Microsoft Docs

Introduction to MDX

Multidimensional Expressions (MDX) is a query language used to retrieve data from OLAP (Online Analytical Processing) cubes. Developed by Microsoft, MDX is the standard query language for SQL Server Analysis Services (SSAS) and is essential for anyone working with multidimensional data models in SQL Server.

Unlike standard SQL, which operates on tables and rows, MDX is designed to query data organized into dimensions, hierarchies, and measures. This structure is ideal for analytical scenarios, allowing for fast aggregation, slicing, and dicing of data.

MDX Fundamentals

MDX queries are structured to navigate and aggregate data within a multidimensional model. The core components of an MDX query involve:

• Specifying the cube or database to query.
• Defining the axes (rows, columns, slicers) of the query.
• Selecting members from dimensions to populate the axes.
• Aggregating measures.

Key Concepts in MDX

Dimensions, Hierarchies, Levels, and Members

Understanding the multidimensional model is crucial for writing effective MDX.

• Dimension: A category of data used for analysis (e.g., Time, Geography, Product).
• Hierarchy: A logical structure within a dimension that organizes members into parent-child relationships (e.g., Year -> Quarter -> Month -> Day within the Time dimension).
• Level: A distinct step within a hierarchy (e.g., Year, Quarter, Month are levels in the Time hierarchy).
• Member: An individual data point within a level (e.g., '2023' is a member of the Year level, 'North America' is a member of the Country level).

Tuples

A tuple is a set of members, one from each of one or more dimensions, that identifies a specific data point in the cube. Tuples are enclosed in parentheses and separated by commas. For example, `([Date].[Calendar Year].&[2023], [Geography].[Country].&[USA])` represents a specific point in time and a specific geographical location.

Sets

A set is an ordered collection of tuples. Sets are fundamental to MDX for defining rows, columns, and slicers. Sets can be constructed using various functions like `{}` (set constructor), `() ON COLUMNS`, `() ON ROWS`, `NON EMPTY`. For instance, `{[Date].[Calendar Year].&[2022], [Date].[Calendar Year].&[2023]}` is a set containing two members.

Axes

MDX queries define results on axes. The most common axes are the COLUMN axis and the ROW axis. A slicer axis can also be defined to filter the entire query context without appearing on the row or column output.

SELECT
    {[Measures].[Sales Amount], [Measures].[Cost Amount]} ON COLUMNS,
    {[Product].[Category].Members} ON ROWS
FROM
    [Adventure Works]
WHERE
    ([Date].[Calendar Year].&[2023], [Geography].[Country].&[USA])
                

MDX Syntax Elements

MDX syntax is rich and expressive, allowing for complex data analysis. Some key syntax elements include:

• Member Selection: Referring to specific members using their unique names (e.g., [Dimension].[Hierarchy].[Level].&[MemberKey]).
• Set Operations: Functions to create, manipulate, and combine sets (e.g., UNION, INTERSECT, EXCEPT).
• Navigation Functions: Functions to move within hierarchies (e.g., Parent(), Children(), Ancestor(), Descendants()).
• Aggregations: Functions to calculate sums, averages, counts, etc. (e.g., SUM(), AVG(), COUNT()).
• Calculations: Creating calculated members and measures using MDX expressions.

Common MDX Functions

MDX provides a wide array of functions for data manipulation and analysis. Some frequently used functions include:

• StrToSet(): Converts a string representation of a set into an actual set.
• StrToMember(): Converts a string representation of a member into an actual member.
• PeriodsToDate(): Returns a set of sibling members in a hierarchy between the first member of the hierarchy and a specified member.
• ParallelPeriod(): Returns a period in a specified ancestor hierarchy that is parallel to a specified ancestor.
• Aggregate(): Aggregates a set of members.
• TOPCOUNT(), BOTTOMCOUNT(): Returns a specified number of top or bottom members from a set based on an expression.
• Rank(): Returns the rank of a member within a set.

Example Queries

1. Sales by Year and Country

This query shows total sales amount for each year and country.

SELECT
    {[Measures].[Sales Amount]} ON COLUMNS,
    NON EMPTY {[Date].[Calendar Year].Members * [Geography].[Country].Members} ON ROWS
FROM
    [Adventure Works]
                

2. Top 5 Products by Sales Amount in 2023

This query retrieves the top 5 products based on sales in the year 2023.

SELECT
    {[Measures].[Sales Amount]} ON COLUMNS,
    TOPCOUNT(
        [Product].[Product Name].Members,
        5,
        [Measures].[Sales Amount]
    ) ON ROWS
FROM
    [Adventure Works]
WHERE
    ([Date].[Calendar Year].&[2023])
                

3. Year-over-Year Sales Growth

This query calculates the year-over-year sales growth percentage.

WITH MEMBER [Measures].[YoY Sales Growth] AS
    (
        [Measures].[Sales Amount] - ([Measures].[Sales Amount], [Date].[Calendar Year].PrevMember)
    ) / ([Measures].[Sales Amount], [Date].[Calendar Year].PrevMember)

SELECT
    {[Measures].[Sales Amount], [Measures].[YoY Sales Growth]} ON COLUMNS,
    {[Date].[Calendar Year].Members} ON ROWS
FROM
    [Adventure Works]
WHERE
    ([Geography].[Country].&[USA])
                

Conclusion

MDX is a powerful and flexible query language for exploring multidimensional data. Mastering its syntax and understanding the underlying OLAP cube structure is key to performing advanced business intelligence analysis. By leveraging MDX functions and concepts, you can extract valuable insights from your data to support informed decision-making.