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

Summary

In this chapter, we explored the internals of the SQL Database Engine’s query optimization process and defined many important concepts that any database professional writing T-SQL queries will keep coming back to, especially when troubleshooting query performance issues. The CE is a fundamental part of the SQL Database Engine’s Query Optimizer: knowing how it uses statistics and the importance of keeping updated and relevant statistics for the overall query optimization process empowers database professionals to write good queries – queries that both drive and leverage good database schema designs. But also, understanding the main estimation model assumptions allows us to account for these when writing queries and avoid pitfalls that hurt query performance. We will see these pitfalls in much more detail in Chapter 5, Writing Elegant T-SQL Queries, and Chapter 6, Discovering T-SQL Anti-Patterns in Depth.

If, at the end of the optimization process, we...