Understanding the Performance Issue with Outer Apply

Why Does the Query Take a Long Time?

When working with data queries, especially those involving joins and subqueries, performance can be a significant concern. In this article, we’ll delve into a specific problem that arises when using the Outer Apply operator in SQL Server, which is often referred to as the “outer apply takes a long time” issue.

The problem presented involves a query with a Common Table Expression (CTE) and an Outer Apply clause. The CTE returns approximately 3 million rows, and the Outer Apply clause is used to join this result with another table. However, when the Outer Apply clause includes a string comparison, such as a keyword comparison in this case, the query performance suffers significantly.

What is Outer Apply?

An Overview of the Operator

The Outer Apply operator is a type of join that allows for the joining of two tables based on a condition. Unlike an inner join, which only returns rows where there are matches in both tables, the outer apply operator will return all rows from the left table (the table on which the operator is applied) and matching rows from the right table.

The syntax for using Outer Apply in SQL Server is as follows:

SELECT column1, column2, ...
FROM table1 AS T1
OUTER APPLY (
    SELECT TOP 1 column2, column3, ...
    FROM table2 AS T2
    WHERE condition = T1.columnX
) AS T2

In this example, T1 is the left table, and T2 is the right table. The inner query returns only one row (as specified by the TOP 1 clause), and the outer apply operator joins this result with the rows in the left table.

Why Does the Query Take a Long Time?

When the Outer Apply clause includes a string comparison, such as the keyword comparison in the problem presented, it can lead to a significant performance issue. This is because the optimizer will often choose to use an index scan instead of a full table scan, which can be much slower.

In this case, when the CTE returns 3 million rows and the Outer Apply clause includes a string comparison, the optimizer may decide to use an index scan on the keyword column. This is because the index is likely to contain all possible values for the keyword column, making it easier for the database engine to find matching rows.

However, this index scan can be much slower than a full table scan, especially when dealing with large datasets like 3 million rows. As a result, the query takes a long time to finish.

How Can We Improve Performance?

Including Keyword in the Index

One way to improve performance is to include the keyword column in an index on the right table (the table being joined by the outer apply operator). This allows the database engine to use a more efficient join algorithm that can take advantage of the index.

When creating an index, it’s essential to consider the columns used in the WHERE clause and any other conditions applied to the join. In this case, including the keyword column in the index will allow the database engine to quickly find matching rows without having to perform a full table scan.

Here is an example of how you might create an index on the right table:

CREATE INDEX IX_KW Keyword ON Table2 (Keyword)

By creating this index, we can improve performance and reduce the query time significantly.

Additional Considerations

Indexing Other Columns

In addition to including the keyword column in the index, it’s also essential to consider indexing other columns used in the join. In this case, Project_Id, SE_Id, and Domain are all columns that may be used in future joins.

By indexing these columns as well, we can ensure that the database engine has the necessary tools to efficiently join the tables.

Avoiding Full Table Scans

Another important consideration is avoiding full table scans whenever possible. In this case, the query uses a CTE and an outer apply operator, which can lead to full table scans if not optimized correctly.

By including indexes on the columns used in the join and using efficient join algorithms, we can reduce the likelihood of full table scans and improve overall performance.

Conclusion

Optimizing Queries with Outer Apply

When working with queries involving Outer Apply, it’s essential to consider the potential performance issues that can arise. By indexing the columns used in the join and avoiding full table scans, we can significantly improve query performance and reduce the time required to complete the query.

In this article, we discussed the specific problem of outer apply taking a long time when it contains string comparisons like keyword comparison, and explored ways to optimize queries with outer apply. By understanding how the Outer Apply operator works and using indexing techniques effectively, we can write more efficient queries that meet our performance requirements.

Last modified on 2025-01-27