Optimizing Spark jobs by minimizing data shuffling

In this recipe, you will learn how to optimize Spark jobs by minimizing data shuffling. Data shuffling is the process of transferring data across different partitions or nodes. Data shuffling can be expensive and time-consuming, as it involves network I/O, disk I/O, and serialization/deserialization of data. Therefore, minimizing data shuffling is one of the key techniques for optimizing Spark performance.

Some of the most common scenarios when data shuffling occurs are the following:

• When you perform a join operation on two or more DataFrames: Joining requires shuffling data across partitions or nodes based on the join keys
• When you perform a global aggregation operation on a DataFrame: Global aggregation requires shuffling data across partitions or nodes to compute some statistics for the whole DataFrame
• When you perform a repartition or coalesce operation on a DataFrame: Repartitioning or coalescing requires shuffling...