Book Image

Learn T-SQL Querying - Second Edition

By : Pedro Lopes, Pam Lahoud
Book Image

Learn T-SQL Querying - Second Edition

By: Pedro Lopes, Pam Lahoud

Overview of this book

Data professionals seeking to excel in Transact-SQL (T-SQL) for Microsoft SQL Server and Azure SQL Database often lack comprehensive resources. This updated second edition of Learn T-SQL Querying focuses on indexing queries and crafting elegant T-SQL code, catering to all data professionals seeking mastery in modern SQL Server versions and Azure SQL Database. Starting with query processing fundamentals, this book lays a solid foundation for writing performant T-SQL queries. You’ll explore the mechanics of the Query Optimizer and Query Execution Plans, learning how to analyze execution plans for insights into current performance and scalability. Through dynamic management views (DMVs) and dynamic management functions (DMFs), you’ll build diagnostic queries. This book thoroughly covers indexing for T-SQL performance and provides insights into SQL Server’s built-in tools for expedited resolution of query performance and scalability issues. Further, hands-on examples will guide you through implementing features such as avoiding UDF pitfalls, understanding predicate SARGability, Query Store, and Query Tuning Assistant. By the end of this book, you‘ll have developed the ability to identify query performance bottlenecks, recognize anti-patterns, and skillfully avoid such pitfalls.
Table of Contents (18 chapters)
1
Part 1: Query Processing Fundamentals
4
Part 2: Dos and Don’ts of T-SQL
9
Part 3: Assembling Our Query Troubleshooting Toolbox

Functions in our predicate

Search predicates should only use deterministic function calls. Calls to non-deterministic functions with columns for parameters cause the SQL Database Engine to be unable to reference the selectivity of those columns, as the result of the function is unknown at compile time. Because of this, they cause unnecessary scans.

Keep in mind what was discussed in previous chapters: that the Query Optimizer uses statistics and some internal transformation rules and heuristics at compile time to determine a good enough plan to execute a query; and how the WHERE clause is one of the first to be evaluated during logical query processing. The Query Optimizer depends on the estimated cost to resolve the search predicates to choose whether to do seeks or scans over indexes.

The following example shows a query executed in the AdventureWorks sample database that uses non-deterministic function calls in the search predicate:

SELECT SalesOrderID, OrderDate
FROM Sales...