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UDFs in SQL Queries: A Performance Impact Analysis
In the realm of SQL queries, the use of User-Defined Functions (UDFs) can significantly affect performance. A notable consequence is that using UDFs often leads to cartesian products instead of the expected full outer joins. Understanding the reasons behind this behavior is crucial for optimizing SQL queries and avoiding performance bottlenecks.
Why Cartesian Products with UDFs?
UDFs introduce an element of non-determinism into SQL queries. When evaluating a UDF, the optimizer cannot anticipate its output based on the input arguments alone. As a result, it must resort to a cartesian product to evaluate the function for all possible pairs of rows from the joined tables. This approach ensures that every combination of input arguments is considered, but it also drastically increases the number of rows to be processed.
Full Outer Joins vs. Cartesian Products
In contrast to cartesian products, full outer joins preserve all rows from both input tables, even if there are no matching rows. This operation is significantly less computationally expensive than a cartesian product because the optimizer can efficiently filter out unmatched rows based on join conditions.
Consequences for Performance
The cartesian product behavior introduced by UDFs can have a substantial impact on performance. Since the number of rows in a cartesian product grows exponentially with the number of rows in the input tables, even small queries with UDFs can result in a significant performance hit. This is especially true for streaming applications, where latency is critical.
Avoiding Cartesian Products with UDFs
Unfortunately, there is no direct way to force an outer join over a cartesian product in the context of UDFs. However, there are some best practices that can help mitigate the performance impact:
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