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
