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: dev@kafka.apache.org; Jay Kreps Cc: us...@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<mailto: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><mailto:eno.there...@gmail.com> Sent: Thursday, June 15, 2017 3:57 PM To: us...@kafka.apache.org<mailto: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><mailto: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><mailto:eno.there...@gmail.com> Sent: Thursday, June 15, 2017 1:45 PM To: us...@kafka.apache.org<mailto: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><mailto: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 ? 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