Apache Spark: Difference between map() and flatMap() transformations

In Apache Spark, the map() and flatMap() transformations are both used to process data in distributed datasets. However, there is a key difference between the two:

• map(): This transformation takes a function as input and applies it to each element of the RDD, resulting in a new RDD with the transformed elements. The output RDD has the same number of elements as the input RDD.
• flatMap(): In contrast, the flatMap() transformation takes a function that returns an iterator of elements for each input element. It then flattens these iterators of elements into a single RDD. This means that the output RDD may have a different number of elements compared to the input RDD.

So, while both map() and flatMap() are used for transforming data in Apache Spark, the key distinction lies in how they handle the output data structure.