If you are using time windows, you can access the TimeWindow parameter of the WindowFunction.apply() method. The TimeWindow contains the start and end timestamp of a window (as Long) which can act as keys.
If you are using count windows, I think you have to use a counter as you described. 2016-10-07 1:06 GMT+02:00 AJ Heller <a...@drfloob.com>: > Thank you Fabian, I think that solves it. I'll need to rig up some tests > to verify, but it looks good. > > I used a RichMapFunction to assign ids incrementally to windows (mapping > STREAM_OBJECT to Tuple2<Long, STREAM_OBJECT> using a private long value in > the mapper that increments on every map call). It works, but by any chance > is there a more succinct way to do it? > > On Thu, Oct 6, 2016 at 1:50 PM, Fabian Hueske <fhue...@gmail.com> wrote: > >> Maybe this can be done by assigning the same window id to each of the N >> local windows, and do a >> >> .keyBy(windowId) >> .countWindow(N) >> >> This should create a new global window for each window id and collect all >> N windows. >> >> Best, Fabian >> >> 2016-10-06 22:39 GMT+02:00 AJ Heller <a...@drfloob.com>: >> >>> The goal is: >>> * to split data, random-uniformly, across N nodes, >>> * window the data identically on each node, >>> * transform the windows locally on each node, and >>> * merge the N parallel windows into a global window stream, such that >>> one window from each parallel process is merged into a "global window" >>> aggregate >>> >>> I've achieved all but the last bullet point, merging one window from >>> each partition into a globally-aggregated window output stream. >>> >>> To be clear, a rolling reduce won't work because it would aggregate over >>> all previous windows in all partitioned streams, and I only need to >>> aggregate over one window from each partition at a time. >>> >>> Similarly for a fold. >>> >>> The closest I have found is ParallelMerge for ConnectedStreams, but I >>> have not found a way to apply it to this problem. Can flink achieve this? >>> If so, I'd greatly appreciate a point in the right direction. >>> >>> Cheers, >>> -aj >>> >> >> >
