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

One additional point to note here is that, as the basic principle of Spark RDD is immutability. The repartition or coalesce will create new RDD. The base RDD will continue to have existence with its original number of partitions. In case the use case demands to persist RDD in cache, then the same has to be done for the newly created RDD.

scala> pairMrkt.repartition(10)
res16: org.apache.spark.rdd.RDD[(String, Array[String])] =MapPartitionsRDD[11] at repartition at :26

scala> res16.partitions.length
res17: Int = 10

scala>  pairMrkt.partitions.length
res20: Int = 2