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From "Edward J. Yoon (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HAMA-983) Hama runner for DataFlow
Date Thu, 01 Sep 2016 04:06:20 GMT

    [ https://issues.apache.org/jira/browse/HAMA-983?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15454239#comment-15454239
] 

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

Just FYI, Apache Beam's basic example is wordcount. I guess, the batch mode can be similar
with org.apache.hama.examples.PiEstimator: (n - 1) tasks parses and counts the words and 1
task aggregates the word counts and emits the final result. The streaming mode is not sure,
so you'll need to check how it handles io.

> 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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