Book Image

SQL Server 2017 Developer???s Guide

Book Image

SQL Server 2017 Developer???s Guide

Overview of this book

Microsoft SQL Server 2017 is a milestone in Microsoft's data platform timeline, as it brings in the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. This book prepares you for advanced topics by starting with a quick introduction to SQL Server 2017's new features. Then, it introduces you to enhancements in the Transact-SQL language and new database engine capabilities before switching to a different technology: JSON support. You will take a look at the security enhancements and temporal tables. Furthermore, the book focuses on implementing advanced topics, including Query Store, columnstore indexes, and In-Memory OLTP. Toward the end of the book, you'll be introduced to R and how to use the R language with Transact-SQL for data exploration and analysis. You'll also learn to integrate Python code into SQL Server and graph database implementations as well as the deployment options on Linux and SQL Server in containers for development and testing. By the end of this book, you will be armed to design efficient, high-performance database applications without any hassle.
Table of Contents (25 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
Free Chapter
1
Introduction to SQL Server 2017
Index

Summary


In this chapter, you have learned that Query Store is a great troubleshooting tool and captures all execution-relevant parameters for database queries: query details, execution plans, and runtime statistics and stores them in the same database so that they can survive after server failures. All that is done out-of-the-box, with minimal impact on the database workload.

By using Query Store, you can not only quickly identify performance regressions, but also mitigate them by forcing a well-known and previously used execution plan. Query Store will save you time and money. It also helps you to learn how your database workload changes over time and to identify queries that did not execute successfully.

With the automatic tuning feature, you can get a list of regressed queries with tuning actions recommended by Query Store, which significantly reduces troubleshooting time and even offers you fully automated tuning for queries with a regressed executed plan.

In the next chapter, you will...