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From Fabian Hueske <fhue...@gmail.com>
Subject Re: Merging N parallel/partitioned WindowedStreams together, one-to-one, into a global window stream
Date Fri, 07 Oct 2016 07:27:48 GMT
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

2016-10-07 1:06 GMT+02:00 AJ Heller <aj@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 <fhueske@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 <aj@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

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