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: us...@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: us...@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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