Spark - repartition() vs coalesce()

Question

According to Learning Spark

Keep in mind that repartitioning your data is a fairly expensive operation. Spark also has an optimized version of

Answer 1

In a simple way COALESCE :- is only for decreases the no of partitions , No shuffling of data it just compress the partitions

REPARTITION:- is for both increase and decrease the no of partitions , But shuffling takes place

Example:-

val rdd = sc.textFile("path",7)
rdd.repartition(10)
rdd.repartition(2)

Both works fine

But we go generally for this two things when we need to see output in one cluster,we go with this.