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From mateiz <...@git.apache.org>
Subject [GitHub] spark pull request: SPARK-1242 Add aggregate to python rdd
Date Sat, 05 Apr 2014 22:03:52 GMT
Github user mateiz commented on a diff in the pull request:

    https://github.com/apache/spark/pull/139#discussion_r11323477
  
    --- Diff: python/pyspark/rdd.py ---
    @@ -566,7 +566,32 @@ def func(iterator):
             vals = self.mapPartitions(func).collect()
             return reduce(op, vals, zeroValue)
     
    -    # TODO: aggregate
    +    def aggregate(self, zeroValue, seqOp, combOp):
    +        """
    +        Aggregate the elements of each partition, and then the results for all
    +        the partitions, using a given combine functions and a neutral "zero
    +        value."
    +
    +        The functions 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}.
    +
    +        The first function (seqOp) can return a different result type, U, than
    +        the type of this RDD. Thus, we need one operation for merging a T into an U
    +        and one operation for merging two U
    +
    +        >>> seqOp = (lambda x, y: (x[0] + y, x[1] + 1))
    +        >>> combOp = (lambda x, y: (x[0] + y[0], x[1] + y[1]))
    +        >>> sc.parallelize([1, 2, 3, 4]).aggregate((0, 0), seqOp, combOp)
    +        (10, 4)
    +        """
    +        def func(iterator):
    +            acc = zeroValue
    +            for obj in iterator:
    +                acc = seqOp(acc, obj)
    +            if acc is not None:
    --- End diff --
    
    acc will never be None, right, because you set it to zeroValue? You can probably just
`yield acc`.


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