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Learn T-SQL Querying

You're reading from   Learn T-SQL Querying A guide to developing efficient and elegant T-SQL code

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781837638994
Length 456 pages
Edition 2nd Edition
Languages
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Authors (2):
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Pedro Lopes Pedro Lopes
Author Profile Icon Pedro Lopes
Pedro Lopes
Pam Lahoud Pam Lahoud
Author Profile Icon Pam Lahoud
Pam Lahoud
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Table of Contents (18) Chapters Close

Preface 1. Part 1: Query Processing Fundamentals
2. Chapter 1: Understanding Query Processing FREE CHAPTER 3. Chapter 2: Mechanics of the Query Optimizer 4. Part 2: Dos and Don’ts of T-SQL
5. Chapter 3: Exploring Query Execution Plans 6. Chapter 4: Indexing for T-SQL Performance 7. Chapter 5: Writing Elegant T-SQL Queries 8. Chapter 6: Discovering T-SQL Anti- Patterns in Depth 9. Part 3: Assembling Our Query Troubleshooting Toolbox
10. Chapter 7: Building Diagnostic Queries Using DMVs and DMFs 11. Chapter 8: Building XEvent Profiler Traces 12. Chapter 9: Comparative Analysis of Query Plans 13. Chapter 10: Tracking Performance History with Query Store 14. Chapter 11: Troubleshooting Live Queries 15. Chapter 12: Managing Optimizer Changes 16. Index 17. Other Books You May Enjoy

Features that rely on the Query Store

When the QS was first introduced in SQL Server 2016, it was turned off by default to avoid potential impact on production workloads, as Microsoft just didn’t know how it would behave with the millions of workload permutations executing out in the wild. Over time, there have been numerous scalability improvements, and better default settings were introduced in Azure and SQL Server 2019, both from customer input and Microsoft Engineering’s own experience with the QS. This resulted in the ability to have QS turned on by default for new databases, starting with SQL Server 2022.

Having the QS enabled by default is great for database professionals, but perhaps more importantly, it becomes part of an entire feedback system that gives the SQL Database Engine rich information about query performance. Some of that information can be used to ensure the Query Optimizer can shortcut certain decisions, learn from past performance degradation...

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