UDFs in Spark SQL: Why Do They Sometimes Create Cartesian Products Instead of Full Outer Joins?

UDFs vs Full Outer Joins: Understanding the Cartesian Product Behavior

In Spark SQL, utilizing user-defined functions (UDFs) within SQL queries can introduce unexpected behavior, particularly the emergence of Cartesian products instead of intended full outer joins.

Cause of Cartesian Product with UDFs

When employing UDFs, Spark treats them as arbitrary functions, considering every possible argument combination for evaluation. This necessitate a Cartesian product to ensure thorough examination of all pairs.

Absence of Predictability with UDFs

Unlike UDFs, basic equality comparisons like t1.foo = t2.bar possess predictable behavior, allowing Spark to efficiently shuffle t1 and t2 rows based on the equality criteria. This optimization is absent with UDFs due to their unpredictable nature.

Distinction between Outer Join and Natural Join

In relational algebra, an outer join is fundamentally expressed as a natural join, which is merely an optimization in popular SQL engines. Therefore, it's crucial to recognize that forcing an outer join over a Cartesian product with UDFs is not readily feasible without altering the Spark SQL engine itself.

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