MDX Overview

Note: Multidimensional Expressions (MDX) is a query language for OLAP (Online Analytical Processing) databases. It is used with SQL Server Analysis Services and other OLAP providers.

Multidimensional Expressions (MDX) is a powerful query language designed specifically for querying and manipulating data in OLAP (Online Analytical Processing) cubes. Developed by Microsoft, MDX is fundamental to working with SQL Server Analysis Services (SSAS) and provides a rich syntax for slicing, dicing, drilling down, and aggregating multidimensional data.

What is MDX?

MDX is a declarative language, meaning you specify what data you want to retrieve rather than how to retrieve it. This abstraction allows developers and analysts to focus on the business logic and data structure of the cube rather than the underlying physical storage mechanisms.

Key Concepts in MDX:

  • Cube: A multidimensional data structure that stores aggregated data for analysis.
  • Dimension: Represents a category of data, such as "Time," "Geography," or "Product."
  • Hierarchy: A structure within a dimension that organizes members in a parent-child relationship (e.g., Year -> Quarter -> Month).
  • Member: An individual item within a dimension hierarchy (e.g., "2023" is a member of the "Year" level in the "Time" dimension).
  • Measure: A numerical value that represents a business metric (e.g., "Sales Amount," "Quantity Sold").
  • Tuple: An ordered set of members, one from each of one or more dimensions. It represents a single point in the multidimensional space.
  • Set: An unordered collection of tuples.

Why Use MDX?

MDX offers several advantages for multidimensional data analysis:

  • Powerful Querying: Allows complex analysis, such as calculating year-over-year growth, moving averages, and rankings.
  • Performance: Optimized for querying large volumes of aggregated data stored in OLAP cubes.
  • Flexibility: Enables users to explore data from different perspectives by dynamically changing the dimensions and measures in their queries.
  • Business Intelligence Tools: Widely supported by BI tools like Microsoft Excel, Power BI (for multidimensional models), and various custom applications.

Basic MDX Query Structure

An MDX query typically consists of several clauses:

  • SELECT: Specifies the data to be returned. It defines the axes (rows and columns) of the result set.
  • FROM: Specifies the cube from which to retrieve data.
  • WHERE: (Optional) Filters the data to be returned by defining a slicer for the query context.

Example MDX Query:

This example retrieves the "Sales Amount" for "Canada" in "2023" from the "Adventure Works" cube.


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

MDX Functions and Operators

MDX provides a rich library of functions and operators to perform calculations, manipulate sets, and define complex logic. Some common categories include:

  • Set Functions: Functions that operate on sets of members or tuples (e.g., {(), {}, UNION, INTERSECT).
  • Numeric Functions: Functions for mathematical calculations and aggregations (e.g., SUM, AVG, COUNT).
  • String Functions: Functions for string manipulation.
  • Aggregate Functions: Functions that aggregate measures across specified dimensions.
  • Navigation Functions: Functions to traverse hierarchies (e.g., Parent, Children, Ancestor).
Key Takeaway: MDX is essential for deep analysis of multidimensional data in SQL Server Analysis Services. Mastering its syntax and functions unlocks the full potential of your OLAP cubes.

Further Learning