aggregate

First lets see how parallelize splits your data between partitions:

val x = sc.parallelize(List("12","23","345","4567"), 2)
x.glom.collect
// Array[Array[String]] = Array(Array(12, 23), Array(345, 4567))

val y = sc.parallelize(List("12","23","345",""), 2)
y.glom.collect
// Array[Array[String]] = Array(Array(12, 23), Array(345, ""))
and define two helpers:

def seqOp(x: String, y: String) =  math.min(x.length, y.length).toString
def combOp(x: String, y: String) = x + y
Now lets trace execution for x. Ignoring parallelism it can be represented as follows:

(combOp (seqOp (seqOp "" "12") "23") (seqOp (seqOp "" "345") "4567"))
(combOp (seqOp "0" "23") (seqOp (seqOp "" "345") "4567"))
(combOp "1" (seqOp (seqOp "" "345") "4567"))
(combOp "1" (seqOp "0" "4567"))
(combOp "1" "1")
"11"
The same thing for y:

(combOp (seqOp (seqOp "" "12") "23") (seqOp (seqOp "" "345") ""))
(combOp (seqOp "0" "23") (seqOp (seqOp "" "345") ""))
(combOp "1" (seqOp (seqOp "" "345") ""))
(combOp "1" (seqOp "0" ""))
(combOp "1" "0")
"10"