Learning about input partitions 

Partitions are subsets of files in memory or storage. In Spark, partitions are more utilized compared to the Hive system or SQL databases. Spark uses partitions for parallel processing and to gain maximum performance.

Spark and Hive partitions are different; Spark processes data in memory, whereas Hive partitions are in storage. In this recipe, we will cover three different partitions; that is, the input, shuffle, and output partitions.

Let's start by looking at input partitions.

Getting ready

Apache Spark has a layered architecture, and the driver nodes communicate with the worker nodes to get the job done. All the data processing happens in the worker nodes. When the job is submitted for processing, each data partition is sent to the specific executors. Each executor processes one partition at a time. Hence, the time it takes each executor to process data is directly proportional to the size and number of partitions. The more...