[
https://issues.apache.org/jira/browse/SPARK-6728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Davies Liu updated SPARK-6728:
------------------------------
Target Version/s: (was: 1.6.0)
> 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
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
