[ https://issues.apache.org/jira/browse/SPARK-3488?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Aaron Staple resolved SPARK-3488. --------------------------------- Resolution: Won't Fix > cache deserialized python RDDs before iterative learning > -------------------------------------------------------- > > Key: SPARK-3488 > URL: https://issues.apache.org/jira/browse/SPARK-3488 > Project: Spark > Issue Type: Improvement > Components: MLlib, PySpark > Reporter: Aaron Staple > > When running an iterative learning algorithm, it makes sense that the input > RDD be cached for improved performance. When learning is applied to a python > RDD, currently the python RDD is always cached, then in scala that cached RDD > is mapped to an uncached deserialized RDD, and the uncached RDD is passed to > the learning algorithm. Instead the deserialized RDD should be cached. > This was originally discussed here: > https://github.com/apache/spark/pull/2347#issuecomment-55181535 -- 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