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

Index maintenance

While index maintenance is more of a database administration topic than a developer topic, it’s worth discussing the importance of index maintenance. As we discussed in the section on index structure, over time, INSERT, UPDATE, and DELETE operations can cause an index to become fragmented. Once the data is in memory, fragmentation doesn’t cause a noticeable performance issue, so the main concern is I/O. The SQL Database Engine has a few I/O optimizations, such as the readahead mechanism that’s used when scanning an index, that rely on the data being stored contiguously. When the data is fragmented, I/O may not be as efficient.

Another side effect of fragmentation is lower page density. A page is the smallest unit of I/O in the SQL Database Engine, so an index that contains a lot of partially empty pages will generate a lot more I/O than necessary. If the pages are full, it will take fewer of them to store the same amount of data. This is a...