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

Why UDFs Lead to Cartesian Products in SQL Queries

When using SQL, user-defined functions (UDFs) can introduce unexpected performance issues. This is particularly evident in join operations, where UDFs can lead to Cartesian products instead of the desired full outer join.

Cause of Cartesian Products

The use of UDFs necessitates the evaluation of arbitrary functions with potentially infinite domains and non-deterministic behavior. To determine the value of these functions, the system must consider all possible argument combinations, resulting in a Cartesian product.

Example

Consider the SQL queries provided in the given Databricks-Question:

-- Query 1: Join without UDF
SELECT col1, col2 FROM table1 AS t1 JOIN table2 AS t2 ON t1.foo = t2.bar;

-- Query 2: Join with UDF
SELECT col1, col2 FROM table1 AS t1 JOIN table2 AS t2 ON equals(t1.foo, t2.bar);

In Query 1, the simple equality condition allows for data shuffling based on the foo and bar columns, producing the expected result. However, in Query 2, the use of the equals UDF necessitates evaluating the function for all possible pair combinations, resulting in a Cartesian product.

Solution

Forcing an outer join over a Cartesian product is generally not possible without modifying the Spark SQL engine. However, optimizing the UDF itself to reduce the number of evaluations could mitigate some of the performance degradation.

The above is the detailed content of Why Do UDFs in SQL Joins Sometimes Produce Cartesian Products Instead of Expected Joins?. For more information, please follow other related articles on the PHP Chinese website!