[jira] [Assigned] (SPARK-4315) PySpark pickling of pyspark.sql.Row objects is extremely inefficient

["Davies Liu (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-4315?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Davies Liu reassigned SPARK-4315:
---------------------------------

    Assignee: Davies Liu

> PySpark pickling of pyspark.sql.Row objects is extremely inefficient
> --------------------------------------------------------------------
>
>                 Key: SPARK-4315
>                 URL: https://issues.apache.org/jira/browse/SPARK-4315
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 1.1.0
>         Environment: Ubuntu, Python 2.7, Spark 1.1.0
>            Reporter: Adam Davison
>            Assignee: Davies Liu
>
> Working with an RDD of pyspark.sql.Row objects, created by reading a file with SQLContext in a local PySpark context.
> Operations on the RDD, such as: data.groupBy(lambda x: x.field_name) are extremely slow (more than 10x slower than an equivalent Scala/Spark implementation). Obviously I expected it to be somewhat slower, but I did a bit of digging given the difference was so huge.
> Luckily it's fairly easy to add profiling to the Python workers. I see that the vast majority of time is spent in:
> spark-1.1.0-bin-cdh4/python/pyspark/sql.py:757(_restore_object)
> It seems that this line attempts to accelerate pickling of Rows with the use of a cache. Some debugging reveals that this cache becomes quite big (100s of entries). Disabling the cache by adding:
> return _create_cls(dataType)(obj)
> as the first line of _restore_object made my query run 5x faster. Implying that the caching is not providing the desired acceleration...



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