Analysis Services Tutorials

Aggregation Design Strategies in SQL Server Analysis Services

Effective aggregation design is crucial for optimizing the performance of SQL Server Analysis Services (SSAS) cubes. Aggregations are pre-calculated summaries of data that allow queries to return results much faster, especially for large datasets. This article explores various aggregation design strategies and best practices.

Understanding Aggregations

In SSAS, aggregations are stored in measure groups. When a user queries a cube, SSAS first checks if the required data can be satisfied by existing aggregations. If so, it uses the pre-calculated results, leading to significantly improved query response times. If not, it resorts to calculating the data on the fly, which can be slow.

Key Aggregation Design Strategies

1. ROLAP vs. MOLAP vs. HOLAP

The storage mode of your data greatly influences how aggregations are handled:

• ROLAP (Relational OLAP): Data and aggregations are stored in the relational source database. Performance relies heavily on the underlying database. Aggregations are typically managed by the relational database engine.
• MOLAP (Multidimensional OLAP): Data and aggregations are stored in the SSAS multidimensional database. This offers the best query performance as aggregations are native to SSAS.
• HOLAP (Hybrid OLAP): Data is stored in the relational source database, but aggregations are stored in the SSAS database. This provides a balance between performance and scalability.

For most scenarios requiring high query performance, MOLAP or HOLAP is preferred for storing aggregations.

2. The Aggregation Wizard

SSAS provides an Aggregation Wizard that can automatically generate aggregation designs based on usage patterns or predefined rules. This is a powerful tool, especially for initial cube design.

The wizard offers several options:

• Best: Generates aggregations that provide the best possible performance gains, potentially leading to a large aggregation storage size.
• Used space: Aims to balance performance gains with the storage space required for aggregations.
• Natural: Creates aggregations that mirror the structure of the cube, often a good starting point.
• Custom: Allows you to manually define which aggregations to create.

3. Usage-Based Aggregations

One of the most effective strategies is to design aggregations based on how users actually query the cube. SSAS logs query activity, which can be used to analyze usage patterns and create or modify aggregations accordingly.

Key steps include:

1. Deploy and allow users to query the cube.
2. Enable query logging in SSAS.
3. Use the Aggregation Usage tool or third-party tools to analyze the logs.
4. Create new aggregations or modify existing ones based on frequently accessed queries.

4. Grouping Aggregations

Instead of creating aggregations for every possible combination, group dimensions into "aggregation dimensions." This means creating a single aggregation that can serve multiple related queries, reducing redundancy and storage space.

For example, if users frequently query sales by Date and Product, and also by Date and Customer, consider creating an aggregation that can satisfy both by appropriately grouping the Product and Customer dimensions.

5. Choosing Measures Wisely

Not all measures need to be aggregated. Measures that are always aggregated at the lowest grain (e.g., individual transaction amounts) or are calculated based on other aggregated measures might not benefit significantly from explicit aggregation design. Focus on measures that are frequently used in high-level summaries.

6. Understanding Degenerate Dimensions

Degenerate dimensions (e.g., Order Number, Invoice Number) are attributes that exist in a fact table but not in a separate dimension table. Aggregating over these can sometimes be less efficient. Design aggregations carefully when dealing with degenerate dimensions.

Best Practices

• Start with the Aggregation Wizard: Use it to generate an initial set of aggregations.
• Monitor Performance: Continuously monitor query performance and aggregation usage.
• Iterate and Refine: Aggregation design is an iterative process. Refine your designs based on performance monitoring and usage analysis.
• Balance Performance and Storage: Avoid creating too many aggregations, which can lead to excessive storage requirements and longer processing times.
• Use Tools: Leverage SSAS tools and external tools to analyze usage and optimize designs.

By carefully considering these strategies and best practices, you can significantly enhance the performance and user experience of your SQL Server Analysis Services solutions.