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>.
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/> Thanks Eno > On Jun 15, 2017, at 2:38 PM, Paolo Patierno <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> > Linkedin : paolopatierno<http://it.linkedin.com/in/paolopatierno> > Blog : DevExperience<http://paolopatierno.wordpress.com/>
