Partitioning and repartitioning

Partitioning is a way to split the data into multiple chunks that can be processed in parallel by different nodes in a cluster. Repartitioning is a way to change the number or the distribution of partitions in an existing dataset. Both partitioning and repartitioning are important techniques to optimize the performance and scalability of Spark applications.

In this recipe, you will learn how to partition and repartition data using Spark DataFrames in Python. You will also learn how to choose the appropriate partitioning method and number of partitions for your use case and how to deal with some common issues and challenges related to partitioning.

How to do it…

1. Import the required libraries: Start by importing the necessary libraries for working with Delta Lake. In this case, we need the delta module and the SparkSession class from the pyspark.sql module:

   from pyspark.sql import SparkSession

   from pyspark.sql.functions import rand,...