[jira] [Updated] (SPARK-6728) Improve performance of py4j for large bytearray

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

     [ https://issues.apache.org/jira/browse/SPARK-6728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen updated SPARK-6728:
-----------------------------
    Target Version/s: 1.5.0  (was: 1.4.0)

> Improve performance of py4j for large bytearray
> -----------------------------------------------
>
>                 Key: SPARK-6728
>                 URL: https://issues.apache.org/jira/browse/SPARK-6728
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark
>    Affects Versions: 1.3.0
>            Reporter: Davies Liu
>            Priority: Critical
>
> PySpark relies on py4j to transfer function arguments and return between Python and JVM, it's very slow to pass a large bytearray (larger than 10M). 
> In MLlib, it's possible to have a Vector with more than 100M bytes, which will need few GB memory, may crash.
> The reason is that py4j use text protocol, it will encode the bytearray as base64, and do multiple string concat. 
> Binary will help a lot, create a issue for py4j: https://github.com/bartdag/py4j/issues/159



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