MDX: Multidimensional Expressions

Multidimensional Expressions (MDX)

Multidimensional Expressions (MDX) is a query language for OLAP (Online Analytical Processing) databases. It is used to retrieve data from cubes and to perform calculations on that data. MDX is a powerful and flexible language that allows you to express complex queries and calculations in a concise way.

Introduction to MDX

MDX was developed by Microsoft for its Analysis Services platform. It is designed to work with the multidimensional data structures that are common in OLAP cubes, such as dimensions, hierarchies, levels, and members. MDX queries are used to slice and dice data, perform aggregations, and calculate complex business metrics.

Key Concepts

• Cubes: The fundamental multidimensional data structure that MDX queries against.
• Dimensions: Represent categories of data, such as Time, Geography, or Products.
• Hierarchies: Organize members within a dimension into a tree-like structure (e.g., Year > Quarter > Month).
• Levels: Represent specific points in a hierarchy (e.g., the 'Month' level within the 'Time' dimension).
• Members: The individual data points within a level (e.g., 'January', 'February', or a specific product name).
• Measures: Numerical data that is aggregated, such as Sales Amount, Quantity, or Cost.
• Tuples: An ordered set of members, one from each of one or more dimensions. Tuples define a specific intersection within the cube.
• Sets: An ordered collection of tuples.

Syntax Overview

An MDX query typically consists of the following clauses:

• SELECT: Specifies the data to be returned. It defines the axes of the query (rows, columns, pages, etc.).
• FROM: Specifies the cube or cubes from which to retrieve data.
• WHERE: Filters the data by specifying a slicer context.
• WITH: Allows you to define calculated members and named sets.

Here's a basic structure:

SELECT
    {[Measures].[Internet Sales Amount], [Measures].[Reseller Sales Amount]} ON COLUMNS,
    {[Date].[Calendar Year].&[2003], [Date].[Calendar Year].&[2004]} ON ROWS
FROM
    [Adventure Works]
WHERE
    ([Geography].[City].&[London])
            

Example Queries

1. Basic Sales Amount by Year

Retrieve the total sales amount for each year.

SELECT
    [Measures].[Internet Sales Amount] ON COLUMNS,
    [Date].[Calendar Year].Members ON ROWS
FROM
    [Adventure Works]
            

2. Sales by Product Category and Month

Show sales for each product category for a specific month.

SELECT
    [Measures].[Internet Sales Amount] ON COLUMNS,
    [Product].[Category].Members ON ROWS
FROM
    [Adventure Works]
WHERE
    ([Date].[Month of Year].&[5]) -- May
            

3. Top 5 Customers by Sales

Find the top 5 customers based on their total sales.

SELECT
    TOP 5
    [Measures].[Internet Sales Amount] ON COLUMNS,
    [Customer].[Customer Name].Members ON ROWS
FROM
    [Adventure Works]
ORDER BY
    [Measures].[Internet Sales Amount] DESC
            

Best Practices

• Use Aliases: Make your queries more readable by aliasing dimensions, hierarchies, levels, and members.
• Understand the Cube Schema: Familiarize yourself with the cube's dimensions, hierarchies, and measures before writing queries.
• Use Named Sets: Pre-define frequently used sets of members or tuples to simplify your queries.
• Leverage Calculated Members: Define custom calculations within your queries rather than relying solely on predefined measures.
• Optimize for Performance: Be mindful of query complexity, especially when dealing with large datasets. Use slicers effectively.
• Use INTELISENSE: If using a tool like SQL Server Management Studio (SSMS), leverage IntelliSense for MDX to help with syntax and object discovery.

Further Resources

• MDX Syntax Reference (Microsoft Docs)
• MDX Scripting Statements (Microsoft Docs)
• MDX Tutorial (olap.com)