#32 Repartition vs coalsece

repartition() and coalesce() are both methods in Apache Spark used to manage the number of partitions in an RDD or DataFrame. However, they differ in their behavior and use cases:

repartition():

• repartition() is used to increase or decrease the number of partitions in an RDD or DataFrame. It involves a full shuffle of the data across the cluster.
• When you call repartition(n), Spark evenly redistributes the data into n partitions, regardless of the current number of partitions.
• It's typically used when you want to increase the level of parallelism or when you want to explicitly control the number of partitions, for example, before performing an operation that benefits from a specific partitioning scheme.

Example: df.repartition(10)

coalesce():

• coalesce() is used to decrease the number of partitions in an RDD or DataFrame. It performs a narrow transformation and tries to minimize data movement by merging partitions on the same worker node if possible.
• Unlike repartition(), coalesce() does not involve a full shuffle of the data across the cluster unless you explicitly set the shuffle parameter to True.
• It's typically used when you want to reduce the number of partitions to optimize performance or when you know that the data distribution is skewed and you want to reduce the number of partitions without incurring the overhead of a full shuffle.

Example: ?df.coalesce(5)

In summary, repartition() is used to increase or decrease the number of partitions with a full shuffle, while coalesce() is primarily used to decrease the number of partitions with a narrow transformation and optional shuffle. coalesce() is more efficient when reducing the number of partitions without significant data movement.