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From "LI Guobao (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SYSTEMML-2418) Spark data partitioner
Date Thu, 28 Jun 2018 13:32:00 GMT

     [ https://issues.apache.org/jira/browse/SYSTEMML-2418?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

LI Guobao updated SYSTEMML-2418:
--------------------------------
    Description: In the context of ml, it would be more efficient to support the data partitioning
in distributed manner. This task aims to do the data partitioning on Spark which means that
all the data will be firstly splitted among workers and then execute data partitioning on
worker side according to scheme, and then the partitioned data which stay on each worker could
be directly passed to run model training work without materialization on HDFS.  (was: In the
context of ml, it would be more efficient to support the data partitioning in distributed
manner. This task aims to do the data partitioning on Spark which means that all the data
will be firstly splitted among workers and then execute data partitioning on worker side according
to scheme, and then the partitioned data which stay on each worker could be directly passed
to run model training work.)

> Spark data partitioner
> ----------------------
>
>                 Key: SYSTEMML-2418
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-2418
>             Project: SystemML
>          Issue Type: Sub-task
>            Reporter: LI Guobao
>            Assignee: LI Guobao
>            Priority: Major
>
> In the context of ml, it would be more efficient to support the data partitioning in
distributed manner. This task aims to do the data partitioning on Spark which means that all
the data will be firstly splitted among workers and then execute data partitioning on worker
side according to scheme, and then the partitioned data which stay on each worker could be
directly passed to run model training work without materialization on HDFS.



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