Why Do UDFs in SQL Queries Sometimes Produce Cartesian Products Instead of Outer Joins?

UDFs in SQL Queries and Cartesian Products

The use of user-defined functions (UDFs) in SQL queries can lead to a Cartesian product instead of the intended full outer join. A Cartesian product occurs when all rows from one table are combined with all rows from another table, resulting in a much larger dataset than a full outer join.

Why Does a UDF Cause a Cartesian Product?

UDFs introduce an additional level of complexity that prevents optimization by the query engine. A UDF may accept any number of arguments with non-deterministic behavior. To evaluate the UDF for all possible combinations of rows, the query engine must perform a Cartesian product.

In contrast, a simple equality comparison between columns (e.g., t1.foo = t2.bar) has a predictable behavior. The query engine can use this to optimize the join by shuffling rows based on the foo and bar columns, avoiding the need for a Cartesian product.

Enforcing Outer Joins

Unfortunately, there is no straightforward way to force an outer join over a Cartesian product in the above example. The only option would be to modify the Spark SQL engine.

As explained above, the Cartesian product is a consequence of the arbitrary and non-deterministic nature of UDFs. The query engine cannot optimize them without introducing additional constraints.

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