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Posted to issues@flink.apache.org by "ASF GitHub Bot (JIRA)" <ji...@apache.org> on 2015/06/22 22:20:01 UTC

[jira] [Commented] (FLINK-2261) Remove reduce/aggregation from DataStream

    [ https://issues.apache.org/jira/browse/FLINK-2261?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14596574#comment-14596574 ] 

ASF GitHub Bot commented on FLINK-2261:
---------------------------------------

GitHub user gyfora opened a pull request:

    https://github.com/apache/flink/pull/860

    [FLINK-2261] [streaming] Remove reduce/fold/aggregations from DataStream

    

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/gyfora/flink FLINK-2261

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/flink/pull/860.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #860
    
----
commit e9ec12aa3f68fe68c3904f230103a590b66f0e2c
Author: Gyula Fora <gy...@apache.org>
Date:   2015-06-22T19:33:28Z

    [FLINK-2261] [streaming] Remove reduce/fold/aggregations from DataStream

----


> Remove reduce/aggregation from DataStream
> -----------------------------------------
>
>                 Key: FLINK-2261
>                 URL: https://issues.apache.org/jira/browse/FLINK-2261
>             Project: Flink
>          Issue Type: Improvement
>          Components: Java API, Scala API, Streaming
>    Affects Versions: 0.10
>            Reporter: Gyula Fora
>            Assignee: Gyula Fora
>
> Currently we have reduce and aggregation methods for non-grouped DataStreams as well, which will produce local aggregates depending on the parallelism of the operator.
> This behaviour is neither intuitive nor useful as it only produces sensible results if the user specifically sets the parallelism to 1 which should not be encouraged. 
> I would like to remove these methods from the DataStream api and only keep it for GroupedDataStreams and WindowedDataStream where the aggregation is either executed per-key or per-window.



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