[ https://issues.apache.org/jira/browse/SPARK-1425?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Nicholas Chammas updated SPARK-1425: ------------------------------------ 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org