Adam Davison created SPARK-4315:
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Summary: 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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