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From davies <...@git.apache.org>
Subject [GitHub] spark pull request #15089: [SPARK-15621] [SQL] Support spilling for Python U...
Date Tue, 13 Sep 2016 23:56:52 GMT
GitHub user davies opened a pull request:

    https://github.com/apache/spark/pull/15089

    [SPARK-15621]  [SQL] Support spilling for Python UDF

    ## What changes were proposed in this pull request?
    
    When execute a Python UDF, we buffer the input row into as queue, then pull them out to
join with the result from Python UDF. In the case that Python UDF is slow or the input row
is too wide, we could ran out of memory because of the queue. Since we can't flush all the
buffers (sockets) between JVM and Python process from JVM side, we can't limit the rows in
the queue, otherwise it could deadlock.
    
    This PR will manage the memory used by the queue, spill that into disk when there is no
enough memory (also release the memory and disk space as soon as possible). 
    
    ## How was this patch tested?
    
    Added unit tests. Also manually ran a workload with large input row and slow python UDF
(with  large broadcast) like this:
    
    ```
    b = range(1<<24)
    add = udf(lambda x: x + len(b), IntegerType())
    df = sqlContext.range(1, 1<<26, 1, 4)
    print df.select(df.id, lit("adf"*10000).alias("s"), add(df.id).alias("add")).groupBy(length("s")).sum().collect()
    ```
    
    It ran out of memory (hang because of full GC) before the patch, ran smoothly after the
patch.


You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/davies/spark spill_udf

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/spark/pull/15089.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #15089
    
----
commit 4964b9a611ed01aaa5252ac642df94db07a38868
Author: Davies Liu <davies@databricks.com>
Date:   2016-09-13T23:47:31Z

    spill the buffer for Python UDF into disk

----


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