Book Image

Expert Cube Development with Microsoft SQL Server 2008 Analysis Services

Book Image

Expert Cube Development with Microsoft SQL Server 2008 Analysis Services

Overview of this book

Microsoft's SQL Server Analysis Services 2008 is an OLAP server that allows users to analyze business data quickly and easily. However, designing cubes in Analysis Services can be a complex task: it's all too easy to make mistakes early on in development that lead to serious problems when the cube is in production. Learning the best practices for cube design before you start your project will help you avoid these problems and ensure that your project is a success. This book offers practical advice on how to go about designing and building fast, scalable, and maintainable cubes that will meet your users' requirements and help make your Business Intelligence project a success. This book gives readers insight into the best practices for designing and building Microsoft Analysis Services 2008 cubes. It also provides details about server architecture, performance tuning, security, and administration of an Analysis Services solution. In this book, you will learn how to design and implement Analysis Services cubes. Starting from designing a data mart for Analysis Services, through the creation of dimensions and measure groups, to putting the cube into production, we'll explore the whole of the development lifecycle. This book is an invaluable guide for anyone who is planning to use Microsoft Analysis Services 2008 in a Business Intelligence project.
Table of Contents (17 chapters)
Expert Cube Development with Microsoft SQL Server 2008 Analysis Services
Credits
About the Authors
About the Reviewers
Preface
Index

Caching


We've already seen how Analysis Services can cache the values returned in the cells of a query, and how this can have a significant impact on the performance of a query. Both the Formula Engine and the Storage Engine can cache data, but may not be able to do so in all circumstances; similarly, although Analysis Services can share the contents of the cache between users there are several situations where it is unable to do so. Given that in most cubes there will be a lot of overlap in the data that users are querying, caching is a very important factor in the overall performance of the cube and as a result ensuring that as much caching as possible is taking place is a good idea.

Formula cache scopes

There are three different cache contexts within the Formula Engine, which relate to how long data can be stored within the cache and how that data can be shared between users:

  • Query Context, which means that the results of calculations can only be cached for the lifetime of a single query...