Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/139#discussion_r10616781
--- Diff: python/pyspark/rdd.py ---
@@ -566,8 +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:
+ yield acc
+ return self.mapPartitions(func).reduce(combOp)
--- End diff --
nit: new line after
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