SSAS Aggregation Design Best Practices
Published: July 20, 2023 | By: A. Data Architect
Designing effective aggregations for SQL Server Analysis Services (SSAS) is crucial for optimizing query performance. Aggregations pre-calculate and store summarized data, significantly reducing the amount of data that needs to be scanned during query execution. This article delves into best practices and strategies for creating efficient SSAS aggregation designs.
What are Aggregations?
In SSAS, aggregations are pre-computed summaries of cube data. They are stored separately from the detailed fact data and are designed to answer common analytical queries quickly. Instead of calculating sums, averages, or counts on the fly from millions or billions of detail rows, SSAS can directly retrieve these values from the aggregation structures.
Why are Aggregations Important?
- Performance Boost: The primary reason is to dramatically improve query response times, especially for large cubes.
- Reduced CPU Load: By serving pre-calculated results, aggregations lessen the computational burden on the SSAS server.
- Improved User Experience: Faster queries lead to more interactive and productive analytical sessions.
Key Considerations for Aggregation Design
1. Understand Your Queries
The most effective aggregation design starts with a deep understanding of how users interact with your cube. Analyze common query patterns using tools like SQL Server Profiler or the SSAS Aggregation Usage feature. Identify the measures and dimensions most frequently queried and the levels of granularity involved.
2. Leverage the Aggregation Wizard
SSAS provides a powerful Aggregation Wizard that can suggest aggregation designs based on usage data. While it's a great starting point, it's not a substitute for human expertise. Always review and refine the wizard's suggestions.
3. Choose the Right Aggregation Granularity
Aggregations are defined at specific levels of dimension hierarchies. Avoid aggregating at every single level, as this can lead to excessive storage and processing overhead. Focus on the levels that are most frequently queried.
4. Select Appropriate Aggregation Operations
For each aggregation dimension level, you can specify the operations to perform (e.g., SUM, COUNT, MIN, MAX, AVG). Choose operations that align with the business needs and the nature of your measures.
5. Understand Aggregation Usage
The Aggregation Usage property allows you to control how aggregations are used. You can set it to:
- Full: The aggregation is used whenever possible.
- None: The aggregation is never used.
- Fastest: SSAS will try to use the aggregation that provides the fastest query results.
- NoneOptimized: Aggregations are ignored, but the aggregation structure is still maintained.
Typically, 'Full' or 'Fastest' are used for production aggregations.
6. Consider Fact Table Relationships
The relationships between your fact table and dimension tables influence how aggregations are materialized. Ensure your schema is designed to support efficient joins for aggregation processing.
7. Balance Storage and Performance
Each aggregation consumes storage space and increases processing time. There's a trade-off: more aggregations generally mean better performance but also higher storage costs and longer processing cycles. Aim for a balanced design that meets performance goals without becoming unmanageable.
8. Incremental Processing
For large cubes, consider enabling incremental processing for both the fact data and aggregations. This significantly reduces the time required to update the cube after data changes.
Example Scenario
Imagine a Sales cube. Users frequently query total sales by Year, Month, and Product Category. A good aggregation design might include aggregations at the 'Year' level for all products, and at the 'Month' and 'Product Category' level for the most popular categories.
Advanced Techniques
- Ratios Aggregations: Pre-calculate common ratios to speed up their retrieval.
- Attribute-Based Aggregations: Create aggregations based on specific attribute values within dimensions.
- Degenerate Dimensions: Optimize aggregations when dealing with degenerate dimensions in your fact table.
Monitoring and Maintenance
Aggregation performance is not static. Regularly monitor query performance and review your aggregation design as business requirements and query patterns evolve. Rebuild or refresh aggregations as needed.
By following these best practices, you can ensure your SSAS cubes deliver exceptional performance, empowering your users with rapid insights into their data.