[jira] [Updated] (SPARK-6416) RDD.fold() requires the operator to be commutative

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-6416?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen updated SPARK-6416:
-----------------------------
    Target Version/s: 2+

> RDD.fold() requires the operator to be commutative
> --------------------------------------------------
>
>                 Key: SPARK-6416
>                 URL: https://issues.apache.org/jira/browse/SPARK-6416
>             Project: Spark
>          Issue Type: Bug
>          Components: Documentation, Spark Core
>            Reporter: Josh Rosen
>            Priority: Critical
>
> Spark's {{RDD.fold}} operation has some confusing behaviors when a non-commutative reduce function is used.
> Here's an example, which was originally reported on StackOverflow (https://stackoverflow.com/questions/29150202/pyspark-fold-method-output):
> {code}
> sc.parallelize([1,25,8,4,2]).fold(0,lambda a,b:a+1 )
> 8
> {code}
> To understand what's going on here, let's look at the definition of Spark's `fold` operation.  
> I'm going to show the Python version of the code, but the Scala version exhibits the exact same behavior (you can also [browse the source on GitHub|https://github.com/apache/spark/blob/8cb23a1f9a3ed08e57865bcb6cc1cc7902881073/python/pyspark/rdd.py#L780]:
> {code}
>     def fold(self, zeroValue, op):
>         """
>         Aggregate the elements of each partition, and then the results for all
>         the partitions, using a given associative function and a neutral "zero
>         value."
>         The function C{op(t1, t2)} is allowed to modify C{t1} and return it
>         as its result value to avoid object allocation; however, it should not
>         modify C{t2}.
>         >>> from operator import add
>         >>> sc.parallelize([1, 2, 3, 4, 5]).fold(0, add)
>         15
>         """
>         def func(iterator):
>             acc = zeroValue
>             for obj in iterator:
>                 acc = op(obj, acc)
>             yield acc
>         vals = self.mapPartitions(func).collect()
>         return reduce(op, vals, zeroValue)
> {code}
> (For comparison, see the [Scala implementation of `RDD.fold`|https://github.com/apache/spark/blob/8cb23a1f9a3ed08e57865bcb6cc1cc7902881073/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L943]).
> Spark's `fold` operates by first folding each partition and then folding the results.  The problem is that an empty partition gets folded down to the zero element, so the final driver-side fold ends up folding one value for _every_ partition rather than one value for each _non-empty_ partition.  This means that the result of `fold` is sensitive to the number of partitions:
> {code}
>     >>> sc.parallelize([1,25,8,4,2], 100).fold(0,lambda a,b:a+1 )
>     100
>     >>> sc.parallelize([1,25,8,4,2], 50).fold(0,lambda a,b:a+1 )
>     50
>     >>> sc.parallelize([1,25,8,4,2], 1).fold(0,lambda a,b:a+1 )
>     1
> {code}
> In this last case, what's happening is that the single partition is being folded down to the correct value, then that value is folded with the zero-value at the driver to yield 1.
> I think the underlying problem here is that our fold() operation implicitly requires the operator to be commutative in addition to associative, but this isn't documented anywhere.  Due to ordering non-determinism elsewhere in Spark, such as SPARK-5750, I don't think there's an easy way to fix this.  Therefore, I think we should update the documentation and examples to clarify this requirement and explain that our fold acts more like a reduce with a default value than the type of ordering-sensitive fold() that users may expect in functional languages.



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