Hi Abdul, Every window is handled by a single machine, if this is what you mean by “partition”.
Kostas > On Jan 5, 2017, at 9:21 PM, Abdul Salam Shaikh <abd.salam.sha...@gmail.com> > wrote: > > Thanks Fabian and Kostas, > > How can I put to use the power of flink as a distributed system ? > > In cases where we have multiple windows, is one single window handled by one > partition entirely or is it spread across several partitions ? > > On Thu, Jan 5, 2017 at 12:25 PM, Fabian Hueske <fhue...@gmail.com > <mailto:fhue...@gmail.com>> wrote: > Flink is a distributed system and does not preserve order across partitions. > The number prefix (e.g., 1>, 2>, ...) tells you the parallel instance of the > printing operator. > > You can set the parallelism to 1 to have the stream in order. > > Fabian > > 2017-01-05 12:16 GMT+01:00 Kostas Kloudas <k.klou...@data-artisans.com > <mailto:k.klou...@data-artisans.com>>: > Hi Abdul, > > Flink provides no ordering guarantees on the elements within a window. > The only “order” it guarantees is that the results referring to window-1 are > going to be emitted before those of window-2 (assuming that window-1 precedes > window-2). > > Thanks, > Kostas > >> On Jan 5, 2017, at 11:57 AM, Abdul Salam Shaikh <abd.salam.sha...@gmail.com >> <mailto:abd.salam.sha...@gmail.com>> wrote: >> >> Hi, >> >> I am using a JSON file as the source for the streaming (in the ascending >> order of the field Umlaufsekunde)which has events as follows: >> >> {"event":[{"Umlaufsekunde":115}]} >> {"event":[{"Umlaufsekunde":135}]} >> {"event":[{"Umlaufsekunde":135}]} >> {"event":[{"Umlaufsekunde":145}]} >> {"event":[{"Umlaufsekunde":155}]} >> {"event":[{"Umlaufsekunde":155}]} >> {"event":[{"Umlaufsekunde":185}]} >> {"event":[{"Umlaufsekunde":195}]} >> {"event":[{"Umlaufsekunde":195}]} >> {"event":[{"Umlaufsekunde":205}]} >> {"event":[{"Umlaufsekunde":245}]} >> >> However, when I try to print the stream, it is unordered as given below: >> 1> (115,null,1483517983252,1190) -- The first value indicating Umlaufsekunde >> 2> (135,null,1483517984877,1190) >> 2> (155,null,1483517986861,1190) >> 4> (145,null,1483517985752,1190) >> 3> (135,null,1483517985424,1190) >> 4> (195,null,1483517990736,1190) >> 4> (255,null,1483517997424,1190) >> 2> (205,null,1483517991518,1190) >> 2> (275,null,1483517999330,1190) >> 2> (385,null,1483518865371,1190) >> 2> (395,null,1483518866840,1190) >> 1> (155,null,1483517986533,1190) >> 4> (285,null,1483518000189,1190) >> 4> (395,null,1483518866231,1190) >> >> I have also tried using the Timestamps and Watermarks but no luck as >> follows: >> >> public class TimestampExtractor implements >> AssignerWithPeriodicWatermarks<Tuple5<String, Long, List<Lane>, Long, Long>>{ >> >> private long currentMaxTimestamp; >> >> @Override >> public Watermark getCurrentWatermark() { >> return new Watermark(currentMaxTimestamp); >> } >> >> @Override >> public long extractTimestamp(Tuple5<String, Long> element, long >> previousElementTimestamp) { >> long timestamp = element.getField(1); >> currentMaxTimestamp = timestamp; >> return currentMaxTimestamp; >> } >> >> } >> >> Could anyone suggest how do I handle this problem for the arrival of events >> in order ? >> >> Thanks! >> >> > > > > > > -- > Thanks & Regards, > > Abdul Salam Shaikh >
