Statistics on Views César A. Galindo-Legaria, Milind Joshi, Florian Waas, and Ming-Chuan Wu   View Paper (PDF)   Return to Performance (Session C1) Abstract The quality of execution plans generated by a query optimizer is tied to the accuracy of its car- dinality estimation. Errors in estimation lead to poor performance, erratic behavior, and user frus- tration. Traditionally, the optimizer is restricted to use only statistics on base table columns and derive estimates bottom-up. This approach has shortcomings with dealing with complex queries, and with rich languages such as SQL: Errors grow as estimation is done on top of estimation, and some constructs are simply not handled. In this paper we describe the creation and utiliza- tion of statistics on views in SQL Server, which provides the optimizer with statistical information on the result of scalar or relational expressions. It opens a new dimension on the data available for cardinality estimation and enables arbitrary cor- rection. We describe the implementation of this feature in the optimizer architecture, and show its impact on the quality of plans generated through a number of examples. ©2004 Association for Computing Machinery