Book Image

Introducing Microsoft SQL Server 2019

By : Kellyn Gorman, Allan Hirt, Dave Noderer, Mitchell Pearson, James Rowland-Jones, Dustin Ryan, Arun Sirpal, Buck Woody
Book Image

Introducing Microsoft SQL Server 2019

By: Kellyn Gorman, Allan Hirt, Dave Noderer, Mitchell Pearson, James Rowland-Jones, Dustin Ryan, Arun Sirpal, Buck Woody

Overview of this book

Microsoft SQL Server comes equipped with industry-leading features and the best online transaction processing capabilities. If you are looking to work with data processing and management, getting up to speed with Microsoft Server 2019 is key. Introducing SQL Server 2019 takes you through the latest features in SQL Server 2019 and their importance. You will learn to unlock faster querying speeds and understand how to leverage the new and improved security features to build robust data management solutions. Further chapters will assist you with integrating, managing, and analyzing all data, including relational, NoSQL, and unstructured big data using SQL Server 2019. Dedicated sections in the book will also demonstrate how you can use SQL Server 2019 to leverage data processing platforms, such as Apache Hadoop and Spark, and containerization technologies like Docker and Kubernetes to control your data and efficiently monitor it. By the end of this book, you'll be well versed with all the features of Microsoft SQL Server 2019 and understand how to use them confidently to build robust data management solutions.
Table of Contents (15 chapters)

Statistics for columnstore indexes

Because the characteristics of a data warehouse workload are typically very different from those of a transaction workload, the methodology you follow for the maintenance of statistics will depend on the characteristics of your data. As you develop your plan, consider the following guidelines:

  • For columns that contain static values, such as certain dimension tables, reduce the frequency of updates to the statistics.
  • An ascending key column for which new values are added frequently, such as a transaction date or an order number, likely requires more frequent updates to the statistics. Consider updating the related statistics more often.
  • Consider using asynchronous statistics for workloads, such as data warehouses, that frequently execute the same query or similar queries. Query response times could be more predictable because the Query Optimizer can execute queries without waiting for up-to-date statistics.
  • Consider using synchronous...