Book Image

Learn T-SQL Querying

By : Pedro Lopes, Pam Lahoud
Book Image

Learn T-SQL Querying

By: Pedro Lopes, Pam Lahoud

Overview of this book

Transact-SQL (T-SQL) is Microsoft's proprietary extension to the SQL language used with Microsoft SQL Server and Azure SQL Database. This book will be a usefu to learning the art of writing efficient T-SQL code in modern SQL Server versions as well as the Azure SQL Database. The book will get you started with query processing fundamentals to help you write powerful, performant T-SQL queries. You will then focus on query execution plans and leverage them for troubleshooting. In later chapters, you will explain how to identify various T-SQL patterns and anti-patterns. This will help you analyze execution plans to gain insights into current performance, and determine whether or not a query is scalable. You will also build diagnostic queries using dynamic management views (DMVs) and dynamic management functions (DMFs) to address various challenges in T-SQL execution. Next, you will work with the built-in tools of SQL Server to shorten the time taken to address query performance and scalability issues. In the concluding chapters, this will guide you through implementing various features, such as Extended Events, Query Store, and Query Tuning Assistant, using hands-on examples. By the end of the book, you will have developed the skills to determine query performance bottlenecks, avoid pitfalls, and discover the anti-patterns in use.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: Query Processing Fundamentals
5
Section 2: Dos and Donts of T-SQL
10
Section 3: Assemble Your 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 development (dev) environment with a production-like database, we were able consistently response the issue. And then, through comparative analysis of the cached query plan from production (an estimated execution plan) and the actual execution plan from dev, we created 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 the query, which, again by comparing plans, determined that the new plan was better than the old plan, and 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...