watermarks-tables-event-time-dataflow-model/> >> >> Eno >> >> >> >> On 15 Jun 2017, at 14:57, Paolo Patierno <ppatie...@live.com> >> <ppatie...@live.com> wrote: >> >> Hi Emo, >> >> >> thanks for the reply ! >> >> Regarding the cache I'm already using CACHE_MAX_BYTES_BUFFERING_CONFIG = 0 >> (so disabling cache). >> >> Regarding the interactive query API (I'll take a look) it means that it's up >> to the application doing something like we have oob with Spark. >> >> May I ask what do you mean with "We don’t believe in closing windows" ? >> Isn't it much more code that user has to write for having the same result ? >> >> I'm exploring Kafka Streams and it's very powerful imho even because the >> usage is pretty simple but this scenario could have a lack against Spark. >> >> >> Thanks, >> >> Paolo. >> >> >> Paolo Patierno >> Senior Software Engineer (IoT) @ Red Hat >> Microsoft MVP on Windows Embedded & IoT >> Microsoft Azure Advisor >> >> Twitter : @ppatierno<http://twitter.com/ppatierno> >> <http://twitter.com/ppatierno> >> Linkedin : paolopatierno<http://it.linkedin.com/in/paolopatierno> >> <http://it.linkedin.com/in/paolopatierno> >> Blog : DevExperience<http://paolopatierno.wordpress.com/> >> <http://paolopatierno.wordpress.com/> >> >> >> ________________________________ >> From: Eno Thereska <eno.there...@gmail.com> <eno.there...@gmail.com> >> Sent: Thursday, June 15, 2017 1:45 PM >> To: users@kafka.apache.org >> Subject: Re: Kafka Streams vs Spark Streaming : reduce by window >> >> Hi Paolo, >> >> That is indeed correct. We don’t believe in closing windows in Kafka Streams. >> You could reduce the number of downstream records by using record caches: >> http://docs.confluent.io/current/streams/developer-guide.html#record-caches-in-the-dsl >> >> <http://docs.confluent.io/current/streams/developer-guide.html#record-caches-in-the-dsl> >> >> <http://docs.confluent.io/current/streams/developer-guide.html#record-caches-in-the-dsl>. >> >> Alternatively you can just query the KTable whenever you want using the >> Interactive Query APIs (so when you query dictates what data you receive), >> see this >> https://www.confluent.io/blog/unifying-stream-processing-and-interactive-queries-in-apache-kafka/ >> >> <https://www.confluent.io/blog/unifying-stream-processing-and-interactive-queries-in-apache-kafka/> >> >> <https://www.confluent.io/blog/unifying-stream-processing-and-interactive-queries-in-apache-kafka/> >> >> Thanks >> Eno >> >> On Jun 15, 2017, at 2:38 PM, Paolo Patierno <ppatie...@live.com> >> <ppatie...@live.com> wrote: >> >> Hi, >> >> >> using the streams library I noticed a difference (or there is a lack of >> knowledge on my side)with Apache Spark. >> >> Imagine following scenario ... >> >> >> I have a source topic where numeric values come in and I want to check the >> maximum value in the latest 5 seconds but ... putting the max value into a >> destination topic every 5 seconds. >> >> This is what happens with reduceByWindow method in Spark. >> >> I'm using reduce on a KStream here that process the max value taking into >> account previous values in the latest 5 seconds but the final value is put >> into the destination topic for each incoming value. >> >> >> For example ... >> >> >> An application sends numeric values every 1 second. >> >> With Spark ... the source gets values every 1 second, process max in a >> window of 5 seconds, puts the max into the destination every 5 seconds (so >> when the window ends). If the sequence is 21, 25, 22, 20, 26 the output will >> be just 26. >> >> With Kafka Streams ... the source gets values every 1 second, process max in >> a window of 5 seconds, puts the max into the destination every 1 seconds (so >> every time an incoming value arrives). Of course, if for example the >> sequence is 21, 25, 22, 20, 26 ... the output will be 21, 25, 25, 25, 26. >> >> >> Is it possible with Kafka Streams ? Or it's something to do at application >> level ? >> >> >> Thanks, >> >> Paolo >> >> >> Paolo Patierno >> Senior Software Engineer (IoT) @ Red Hat >> Microsoft MVP on Windows Embedded & IoT >> Microsoft Azure Advisor >> >> Twitter : @ppatierno<http://twitter.com/ppatierno> >> <http://twitter.com/ppatierno> >> Linkedin : paolopatierno<http://it.linkedin.com/in/paolopatierno> >> <http://it.linkedin.com/in/paolopatierno> >> Blog : DevExperience<http://paolopatierno.wordpress.com/> >> <http://paolopatierno.wordpress.com/> >> >> >> -- >> <http://www.openbet.com/> Michal Borowiecki >> Senior Software Engineer L4 >> T: +44 208 742 1600 <+44%2020%208742%201600> >> >> >> +44 203 249 8448 <+44%2020%203249%208448> >> >> >> >> E: michal.borowie...@openbet.com >> W: www.openbet.com >> OpenBet Ltd >> >> Chiswick Park Building 9 >> >> 566 Chiswick High Rd >> >> London >> >> W4 5XT >> >> UK >> <https://www.openbet.com/email_promo> >> This message is confidential and intended only for the addressee. 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