Measures and Aggregations - SQL Server Analysis Services Documentation

Measures and Aggregations in SQL Server Analysis Services

Measures are fundamental to OLAP (Online Analytical Processing) cubes in SQL Server Analysis Services (SSAS). They represent the numerical data that users analyze, such as sales revenue, units sold, or profit margins. Aggregations are the functions used to combine these measures across various dimensions.

Understanding Measures

A measure is typically a column from a fact table in your data source that contains quantitative data. In SSAS, you define measures to enable users to perform calculations and gain insights. Key aspects of measures include:

Base Measures: These are directly derived from source columns.
Calculated Measures: These are defined using MDX (Multidimensional Expressions) or DAX (Data Analysis Expressions) to perform custom calculations based on base measures or other calculated measures.

Aggregation Functions

When a user views data at a higher level of a dimension (e.g., viewing total sales by year instead of by month), SSAS needs to aggregate the measure values. Common aggregation functions used in SSAS include:

SUM: Adds all values.
COUNT: Counts the number of items.
MIN: Finds the minimum value.
MAX: Finds the maximum value.
AVERAGE: Calculates the average value.
DISTINCTCOUNT: Counts the number of unique values.

The aggregation behavior for a measure is defined in the cube definition and can be configured to optimize performance and ensure correct results. You can specify a default aggregation function, and also define custom aggregations for specific scenarios.

Defining Measures in SSAS

When creating a cube in SSAS, you typically drag and drop measure group columns into the Measures pane. For each measure, you can specify:

Name: A user-friendly name for the measure.
Source Column: The underlying column from the fact table.
Aggregation Function: The default function to use for aggregation.
Format String: How the measure should be displayed (e.g., currency, percentage).

Tip: For measures that represent counts (like distinct customers), ensure you use the appropriate aggregation function like `DISTINCTCOUNT` for accurate reporting.

MDX and DAX for Measures

Complex calculations often require MDX or DAX. For example, to calculate a profit margin measure, you might use an MDX expression like:

                
                    CREATE MEMBER CURRENTCUBE.[Measures].[Profit Margin] AS 

                    ( [Measures].[Sales Amount] - [Measures].[Cost Amount] ) / [Measures].[Sales Amount], 

                    FORMAT_STRING = "Percent", 

                    CAPTION = "Profit Margin"

Understanding how to leverage these languages is crucial for creating sophisticated analytical models.

Performance Considerations

The way measures are aggregated significantly impacts query performance. SSAS uses pre-aggregated data in its storage engine (ROLAP, MOLAP, HOLAP) to speed up queries. Properly defining aggregation designs for your measures is a critical step in performance tuning.

Key Takeaways

Measures are the quantifiable data points in your cube.
Aggregations define how these measures are combined.
Choose appropriate aggregation functions for accurate analysis.
MDX and DAX are used for defining calculated measures and complex logic.
Performance is heavily influenced by measure aggregation design.