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

Using Live Query Statistics

To meet the need to analyze a query execution plan while the query is executing, Live Query Statistics (LQS) was introduced with the SQL Server 2016 release of SSMS, adding rich visuals by animating the in-flight execution plan to allow more immediate and precise identification of hot spots in a plan during query execution.

To see LQS in action, open a new query window in SSMS, in which we can use the following example query from Chapter 2, Mechanics of the Query Optimizer. This could be a previously identified long-running query that was created to troubleshoot and tune:

SELECT e.[BusinessEntityID], p.[Title], p.[FirstName],
     p.[MiddleName], p.[LastName], p.[Suffix], e.[JobTitle],
     pp.[PhoneNumber], pnt.[Name] AS [PhoneNumberType],
     ea.[EmailAddress], p.[EmailPromotion], a.[AddressLine1],
     a.[AddressLine2], a.[City], sp.[Name] AS [StateProvinceName...