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From "Edward J. Yoon (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HAMA-983) Hama runner for DataFlow
Date Wed, 31 Aug 2016 04:41:20 GMT

    [ https://issues.apache.org/jira/browse/HAMA-983?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15451100#comment-15451100
] 

Edward J. Yoon commented on HAMA-983:
-------------------------------------

Hi, I didn't look at dataflow (apache beam) closely, but:

>> Do you mean that each superstep can be executed in data pipeline as a pcollection?


I guess yes, or single job can be executed as the case may be.

If you're interested in working on this, you can refer https://github.com/dataArtisans/flink-dataflow/blob/master/runner/src/main/java/com/dataartisans/flink/dataflow/FlinkPipelineRunner.java

And, before we do this, HAMA-940 and data processing BSP maybe the first I guess. Please feel
free to drop your opinion and contribute the patches. :-)

If you have any questions, let me know.

> Hama runner for DataFlow
> ------------------------
>
>                 Key: HAMA-983
>                 URL: https://issues.apache.org/jira/browse/HAMA-983
>             Project: Hama
>          Issue Type: Bug
>            Reporter: Edward J. Yoon
>              Labels: gsoc2016
>
> As you already know, Apache Beam provides unified programming model for both batch and
streaming inputs.
> The APIs are generally associated with data filtering and transforming. So we'll need
to implement some data processing runner like https://github.com/dapurv5/MapReduce-BSP-Adapter/blob/master/src/main/java/org/apache/hama/mapreduce/examples/WordCount.java
> Also, implementing similarity join can be funny. According to http://www.ruizhang.info/publications/TPDS2015-Heads_Join.pdf,
Apache Hama is clearly winner among Apache Hadoop and Apache Spark.
> Since it consists of transformation, aggregation, and partition computations, I think
it's possible to implement using Apache Beam APIs.



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