Databricks Certified Associate Developer for Apache Spark: Pairs of arguments cannot be used in DataFrame.join() to perform inner join.

Question

Which of the following pairs of arguments cannot be used in DataFrame.join() to perform an inner join on two DataFrames, named and aliased with “a” and “b” respectively, to specify two key columns?

A. on = [a.column1 == b.column1, a.column2 == b.column2]
B. on = [col(“column1”), col(“column2”)]
C. on = [col(“a.column1”) == col(“b.column1”), col(“a.column2”) == col(“b.column2”)]
D. All of these options can be used to perform an inner join with two key columns.
E. on = [“column1”, “column2”]

Answer

D. All of these options can be used to perform an inner join with two key columns.

Explanation

The correct answer is D. All of these options can be used to perform an inner join with two key columns.

In Apache Spark, DataFrame.join() provides multiple ways to specify the join condition. The join condition can be expressed using column expressions, column names, or a combination of both.

Let’s go through each option:

A. on = [a.column1 == b.column1, a.column2 == b.column2]
This option uses column expressions to specify the join condition. It compares column1 from DataFrame “a” with column1 from DataFrame “b” and column2 from DataFrame “a” with column2 from DataFrame “b”. This is a valid way to specify the join condition for an inner join on two key columns.

B. on = [col(“column1”), col(“column2”)]
This option uses col() function to specify the join condition. It selects column1 and column2 from both DataFrames. This is also a valid way to specify the join condition for an inner join on two key columns.

C. on = [col(“a.column1”) == col(“b.column1”), col(“a.column2”) == col(“b.column2”)]
This option combines column expressions and aliases to specify the join condition. It compares column1 from DataFrame “a” with column1 from DataFrame “b” using aliases, and column2 from DataFrame “a” with column2 from DataFrame “b” using aliases. This is another valid way to specify the join condition for an inner join on two key columns.

E. on = [“column1”, “column2”]
This option directly uses column names to specify the join condition. It selects column1 and column2 from both DataFrames. This is also a valid way to specify the join condition for an inner join on two key columns.

All of the given options are valid and can be used to perform an inner join with two key columns. Therefore, the correct answer is D. All of these options can be used to perform an inner join with two key columns.

Reference

• pandas.DataFrame.join — pandas 2.0.2 documentation (pydata.org)
• pandas.DataFrame.merge — pandas 2.0.2 documentation (pydata.org)
• Combining Data in pandas With merge(), .join(), and concat() – Real Python
• Tutorial: Work with PySpark DataFrames on Databricks | Databricks on AWS
• Tutorial: Work with PySpark DataFrames on Azure Databricks – Azure Databricks | Microsoft Learn
• PySpark Join Multiple Columns – Spark By {Examples} (sparkbyexamples.com)
• python – How to join on multiple columns in Pyspark? – Stack Overflow
• How to join on multiple columns in Pyspark? – GeeksforGeeks
• PySpark Join on Multiple Columns | Join Two or Multiple Dataframes (educba.com)
• How to join multiple columns in PySpark Azure Databricks? (azurelib.com)
• Solved: Is there a better method to join two dataframes an… – Databricks – 30559

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