Wednesday, October 12, 2011
This month Thomas Kejser and Denny Lee published the white paper: Analysis Services 2008 R2 Performance Guide. This white paper describes how business intelligence developers can apply query and processing performance-tuning techniques to their Microsoft SQL Server 2008 R2 Analysis Services OLAP solutions.
This guide contains information about building and tuning Analysis Services in SQL Server 2005, SQL Server 2008, and SQL Server 2008 R2 cubes for the best possible performance. It is primarily aimed at business intelligence (BI) developers who are building a new cube from scratch or optimizing an existing cube for better performance.
The goal of this guide is to provide you with the necessary background to understand design tradeoffs and with techniques and design patterns that will help you achieve the best possible performance of even large cubes.
Cube performance can be divided into two types of workload: query performance and processing performance. Because these workloads are very different, this paper is organized into four main sections.
Design Patterns for Scalable Cubes – No amount of query tuning and optimization can beat the benefits of a well-designed data model. This section contains guidance to help you get the design right the first time. In general, good cube design follows Kimball modeling techniques, and if you avoid some typical design mistakes, you are in very good shape.
Tuning Query Performance - Query performance directly impacts the quality of the end-user experience. As such, it is the primary benchmark used to evaluate the success of an online analytical processing (OLAP) implementation. Analysis Services provides a variety of mechanisms to accelerate query performance, including aggregations, caching, and indexed data retrieval. This section also provides guidance on writing efficient Multidimensional Expressions (MDX) calculation scripts.
Tuning Processing Performance - Processing is the operation that refreshes data in an Analysis Services database. The faster the processing performance, the sooner users can access refreshed data. Analysis Services provides a variety of mechanisms that you can use to influence processing performance, including parallelized processing designs, relational tuning, and an economical processing strategy (for example, incremental versus full refresh versus proactive caching).
Special Considerations – Some features of Analysis Services such as distinct count measures and many-to-many dimensions require more careful attention to the cube design than others. At the end of the paper you will find a section that describes the special techniques you should apply when using these features.