Hi Kostas, Thank you very much for the explanation.
Best, Yassine On Wed, Jul 27, 2016 at 1:09 PM, Kostas Kloudas <k.klou...@data-artisans.com > wrote: > Hi Yassine, > > When the WindowFunction is applied to the content of a window, the > timestamp of the resulting record > is the window.maxTimestamp, which is the endOfWindow-1. > > You can imaging if you have a Tumbling window from 0 to 2000, the result > will have a timestamp of 1999. > Window boundaries are closed in the start and open at the end timestamp, > or [start, end). > > If you want to play around, I would suggest checking out the tests in the > WindowOperatorTest class. > > There you can do experiments and figure out how Flink’s windowOperator > works internally and what is the > interplay between windowAssingers, triggers, and the windowOperator. > > Hope this helps, > Kostas > > On Jul 27, 2016, at 8:41 AM, Yassin Marzouki <yassmar...@gmail.com> wrote: > > Hi all, > > Say I assign timestamps to a stream and then apply a transformation like > this: > > > stream.keyBy(0).timeWindow(Time.hours(5)).reduce(count).timeWindowAll(Time.days(1)).apply(transformation) > > Now, when the first window is applied, events are aggregated based on > their timestamps, but I don't understand what timestamp will be assigned to > the aggregated result of the reduce operation for the second window to > process it. Could you please explain it? Thank you. > > Best, > Yassine > > >
