How to Configure Aggregations in Analysis Services Multidimensional Models

This topic guides you through the process of configuring aggregations for your multidimensional models in SQL Server Analysis Services (SSAS). Aggregations are pre-calculated summaries of data that significantly improve query performance by reducing the need to scan large fact tables.

Tip: Proper aggregation design is crucial for optimizing the performance of your SSAS solutions.

Understanding Aggregations

Aggregations in SSAS allow you to define specific combinations of dimension attributes that are pre-calculated. When a query requests data that can be fulfilled by an aggregation, SSAS retrieves the pre-calculated result instead of performing a complex calculation on the fly. This can dramatically reduce query response times, especially for complex analytical queries.

Steps to Configure Aggregations

Open the Solution in SQL Server Data Tools (SSDT)

Launch SQL Server Data Tools and open your Analysis Services project. Navigate to the Multidimensional Models designer.

Select the Cube

In the Solution Explorer, double-click on the cube for which you want to configure aggregations. This will open the Cube Designer.

Navigate to the Aggregations Tab

Within the Cube Designer, select the Aggregations tab. This tab provides tools for managing and creating aggregations.

Use the Aggregation Wizard

Click the Aggregation Wizard button. This wizard will guide you through the process of creating new aggregations or modifying existing ones.

The wizard typically involves the following phases:

Select Aggregations: Choose the measure group for which you want to create aggregations.
Define Aggregation Designs: The wizard can suggest aggregation designs based on common query patterns, or you can manually select dimensions and attributes to include in aggregations.
Select Aggregation Options: Configure options such as the level of aggregation detail and whether to use a predefined aggregation design.

Manually Create Aggregations (Optional)

If you prefer more control, you can manually create aggregations. In the Aggregations tab, you can:

Create New Aggregation: Right-click in the aggregation design pane and select New Aggregation.
Select Measures: Choose the measures that will be included in the aggregation.
Select Dimensions and Attributes: Specify which dimension hierarchies and attributes will be used to group the measures.

You can also select "all" for a dimension to aggregate across all its members.

Generate Aggregations

Once you have defined your aggregation design, you need to process the cube to generate the aggregations. Right-click on the cube in Solution Explorer and select Process.... Choose to process the entire cube or just the affected parts.

Best Practices for Aggregations

Start with the Aggregation Wizard: The wizard provides intelligent suggestions that can save time and improve performance.
Understand Your Queries: Analyze your most frequent and performance-critical queries to design aggregations that will benefit them.
Balance Performance and Storage: Aggregations improve query performance but also increase the size of your cube. Avoid creating too many aggregations.
Regularly Review and Optimize: As your data and query patterns evolve, periodically review your aggregation design and optimize it as needed.
Consider Usage-Based Optimization: After deploying your cube and collecting usage data, you can use usage-based optimization tools to refine your aggregation design.

Warning: Unnecessary or overly complex aggregations can lead to increased processing times and excessive storage consumption without providing significant performance benefits.

Example

Consider a Sales cube with a Time dimension and a Product dimension. If you frequently query total sales by Year and by Product Category, you might create an aggregation that pre-calculates sums of SalesAmount at the Year and Product Category level. This would significantly speed up queries that request this specific combination of data.

You could define an aggregation like this:


Aggregation Design Name: SalesByYearAndCategory
Measure Group: Sales
Measures: SalesAmount
Dimensions:
  - Time (Hierarchy: Calendar, Level: Year)
  - Product (Hierarchy: Product, Level: Category)

Conclusion

Configuring aggregations is a powerful technique for optimizing the performance of your Analysis Services multidimensional models. By carefully designing and implementing aggregations, you can ensure that your users experience fast and responsive analytical queries.