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https://issues.apache.org/jira/browse/SPARK-4315?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14619611#comment-14619611
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Davies Liu commented on SPARK-4315:
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This is fixed by https://github.com/apache/spark/pull/5445
> 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
>
> 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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