Hi, are you windowing based on event time? Cheers, Aljoscha
On Fri, 7 Oct 2016 at 09:28 Fabian Hueske <fhue...@gmail.com> wrote: > 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 > > > > >
