Why Do Spark SQL UDFs Sometimes Cause Cartesian Products Instead of Outer Joins?

UDFs and Cartesian Products

Understanding the Problem

In Spark SQL, using custom User-Defined Functions (UDFs) in SQL queries can sometimes lead to Cartesian product computations instead of the expected full outer join. This performance issue arises because the use of UDFs introduces an arbitrary and non-deterministic function, making it challenging for the optimizer to determine its value without evaluating all possible input combinations.

Solution

Unlike UDFs, the simple equality condition in a full outer join (t1.foo = t2.bar) has a predictable behavior. The optimizer can shuffle t1 and t2 rows based on foo and bar, respectively, to efficiently compute the join.

Preventing the Cartesian Product

Short of modifying the Spark SQL engine, there is no straightforward way to force an outer join over the Cartesian product that a UDF introduces. This limitation stems from the inherent nature of UDFs, which require evaluating all possible argument combinations to determine their value.

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