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Learn T-SQL Querying

Learn T-SQL Querying

By : Pedro Lopes, Lahoud
4.2 (9)
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Learn T-SQL Querying

Learn T-SQL Querying

4.2 (9)
By: Pedro Lopes, 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)
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1
Section 1: Query Processing Fundamentals
5
Section 2: Dos and Donts of T-SQL
10
Section 3: Assemble Your Query Troubleshooting Toolbox

The perils of SELECT *

SELECT * should be avoided in stored procedures, views, and multi-statement table-valued functions (MSTVF) because our T-SQL code might break if there are any changes to the underlying schema. For example, applications that reference SELECT * may be relying on the ordinal position rather than column names and may encounter errors if the underlying table definition is changed. Instead, fully qualify the names of columns that are relevant for our result set.

This also has important performance implications. Some application patterns may rely on reading an entire dataset and applying filters in the client layer only. For example, imagine a web application where a sales supervisor can see a report of orders registered for a given month, with details per product. The application connects to the AdventureWorks sample database and runs a query:

Dim sqlConnection1...

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