Book Image

Mastering SQL Server 2017

By : Miloš Radivojević, Dejan Sarka, William Durkin, Christian Cote, Matija Lah
Book Image

Mastering SQL Server 2017

By: Miloš Radivojević, Dejan Sarka, William Durkin, Christian Cote, Matija Lah

Overview of this book

Microsoft SQL Server 2017 uses the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. By learning how to use the features of SQL Server 2017 effectively, you can build scalable apps and easily perform data integration and transformation. You’ll start by brushing up on the features of SQL Server 2017. This Learning Path will then demonstrate how you can use Query Store, columnstore indexes, and In-Memory OLTP in your apps. You'll also learn to integrate Python code in SQL Server and graph database implementations for development and testing. Next, you'll get up to speed with designing and building SQL Server Integration Services (SSIS) data warehouse packages using SQL server data tools. Toward the concluding chapters, you’ll discover how to develop SSIS packages designed to maintain a data warehouse using the data flow and other control flow tasks. By the end of this Learning Path, you'll be equipped with the skills you need to design efficient, high-performance database applications with confidence. This Learning Path includes content from the following Packt books: SQL Server 2017 Developer's Guide by Miloš Radivojevi?, Dejan Sarka, et. al SQL Server 2017 Integration Services Cookbook by Christian Cote, Dejan Sarka, et. al
Table of Contents (20 chapters)
Title Page
Free Chapter
1
Introduction to SQL Server 2017

Performance considerations

One of the main concerns about JSON in SQL Server 2016 is performance. As mentioned, unlike XML, JSON is not fully supported; there is no JSON data type. Data in XML columns is stored as binary large objects (BLOBs). SQL Server supports two types of XML indexes that avoid parsing all the data at runtime to evaluate a query and allow efficient query processing. Without an index, these BLOBs are shredded at runtime to evaluate a query. As mentioned several times, there is no JSON data type; JSON is stored as simple Unicode text and the text has to be interpreted at runtime to evaluate a JSON query. This can lead to slow reading and writing performance for large JSON documents. The primary XML index indexes all tags, values, and paths within the XML instances in an XML column. The primary XML index is a shredded and persisted representation of the XML...