Cardinality estimates play a crucial role in the performance of SQL Server queries. Ever since SQL Server 2014 introduced the new Cardinality estimates, there has been a lot of discussion and exploration around this topic. In this blog post, we will delve into the concept of cardinality estimates and discuss a useful tool called Query_optimizer_estimate_cardinality XEvent that can help troubleshoot cardinality estimate issues.

Understanding Cardinality Estimates

Cardinality estimates refer to the estimated number of rows that will be returned by a query. These estimates are used by the SQL Server query optimizer to choose the most efficient execution plan for a given query. Accurate cardinality estimates are crucial for the optimizer to make optimal decisions and produce efficient query plans.

Prior to SQL Server 2014, it was challenging to understand how the code estimates cardinality. However, with the introduction of the Query_optimizer_estimate_cardinality XEvent, we now have a powerful tool to troubleshoot cardinality estimate issues and gain insights into how the cardinality of each operator is derived.

Using Query_optimizer_estimate_cardinality XEvent

The Query_optimizer_estimate_cardinality XEvent provides valuable information about the cardinality estimation process. By enabling this XEvent, you can access fields such as Calculator, Create_time, Input_relation, Stats_collection, and Stats_collection_id. These fields help in understanding the strategy used for the estimate, the time at which the query was compiled, the input relation on which the cardinality is being estimated, and the statistics collection generated for the input.

To view the properties of a specific operator in an execution plan, you can hover over the operator and choose “Properties”. This will open a “Properties” window where you can find the StatsCollectionID attribute. This attribute provides additional information about the statistics collection generated for the input.

Exploring Cardinality Estimates

Now that we have an understanding of the Query_optimizer_estimate_cardinality XEvent, it’s time to explore its practical applications. If you have the opportunity to work with this XEvent, I encourage you to share your experiences and any interesting insights you have gained through the comments section below.

Some potential use cases for this XEvent include:

• Troubleshooting queries with suboptimal cardinality estimates
• Identifying the specific operators or code sections responsible for inaccurate estimates
• Analyzing the statistics collections used in cardinality estimation

By leveraging the Query_optimizer_estimate_cardinality XEvent, you can gain a deeper understanding of how SQL Server estimates cardinality and identify areas for optimization in your queries.

Thank you for reading this blog post. I hope you found it informative and useful in your SQL Server journey. If you have any questions or would like to share your experiences with cardinality estimates, please feel free to leave a comment below.