It’s been a while since my last post about SQL Server performance optimization. This one focuses on index design and how the order of columns in your indexes can make or break your database queries. Using my insights from real-world consulting experience, this guide should help you understand the critical decisions that would impact your database performance.
How does SQL Server decide where to look first when executing a query? The answer lies in the structure of your indexes. Learn how the sequence of key columns can optimize your data retrieval, making your database faster and more responsive.
Understanding how B+ index seeks work in SQL Server is crucial for determining the best order of key columns. When an index has more than one key column, the database engine tries to efficiently locate and retrieve the desired data.
Seek Predicate: This is the condition applied during the index seek operation. For optimal performance, the leading key columns in the index should align with the columns specified in the equality conditions of the query’s WHERE clause. The index seek predicate is instrumental in narrowing down the data set, making subsequent operations more efficient.
Seek Predicate (Multi-Column): When more than one column is being filtered as part of an Index Seek operation, it works as a “nested” B+ tree. In other words, once the seek on the first key column is done (i.e. the requested value is found), “under” that one value you would find another “sub-tree” for the next column in line.
Illustration of a multi-column index
Predicate (Leaf-Level): After the index seek, the leaf-level predicate comes into play. This involves additional conditions, such as inequalities, ranges, or additional filters, applied to the retrieved rows. However, these conditions are evaluated at the leaf level of the index structure. To minimize the impact of the leaf-level predicate, consider the order of columns used in range conditions or inequalities.
