[jira] [Resolved] (SPARK-12353) wrong output for countByValue and countByValueAndWindow

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-12353?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-12353.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 2.0.0

Issue resolved by pull request 10350
[https://github.com/apache/spark/pull/10350]

> wrong output for countByValue and countByValueAndWindow
> -------------------------------------------------------
>
>                 Key: SPARK-12353
>                 URL: https://issues.apache.org/jira/browse/SPARK-12353
>             Project: Spark
>          Issue Type: Bug
>          Components: Documentation, Input/Output, PySpark, Streaming
>    Affects Versions: 1.5.2
>         Environment: Ubuntu 14.04, Python 2.7.6
>            Reporter: Bo Jin
>              Labels: releasenotes
>             Fix For: 2.0.0
>
>   Original Estimate: 2h
>  Remaining Estimate: 2h
>
> http://stackoverflow.com/q/34114585/4698425
> In PySpark Streaming, function countByValue and countByValueAndWindow return one single number which is the count of distinct elements, instead of a list of (k,v) pairs.
> It's inconsistent with the documentation: 
> countByValue: When called on a DStream of elements of type K, return a new DStream of (K, Long) pairs where the value of each key is its frequency in each RDD of the source DStream.
> countByValueAndWindow: When called on a DStream of (K, V) pairs, returns a new DStream of (K, Long) pairs where the value of each key is its frequency within a sliding window. Like in reduceByKeyAndWindow, the number of reduce tasks is configurable through an optional argument.



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