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Posted to issues@spark.apache.org by "Davies Liu (JIRA)" <ji...@apache.org> on 2014/10/20 21:11:34 UTC

[jira] [Resolved] (SPARK-3467) Python BatchedSerializer should dynamically lower batch size for large objects

     [ https://issues.apache.org/jira/browse/SPARK-3467?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Davies Liu resolved SPARK-3467.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 1.2.0

This is fixed by https://github.com/apache/spark/pull/2740

> Python BatchedSerializer should dynamically lower batch size for large objects
> ------------------------------------------------------------------------------
>
>                 Key: SPARK-3467
>                 URL: https://issues.apache.org/jira/browse/SPARK-3467
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark
>            Reporter: Matei Zaharia
>             Fix For: 1.2.0
>
>
> If you try caching largish objects in Python, you will get a crash sooner than you would in Scala / Java because Python automatically batches them. I believe the default batch size is 10, though it may be 1024. But maybe we can start by pickling the first object and using a smaller batch size if it is large.



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