Repartitioning and coalescing in Apache Spark
Efficient data partitioning plays a crucial role in optimizing data processing workflows in Apache Spark. Repartitioning and coalescing are operations that allow you to control the distribution of data across partitions. In this section, we’ll explore the concepts of repartitioning and coalescing and their significance in Spark applications.
Understanding data partitioning
Data partitioning in Apache Spark involves dividing a dataset into smaller, manageable units called partitions. Each partition contains a subset of the data and is processed independently by different worker nodes in a distributed cluster. Proper data partitioning can significantly impact the efficiency and performance of Spark applications.
Repartitioning data
Repartitioning is the process of redistributing data across a different number of partitions. This operation can help balance data distribution, improve parallelism, and optimize data processing...