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

Introducing the Cardinality Estimator

Before we get started, it’s important to have a common frame of reference for a few terms that will be referenced throughout this book:

  • Cardinality: Cardinality in a database is defined as the number of records, also called tuples, in each table or view.
  • Density: This term represents the average number of duplicate values in each column or column set – in other words, the average distribution of unique values in the data. It’s defined as 1 divided by the number of distinct values.
  • Frequency: This term represents the average number of occurrences of a given value in a column or column set. It’s defined as the number of rows times the density.
  • Selectivity: This term represents the fraction of the row count that satisfies a given predicate, between zero and one. This is calculated as the predicate cardinality (Pc) divided by the table cardinality (Tc) multiplied by 100: (Pc ÷ Tc) × 100. As...