Spark Programming in Python for Beginners with Apache Spark 3 - Outer Joins in Dataframe

The video tutorial covers different types of joins in Apache Spark, focusing on inner and outer joins. It explains the limitations of inner joins, such as missing records when there is no match on both sides. The tutorial introduces outer joins, including full, left, and right outer joins, which ensure all records are included even without matches. Practical examples demonstrate how to handle null values using the collies function, providing a comprehensive understanding of join operations in data processing.

What is a key limitation of using an inner join?

• It includes all records from both tables.

• It duplicates records from the left table.

• It only includes records with matching keys from both tables.

• It removes all records from the right table.

How does an outer join differ from an inner join?

• It removes all records from the left table.

• It duplicates records from the right table.

• It includes all records from both tables, regardless of matching keys.

• It only includes records with matching keys from both tables.

What is the purpose of a full outer join?

• To include all records from the left table only.

• To include only matching records from both tables.

• To include all records from both tables, even if there are no matches.

• To include all records from the right table only.

When should you use a left outer join?

• When you want to exclude all unmatched records.

• When you want only matching records from both tables.

• When you want all records from the left table, regardless of matches.

• When you want all records from the right table.

Which join type would you use to ensure all records from the right table are included?

• Inner join

• Left outer join

• Right outer join

• Full outer join

What function can be used to replace null values in a join result?

• The replace function

• The fill function

• The coalesce function

• The substitute function

What does the coalesce function do?

• It duplicates records with null values.

• It fills null values with the first non-null value from a list of columns.

• It converts null values to zero.

• It removes all null values from the dataset.