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https://issues.apache.org/jira/browse/SPARK-7122?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14588211#comment-14588211
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Cody Koeninger commented on SPARK-7122:
---------------------------------------

It's certainly your prerogative to wait for an official release.  However, keep 
in mind that the patch in question is just a performance optimization, not 
necessarily a bug fix targeted at whatever your issue is.  Without a minimal 
reproducible case of your problem, or testing patches against your workload, 
there's no way of knowing if the performance optimization solves your problem.  
If it doesn't, you're looking at waiting for yet another release after 1.4.1.

> KafkaUtils.createDirectStream - unreasonable processing time in absence of 
> load
> -------------------------------------------------------------------------------
>
>                 Key: SPARK-7122
>                 URL: https://issues.apache.org/jira/browse/SPARK-7122
>             Project: Spark
>          Issue Type: Question
>          Components: Streaming
>    Affects Versions: 1.3.1
>         Environment: Spark Streaming 1.3.1, standalone mode running on just 1 
> box: Ubuntu 14.04.2 LTS, 4 cores, 8GB RAM, java version "1.8.0_40"
>            Reporter: Platon Potapov
>            Priority: Minor
>         Attachments: 10.second.window.fast.job.txt, 
> 5.second.window.slow.job.txt, SparkStreamingJob.scala
>
>
> attached is the complete source code of a test spark job. no external data 
> generators are run - just the presence of a kafka topic named "raw" suffices.
> the spark job is run with no load whatsoever. http://localhost:4040/streaming 
> is checked to obtain job processing duration.
> * in case the test contains the following transformation:
> {code}
>     // dummy transformation
>     val temperature = bytes.filter(_._1 == "abc")
>     val abc = temperature.window(Seconds(40), Seconds(5))
>     abc.print()
> {code}
> the median processing time is 3 seconds 80 ms
> * in case the test contains the following transformation:
> {code}
>     // dummy transformation
>     val temperature = bytes.filter(_._1 == "abc")
>     val abc = temperature.map(x => (1, x))
>     abc.print()
> {code}
> the median processing time is just 50 ms
> please explain why does the "window" transformation introduce such a growth 
> of job duration?
> note: the result is the same regardless of the number of kafka topic 
> partitions (I've tried 1 and 8)
> note2: the result is the same regardless of the window parameters (I've tried 
> (20, 2) and (40, 5))



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