Mostly taken from [3]
The RDD is how Spark simplifies complex operations like join or groupBy and hides the fact that under the hood, you’re dealing with fragmented data.
The number of partitions is important because a stage in Spark will operate on one partition at a time (and load the data in that partition into memory). Consequently, if you have fewer partitions than active stages, you will wind up under-utilizing your cluster. Furthermore, since with fewer partitions there’s more data in each partition, you increase the memory pressure on your program. On the flip side, with too many partitions, your performance may degrade as you take a greater hit from network and disk I/O.