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https://issues.apache.org/jira/browse/SPARK-6728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Davies Liu closed SPARK-6728.
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Resolution: Won't Fix
This can not be fixed without changes in Py4j, but this is not in the roadmap
of Py4j yet.
> 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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