When it comes to troubleshooting query performance issues in SQL Server, one of the most important tools at your disposal is the execution plan. The execution plan provides a detailed breakdown of the steps performed during the execution of a query, allowing you to identify potential bottlenecks and optimize your queries for better performance.

Reading a Graphical Execution Plan

The graphical representation of the execution plan is the most preferred method for developers and database administrators. It uses operator icons and arrows to represent the various steps and data flow direction of the query. Before diving into the details, it’s important to start by analyzing the final operator, as it contains important information about the query plan.

For example, the cardinality estimation model provides an estimate of the number of rows returned by each operator, which can help you understand the expected performance of the query. The memory grant info indicates the amount of memory allocated for temporary row data during query execution. The optimization level specifies the level of optimization performed by the query optimizer, and the wait stats show which wait types occur during query execution.

Analyzing the Execution Plan

Let’s take a look at an example query and analyze its execution plan:

SELECT Sl.CarrierTrackingNumber, 
       (SELECT Color.Color 
        FROM Production.ProductListColors Color 
        WHERE Color.ProductID = Sl.ProductID) AS ProductColor 
FROM Sales.SalesOrderDetailEnlarged Sl 
WHERE SalesOrderID > 131336 
  AND ModifiedDate < '20110601';

Starting with the select operator, we can examine its attributes to gain insights into the query plan. The query memory grant attribute indicates the amount of memory used for temporary row data, while the non-parallel plan reason explains why the query optimizer did not generate a parallel plan. The optimization level attribute tells us that all query optimization phases were completed, and the wait stats attribute shows any wait types encountered during query execution.

Now, let’s dive into the graphical representation of the execution plan. The plan can be read from top to bottom and right to left. The clustered index seek operator, numbered as 1, indicates that the data engine is finding the matching rows using the b-tree structure. This is an efficient way to read the data, as the clustered index contains all rows in the leaf level index pages.

Hovering over the clustered index operator reveals more details, such as the range scan method used to find the matching rows. The scan direction attribute shows the direction of the scan, and the tooltip provides additional information about the operator.

One interesting aspect of the execution plan is the difference between the number of rows read and the actual number of rows returned. This can be explained by the seek predicate and residual predicate. The seek predicate is the initial filter applied to the data using the b-tree structure, while the residual predicate is a secondary filter applied after the seek predicate to filter non-key columns.

In our example, the index seek operator initially accesses 4,603,975 rows, but after applying the residual predicate, only 6,069 rows match the criteria. This can result in additional I/O due to reading irrelevant rows.

Another operator to note is the stream aggregate operator, which performs aggregation operations. In our example, it returns one row that includes the number of records for each ProductID and the color value using the ANY function.

The execution plan also includes other operators, such as the assert operator, which validates certain conditions in the query plan, and the nested loop operator, which combines rows using a left outer join.

Impact of Unique Constraints

Adding a unique constraint to a table can also impact the query plan. For example, when a unique constraint is added to the ProductID column, the query optimizer knows that the table does not contain any duplicate rows for that column. This knowledge can lead to changes in the execution plan, resulting in improved performance.

Conclusion

Understanding and interpreting the SQL Server execution plan is crucial for optimizing query performance. By analyzing the plan operators and arrows, you can identify potential bottlenecks and make informed decisions to improve the efficiency of your queries. The execution plan provides valuable insights into the steps performed during query execution, allowing you to fine-tune your queries for better performance.