Multidimensional Expressions (MDX) is a query language used to retrieve data from OLAP (Online Analytical Processing) cubes. It is similar in syntax to SQL but is designed for multidimensional data structures, offering powerful capabilities for slicing, dicing, and aggregating data across various dimensions.
What is MDX?
MDX provides a flexible and expressive way to interact with data stored in multidimensional databases like SQL Server Analysis Services (SSAS). It allows users to formulate complex queries that can answer business questions such as:
- "What were the total sales for the 'Electronics' category in the 'North America' region during Q3 of 2023?"
- "Compare the year-over-year growth in revenue for the 'Software' product line."
- "Show the top 5 customers by profit margin for the last fiscal year."
Key Concepts in MDX
Understanding the core components of MDX is crucial for effective querying:
Tuples
A tuple is an ordered set of members, one from each dimension that is relevant to the query. It represents a single cell in the cube. For example, ([Date].[Calendar Year].&[2023], [Measures].[Sales Amount]) represents a specific sales amount for a particular year.
Sets
A set is an unordered collection of tuples. Sets are fundamental to MDX and are used extensively in various functions. A common use is to specify the rows and columns of a query.
Members
Members are individual items within a dimension hierarchy. For example, '2023' is a member of the 'Calendar Year' hierarchy within the 'Date' dimension.
Hierarchies
Dimensions often contain hierarchies, which represent different levels of aggregation. For example, a 'Geography' dimension might have a hierarchy like 'Country' -> 'State' -> 'City'.
Measures
Measures represent the numerical data you want to analyze, such as sales, profit, or quantity. They are typically stored in a measures group within the cube.
Basic MDX Syntax
An MDX query typically consists of the following clauses:
SELECT
This clause specifies the data you want to retrieve. It defines the axes of your query, which are sets of members.
SELECT
{[Measures].[Sales Amount]} ON COLUMNS,
{[Date].[Calendar Year].Members} ON ROWS
FROM [YourCubeName]FROM
This clause specifies the cube from which to retrieve data.
WHERE
This optional clause specifies a slicer, which filters the data returned by the query without placing it on an axis.
SELECT
{[Measures].[Sales Amount]} ON COLUMNS,
{[Product].[Category].Members} ON ROWS
FROM [YourCubeName]
WHERE ([Geography].[Country].&[USA])Common MDX Functions
MDX offers a rich set of built-in functions to manipulate and analyze data:
SUM(): Aggregates values.AVG(): Calculates the average.COUNT(): Counts members.HEAD(),TAIL(): Retrieves the first or last members of a set.TOP(),BOTTOM(): Retrieves the top or bottom N members based on a value.YTD()(Year-to-Date),QTD()(Quarter-to-Date),MTD()(Month-to-Date) for time-based calculations.
Example Query: Top Products by Sales
Let's find the top 3 products by sales amount for the year 2023:
SELECT
{[Measures].[Sales Amount]} ON COLUMNS,
TOP(3, [Product].[Product Name].Members, [Measures].[Sales Amount]) ON ROWS
FROM [YourCubeName]
WHERE ([Date].[Calendar Year].&[2023])Further Learning
This is a foundational overview. To master MDX, explore advanced concepts like: