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

Database startup and recovery


Recovery of memory-optimized tables is performed in an optimized manner compared to disk-based tables.

The In-Memory OLTP engine gathers information on which checkpoints are currently valid and their locations. Each checkpoint file pair (delta and data) is identified, and the delta file is used to filter out rows from the data file (delete data doesn't need to be recovered). The In-Memory OLTP engine creates one recovery thread per CPU core, allowing for parallel recovery of memory-optimized tables. These recovery threads load the data from the data and delta files, creating the schema and all indexes. Once the checkpoint files have been processed, the tail of the transaction log is replayed from the timestamp of the latest valid checkpoint.

The ability to process recovery in parallel is a huge performance gain and allows objects that are memory-optimized to be recovered in a much shorter time than if only serial recovery was available.