MDX Query Generator for Basic Calculations

An essential tool for anyone starting with calculations using mdx. Generate a simple, valid MDX query by defining the core components, and understand how they fit together.

SELECT {[Measures].[Internet Sales Amount]} ON COLUMNS, {[Product].[Category].[Category].MEMBERS} ON ROWS FROM [Adventure Works] WHERE ([Date].[Calendar Year].&)

Query Components Breakdown

SELECT (COLUMNS):

SELECT (ROWS):

FROM:

WHERE:

What are calculations using mdx?

“Calculations using MDX” refers to the process of defining and retrieving data from multidimensional databases, known as OLAP cubes, using the Multidimensional Expressions (MDX) query language. Unlike SQL which queries relational tables, MDX is designed to work with dimensions, hierarchies, and measures to perform complex analytical and business intelligence tasks. These calculations can range from simple aggregations to sophisticated time-series analysis or KPI evaluations.

This calculator is designed for data analysts, business intelligence developers, and database administrators who are new to MDX. It helps you understand the fundamental structure of a query by generating the code based on the core components. While real-world calculations using mdx can be far more complex, this tool provides a solid starting point. For more advanced topics, you might want to read about MDX time series analysis.

The Basic MDX Formula and Explanation

A foundational MDX query follows a specific syntax, which is different from SQL even though it uses some of the same keywords like SELECT, FROM, and WHERE. The core job of a basic query is to define what data should be on the axes (e.g., columns and rows) of your result set, which cube to get it from, and how to slice or filter that data.

SELECT {SET_EXPRESSION_1} ON COLUMNS,
       {SET_EXPRESSION_2} ON ROWS
FROM [CUBE_NAME]
WHERE (SLICER_EXPRESSION)

Understanding the components is key to mastering calculations using mdx.

MDX Query Syntax Components

Variable	Meaning	Unit (Logical)	Typical Range / Example
SET_EXPRESSION	A set of members from a dimension. This defines what you see on an axis.	Set	{[Measures].[Sales], [Measures].[Profit]} or {[Date].[Year].MEMBERS}
AXIS	Defines the orientation of the result set. Common axes are COLUMNS (0) and ROWS (1).	Axis Identifier	ON COLUMNS, ON ROWS
CUBE_NAME	The specific OLAP cube containing the data.	Cube Identifier	[Adventure Works], [Finance]
SLICER_EXPRESSION	A member or tuple that filters the entire query context. This is what the WHERE clause specifies.	Member/Tuple	([Geography].[Country].&[USA])

Practical Examples

Example 1: Total Sales for Each Product Subcategory in 2014

Here, we want to see the total sales amount for every product subcategory, but only for the year 2014.

• Inputs:
  • Measure: [Measures].[Internet Sales Amount]
  • Dimension on Rows: [Product].[Subcategory].MEMBERS
  • Cube Name: [Adventure Works]
  • Filter (Slicer): [Date].[Calendar Year].&
• Resulting MDX:

  SELECT {[Measures].[Internet Sales Amount]} ON COLUMNS,
         {[Product].[Subcategory].MEMBERS} ON ROWS
  FROM [Adventure Works]
  WHERE ([Date].[Calendar Year].&)

Example 2: Customer Count by Country (No Slicer)

This example shows a simple count of customers for each country, without any filtering from a WHERE clause.

• Inputs:
  • Measure: [Measures].[Customer Count]
  • Dimension on Rows: [Customer].[Country].MEMBERS
  • Cube Name: [Adventure Works]
  • Filter (Slicer): (left blank)
• Resulting MDX:

  SELECT {[Measures].[Customer Count]} ON COLUMNS,
         {[Customer].[Country].MEMBERS} ON ROWS
  FROM [Adventure Works]

For more examples, see our guide on common MDX functions.

How to Use This calculations using mdx Calculator

Using this generator is a straightforward process to kickstart your journey with calculations using mdx.

1. Enter the Measure: In the “Measure” field, type the MDX unique name for the metric you want to analyze (e.g., [Measures].[Sales Amount]). This is the ‘what’ of your query.
2. Define Rows: In the “Dimension on Rows” field, specify the set of members you want to see on the vertical axis. Appending .MEMBERS is a common way to get all members of a level (e.g., [Geography].[City].MEMBERS).
3. Specify the Cube: Enter the name of your OLAP cube in the “Cube Name” field. This must be enclosed in brackets, like [Sales Data].
4. Set the Filter (Optional): If you want to filter the entire query by a specific member, use the “Filter (Slicer)” field. For example, [Date].[Fiscal Year].&.
5. Review and Copy: The calculator automatically generates the full MDX query in real-time. You can analyze the intermediate parts and copy the final result for use in tools like MDX Studio or SSMS.

Key Factors That Affect MDX Calculations

The performance and correctness of your calculations using mdx are influenced by several factors beyond the basic syntax.

• Cube Design: A well-designed cube with proper aggregations and hierarchies is the most critical factor. Poor design leads to slow and complex queries.
• Data Sparsity: Multidimensional data is often sparse (many cells are empty). The NON EMPTY keyword can significantly speed up queries by removing empty rows or columns from the result.
• Hierarchy Structure: Natural hierarchies (e.g., Day -> Month -> Year) are more efficient than unbalanced or unnatural ones.
• Calculation Complexity: Complex calculated members or nested functions can slow down query execution. It’s often better to pre-calculate complex values during the cube’s processing (ETL) phase.
• Use of `Filter()` vs. `WHERE` clause: Using the WHERE clause is generally faster for slicing the entire cube context than using the Filter() function on an axis.
• Axis Specification: Placing smaller sets on the COLUMNS axis and larger sets on the ROWS axis is a common convention that can sometimes improve readability and performance in client tools.

Understanding these factors is crucial as you move from simple queries to more advanced MDX vs SQL comparisons.

Frequently Asked Questions (FAQ)

1. What is the difference between calculations using mdx and SQL?

MDX is designed for querying multidimensional OLAP cubes, while SQL is for relational databases. MDX understands hierarchies and dimensions natively, making complex analytical queries like “year-over-year growth” much simpler than in SQL, which would require complex self-joins.

2. What does `.&` mean in an MDX expression?

The `.&` syntax is often used to specify the key of a dimension member, for example `[Date].[Calendar Year].&[2024]`. It ensures you are referencing a specific member by its unique key rather than its name.

3. How do I get members of a dimension?

You can use the `.MEMBERS` property on a level or hierarchy, like `[Product].[Category].MEMBERS`, to return a set of all members within it.

4. What are the most common MDX functions?

Some of the most used functions include `AGGREGATE()`, `YTD()` (Year-to-Date), `QTD()`, `MTD()`, `ParallelPeriod()`, and `.Children`. A full list can be found in our MDX Function List resource.

5. Are the units in this calculator adjustable?

The “units” in MDX are logical constructs—identifiers for measures, dimensions, and members—not physical units like kilograms or dollars. The calculator handles these logical units by assembling them into a valid query string.

6. Can this calculator handle complex time series calculations?

No, this is a basic generator. For advanced time intelligence like moving averages or period-over-period growth, you would need to write more complex expressions using functions like `ParallelPeriod()` or `LastPeriods()`.

7. Why is my query slow?

Performance issues often stem from querying large, sparse dimensions without using `NON EMPTY`, performing complex calculations on the fly, or an inefficient cube design. Try applying a filter in the `WHERE` clause to reduce the scope.

8. What is a “calculated member”?

A calculated member is a dimension member that is not stored in the cube but is calculated at query time using an MDX expression. For example, you could create a `[Profit Margin]` member by defining it as `[Measures].[Profit] / [Measures].[Sales]`.