map vs flatMap in Spark

map	flatMap
Returns single element based on the function/custom business logic/algorithm	Returns zero or more elements based on the function/custom business logic/algorithm
Single element as return type	Iterator with our return values, but we don’t return an iterator of RDD’s, return RDD that consists of the elements from all the iterators.
e.g. val list = 1 to 5 toList list: List[Int] = List(1, 2, 3, 4, 5) list.map(_.to(3)) List[scala.collection.immutable.Range.Inclusive] = List(Range(1, 2, 3), Range(2, 3), Range(3), Range(), Range())	E.g. val list = 1 to 5 toList list: List[Int] = List(1, 2, 3, 4, 5) list.flatMap(_.to(3)) List[Int] = List(1, 2, 3, 2, 3, 3)
Returns RDD of lists; refer to the example	Returns RDD of elements
map operation only	Equivalent to map followed by flatten
Returns the elements of the RDD	Returns the elements of the Iterators returned as

Another good example:
 val myse = Seq("India","China")
myse: Seq[String] = List(India, China)

val myseqrdd = sc.parallelize(myse)
myseqrdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[0] at parallelize at <console>:26

 myseqrdd.flatMap(_.toUpperCase)
res2: org.apache.spark.rdd.RDD[Char] = MapPartitionsRDD[1] at flatMap at <console>:26

 myseqrdd.flatMap(_.toUpperCase).collect
res3: Array[Char] = Array(I, N, D, I, A, C, H, I, N, A)

 myseqrdd.map(_.toUpperCase).collect
res5: Array[String] = Array(INDIA, CHINA)