Writing custom UDFs in Apache Spark

In this recipe, we will discuss how to write custom UDFs in Apache Spark. Writing UDFs in Apache Spark provides the flexibility and expressiveness necessary to perform custom data transformations and computations. UDFs enable you to extend the capabilities of Spark’s built-in functions, integrate with external libraries, and achieve specific data processing requirements in a scalable and distributed manner. We will use Python as our primary programming language and the PySpark API.

How to do it...

1. Import the libraries: Import the required libraries and create a SparkSession object:

   from pyspark.sql import SparkSession

   from pyspark.sql.functions import explode, col

   spark = (SparkSession.builder

               .appName("write-udfs")

               .master("spark://spark-master:7077")

       ...