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 the Query plan comparison section, we were able to take a query plan from the production environment that was not performing as expected and validate that when running the same query in the dev environment with a production-like database we were able to get a consistent reproduction of the issue. Then, through comparative analysis of the cached query plan from production (an estimated execution plan) and the actual execution plan from dev, we could create hypotheses from the data we observed until we found the root cause. Last, we tested a fix for the root cause of the issue by hinting at queries, which again, by comparing plans, determined that the new plan was better than the old plan, which should now be implemented in production.

In the Query plan analyzer section, we were able to take a query plan that had been captured in the production environment through an XEvent trace and get started on finding what could be negatively affecting performance by using this new...