SQL Server 2019: Performance implementing Recursive Common Table Expressions (CTEs) in Large Datasets
I'm testing a new approach and I'm sure I'm missing something obvious here, but I recently switched to I'm stuck on something that should probably be simple. I'm working with important performance optimization when using recursive Common Table Expressions (CTEs) in SQL Server 2019. I have a table, `Employees`, with a self-referential hierarchy structure for organizational purposes. The recursion is intended to retrieve all subordinates under a specific manager. Here’s the code I’m currently using: ```sql WITH RecursiveCTE AS ( SELECT EmployeeID, ManagerID, Name FROM Employees WHERE EmployeeID = @ManagerId UNION ALL SELECT e.EmployeeID, e.ManagerID, e.Name FROM Employees e INNER JOIN RecursiveCTE r ON e.ManagerID = r.EmployeeID ) SELECT * FROM RecursiveCTE; ``` When I run this query on a dataset with over 10,000 employees, the execution time skyrockets, and in some cases, it times out with the behavior: "Execution Timeout Expired". I’ve tried indexing the `ManagerID` column to speed up the joins, but it hasn’t made a noticeable difference. Additionally, I attempted to limit the recursion depth by adding an option like `OPTION (MAXRECURSION 50)`, but it only changes the behavior message when it hits the limit instead of improving performance. Does anyone have insights on optimizing this type of query or alternative approaches for hierarchies in SQL Server? Perhaps using a different method like table-valued parameters or SQL Server graph databases? Any help would be appreciated! I'm working on a application that needs to handle this. Any help would be greatly appreciated! Am I approaching this the right way? I'm developing on Linux with Sql. Is this even possible? What would be the recommended way to handle this?