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From Mayur Rustagi <mayur.rust...@gmail.com>
Subject Re: Streaming: getting total count over all windows
Date Fri, 14 Nov 2014 06:21:52 GMT
So if you want to do from beginning to end of time the interface is
updateStatebykey, if only over a particular set of windows you can
construct broader windows from smaller windows/batches.

Mayur Rustagi
Ph: +1 (760) 203 3257
http://www.sigmoidanalytics.com
@mayur_rustagi <https://twitter.com/mayur_rustagi>


On Fri, Nov 14, 2014 at 9:17 AM, jay vyas <jayunit100.apache@gmail.com>
wrote:

> I would think this should be done at the application level.
> After all, the core functionality of SparkStreaming is to capture RDDs in
> some real time interval and process them -
> not to aggregate their results.
>
> But maybe there is a better way.......
>
> On Thu, Nov 13, 2014 at 8:28 PM, SK <skrishna.id@gmail.com> wrote:
>
>> Hi,
>>
>> I am using the following code to generate the (score, count) for each
>> window:
>>
>> val score_count_by_window  = topic.map(r =>  r._2)   // r._2 is the
>> integer
>> score
>>                                                      .countByValue()
>>
>> score_count_by_window.print()
>>
>> E.g. output for a window is as follows, which means that within the
>> Dstream
>> for that window, there are 2 rdds with score 0; 3 with score 1, and 1 with
>> score -1.
>> (0, 2)
>> (1, 3)
>> (-1, 1)
>>
>> I would like to get the aggregate count for each score over all windows
>> until program terminates. I tried countByValueAndWindow() but the result
>> is
>> same as countByValue() (i.e. it is producing only per window counts).
>> reduceByWindow also does not produce the result I am expecting. What is
>> the
>> correct way to sum up the counts over multiple windows?
>>
>> thanks
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/Streaming-getting-total-count-over-all-windows-tp18888.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
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>>
>
>
> --
> jay vyas
>

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