As SQL developers, we tend to think of performance tuning in terms of crafting the best table indices, avoiding scalar and table valued functions, and analyzing query plans (among other things). But sometimes going back to the spec and applying some properties of elementary math can be the best way to begin to improve performance of SQL queries which implement mathematical formulas. This article is a case study of how I used this technique to optimize my SQL implementation of the Inverse Simpson Index.
In SQL Server 2005, a feature was introduced that was hardly noticed, but which might make a great difference to anyone doing queries involving temporal data. For anyone doing Data Warehousing, timetabling, or time-based pricing, this could speed up your queries considerably. Who better to introduce this than Query Optimizer expert, Fabiano Amorim?
Microsoft SQL Server 2008 collects statistical information about indexes and column data stored in the database. These statistics are used by the SQL Server query optimizer to choose the most efficient plan for retrieving or updating data. This paper describes what data is collected, where it is stored, and which commands create, update, and delete statistics. By default, SQL Server 2008 also creates and updates statistics automatically, when such an operation is considered to be useful. This paper also outlines how these defaults can be changed on different levels (column, table, and database).