Spark Programming in Python for Beginners with Apache Spark 3 - Creating and Using UDF

The video tutorial explains how to create and use User Defined Functions (UDFs) in Apache Spark. It covers the creation of a UDF to standardize gender data, registration of the UDF for use in both column and SQL expressions, and the differences between these registration methods. The tutorial also demonstrates how to transform data using the 'withColumn' method and how to query the Spark catalog for registered functions.

What are the two types of expressions mentioned for creating expressions in Spark?

• String expressions and SQL expressions

• Column expressions and Row expressions

• Data expressions and Function expressions

• SQL expressions and Data expressions

What is the purpose of the 'parse gender' function?

• To calculate the average age

• To filter out null values

• To standardize gender values

• To sort the data frame

Which method is used to transform a single column in a data frame?

• withColumn

• filter

• groupBy

• select

What is the first step in using a user-defined function in Spark?

• Register it as a SQL function

• Create the function

• Add it to the catalog

• Use it in a data frame

What is the default return type of a UDF if not specified?

• Float type

• Boolean type

• String type

• Integer type

How is a UDF registered for use in SQL expressions?

• Using the withColumn method

• By directly using it in a query

• Through the SparkSession UDF registration method

• By adding it to the Spark catalog

What is the difference between registering a UDF for data frame expressions and SQL expressions?

• SQL UDFs cannot be used in data frames

• Data frame UDFs are faster

• SQL UDFs are not serialized

• Data frame UDFs do not create a catalog entry

Which function is used to parse a SQL expression at runtime in Spark?

• execute

• evaluate

• expr

• parse

What happens to the gender field after using the withColumn transformation?

• It is duplicated

• It is removed from the data frame

• It remains unchanged

• It is transformed to a standardized format

What is the purpose of the expr function in Spark?

• To execute SQL queries

• To parse and resolve expressions at runtime

• To filter data frames

• To join multiple data frames