Hi, you would indeed use apply(), or better fold(<initial_value>, <fold_function>, <window_function>) to map the result of folding your window to some other data type. If you will, a WindowFunction allows "mapping" the result of your windowing to a different type.
Best, Aljoscha On Wed, 15 Feb 2017 at 06:14 nsengupta <sengupta.nirma...@gmail.com> wrote: > I have gone through this post > < > http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/WindowedStream-operation-questions-td6006.html > > > , where Aljoscha explains that /mapping/ on WindowedStream is /not/ > allowed. > > So, I think I haven't asked the question properly. Here is (hopefully) a > better and easier version: > > 1. I begin with records of type RawMITSIMTuple. > 2. When I group them using a Window, I get an > AllWindowedStream[RawMITSIMTuple]. > 3. I /fold/ the tuples obtained in the Window, which gives me a > DataStream[Vector[RawMITSIMTuple]. > 4. What I need is a DataStream[PositionReport]. So, I need to flatMap > the > output of previous step, where I first get hold of each of the > RawMITSIMTuple and map that to PositionReport. > > val positionReportStream = this > .readRawMITSIMTuplesInjected(envDefault,args(0)) > .assignAscendingTimestamps(e => e.timeOfReport) > .windowAll(TumblingEventTimeWindows.of(Time.seconds(30))) > .fold(Vector[RawMITSIMTuple]())((collectorBin,rawRecord) => { > collectorBin :+ rawRecord) > }) > .flatMap(r => r.map(e => this.preparePositionReport(e))) > > This gives me what I want, but I feel this is verbose and inefficient. Am I > thinking correctly? If so, what is a better idiom to use in such cases? > > -- Nirmalya > > > > > -- > View this message in context: > http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Clarification-use-of-AllWindowedStream-apply-function-tp11627p11630.html > Sent from the Apache Flink User Mailing List archive. mailing list archive > at Nabble.com. >
