How to Join Two DataFrames in Spark SQL with Identical Column Names

What steps should I follow to join two DataFrames in Spark SQL when both DataFrames contain columns with the same name?

val df1 = Seq((1, "Alice"), (2, "Bob"), (3, "Cathy")).toDF("id", "name")
val df2 = Seq((1, "HR"), (2, "Engineering"), (3, "Marketing")).toDF("id", "department")
val joinedDF = df1.join(df2, Seq("id"), "inner")

Joining two DataFrames that have columns with the same names can be tricky, especially when you need to specify how the join should be processed. In Spark SQL, you can effectively manage this by using aliasing or carefully naming your columns in the join conditions.

// Renaming columns in the first DataFrame
val renamedDF1 = df1.withColumnRenamed("id", "employee_id")
// Performing the join on the different named columns
val joinedDF = renamedDF1.join(df2, renamedDF1("employee_id") === df2("id"), "inner").select(renamedDF1("employee_id"), renamedDF1("name"), df2("department"))

Causes

• Both DataFrames have identical column names which can lead to confusion in join conditions.
• Without proper aliasing, Spark may not know which column to use for joining.

Solutions

• Use the `join` method with a key argument to specify which columns to join on, especially when the DataFrames have common column names.
• Rename the conflicting columns to unique names before performing the join, using the `withColumnRenamed` method. Alternatively, use aliasing after the join to distinguish results.

Mistake: Not specifying the join condition clearly, leading to ambiguity in the join operation.

Solution: Always provide explicit join columns when your DataFrames share column names or consider renaming columns for clarity.

Mistake: Forgetting to check for duplicates in the output after the join, which can arise if not handled properly.

Solution: Use distinct or dropDuplicates methods to manage the output DataFrame to avoid unwanted duplicates.