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https://issues.apache.org/jira/browse/SPARK-1425?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Nicholas Chammas updated SPARK-1425:
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Component/s: PySpark
> PySpark can crash Executors if worker.py fails while serializing data
> ---------------------------------------------------------------------
>
> Key: SPARK-1425
> URL: https://issues.apache.org/jira/browse/SPARK-1425
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 0.9.0
> Reporter: Matei Zaharia
>
> The PythonRDD code that talks to the worker will keep calling
> stream.readInt() and allocating an array of that size. Unfortunately, if the
> worker gives it corrupted data, it will attempt to allocate a huge array and
> get an OutOfMemoryError. It would be better to use a different stream to give
> feedback, *or* only write an object out to the stream once it's been properly
> pickled to bytes or to a string.
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