Understanding Aggregation Strategies in Analysis Services
By: Jane Doe, Senior BI Developer
Date: October 26, 2023
In Microsoft SQL Server Analysis Services (SSAS), achieving optimal query performance is paramount for a responsive business intelligence experience. One of the most effective techniques for improving query speed is the strategic use of aggregations. Aggregations are pre-computed summaries of data that allow Analysis Services to retrieve answers to common queries much faster than by scanning the underlying fact tables.
What are Aggregations?
At its core, an aggregation is a pre-calculated result of a measure over one or more dimensions. For instance, instead of calculating the sum of sales for each day, month, and year individually on every query, you can pre-calculate these sums and store them as aggregations. When a user queries for total sales by year, Analysis Services can directly retrieve this value from the aggregation, significantly reducing processing time.
Types of Aggregation Designs
Analysis Services provides several ways to design and implement aggregations:
1. MOLAP Aggregations
These are the most common and performant type of aggregations. MOLAP (Multidimensional Online Analytical Processing) aggregations are stored directly within the Analysis Services database, optimized for fast retrieval. They are built during the cube processing operation.
2. ROLAP Aggregations
When using ROLAP (Relational Online Analytical Processing), aggregations are not stored within Analysis Services. Instead, SSAS generates SQL queries against the relational data source to compute the aggregated data on the fly. This approach can be slower but reduces storage overhead within SSAS.
3. HOLAP Aggregations
HOLAP (Hybrid Online Analytical Processing) offers a balance between MOLAP and ROLAP. It stores the dimension data in MOLAP format and fact data (including aggregations) in ROLAP format. This can be a good compromise for large fact tables.
Designing Effective Aggregations
Creating an efficient aggregation design involves understanding user query patterns and cube structure. Here are key strategies:
- Analyze Usage Patterns: Use the Aggregation Usage Worksheet in SQL Server Management Studio (SSAS) or query logs to identify frequently queried measure/dimension combinations.
- Automated Aggregation Design: SSAS provides tools to automatically generate aggregation designs based on usage data. This is a great starting point.
- Define Fact Table Granularity: Ensure your fact table is at the lowest desired granularity.
- Choose Appropriate Dimensions: Aggregations are most effective when built over commonly used dimension attributes. Avoid aggregating over very high cardinality attributes unless absolutely necessary.
- Consider Cache: Properly configure caching to hold frequently accessed aggregations in memory.
- Regular Processing: Cube processing is when aggregations are built or updated. Schedule regular processing to reflect new data and maintain performance.
Aggregation Strategies to Consider:
- Degree of Parallelism (DOP): When processing aggregations, SSAS can leverage multiple CPU cores to speed up the process.
- Aggregation Builder: This tool helps in selecting the best aggregations to create based on performance and storage trade-offs. It suggests a "Reduced" or "Full" aggregation design.
- Pre-computed Aggregations vs. On-the-fly Calculation: For dimensions with few members and frequently accessed measures, pre-computed aggregations are usually best. For dimensions with many members or less frequently accessed measures, on-the-fly calculation might suffice.
Example Scenario: Sales Cube
Consider a sales cube with a 'Sales' measure, and dimensions for 'Date', 'Product', and 'Geography'. Common queries might be:
- Total Sales by Year
- Total Sales by Product Category
- Total Sales by Country
- Total Sales by Product and Month
An aggregation design would pre-calculate these sums. For instance, an aggregation covering 'Date' (at Year level) and 'Geography' (at Country level) would enable very fast retrieval of total sales by country and year.
Conclusion
Mastering aggregation strategies is a fundamental skill for any Analysis Services developer. By intelligently pre-computing data summaries, you can transform a slow-performing cube into a highly responsive analytical tool, empowering users to gain insights quickly and efficiently.
Keywords: SQL Server Analysis Services, SSAS, BI, Aggregations, Performance Tuning, MOLAP, ROLAP, HOLAP, Cube Design, Business Intelligence