To understand data shuffling in Spark, we first need to understand how data is partitioned in RDDs. When we create an RDD by, for instance, loading a file from HDFS, or reading a file in local storage, Spark has no control over what bits of data are distributed in which partitions. This becomes a problem for key-value RDDs: these often require knowing where occurrences of a particular key are, for instance to perform a join. If the key can occur anywhere in the RDD, we have to look through every partition to find the key.
To prevent this, Spark allows the definition of a partitioner on key-value RDDs. A partitioner is an attribute of the RDD that determines which partition a particular key lands in. When an RDD has a partitioner set, the location of a key is entirely determined by the partitioner, and not by the RDD's history, or the number of keys. Two different RDDs with the same partitioner will map the same key to the same partition.
Partitions impact performance...
