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Performance comparison between CROSS APPLY and INNER JOIN
CROSS APPLY is often considered a more efficient alternative to INNER JOIN when working with large data sets. While both techniques may produce similar execution plans under certain scenarios, CROSS APPLY can provide significant performance advantages in some cases.
Definition of CROSS APPLY
CROSS APPLY is a table-valued function that creates a new row for each input row based on a specified expression. Unlike INNER JOIN, it does not require join conditions between tables.
Applicable scenarios for CROSS APPLY
CROSS APPLY is useful when dealing with complex operations that cannot be easily defined using simple join conditions. For example, selecting a specified number of records from a second table for each row in the first table is a task that CROSS APPLY can handle easily. Here is a query that demonstrates this use case:
<code class="language-sql">SELECT t1.*, t2o.* FROM t1 CROSS APPLY ( SELECT TOP 3 * FROM t2 WHERE t2.t1_id = t1.id ORDER BY t2.rank DESC ) t2o</code>
In the above query, CROSS APPLY selects the three latest records from the 't2' table for each row in the 't1' table. This operation is particularly useful when extracting related data from different tables.
Performance comparison
To illustrate the performance advantages of CROSS APPLY, consider the following scenario:
When executing a query using CTE and window functions to simulate CROSS APPLY, the query takes approximately 30 seconds. However, when using CROSS APPLY directly, the query executes immediately. This demonstrates the efficiency of CROSS APPLY in handling complex operations involving row-by-row iteration.
Conclusion
In summary, while INNER JOIN is the standard way to combine data from multiple tables, CROSS APPLY has advantages in specific situations, especially when dealing with complex operations that require row-by-row processing. By understanding the benefits of CROSS APPLY, developers can optimize the performance of their SQL queries and handle complex data extraction tasks more efficiently.
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