Decoding MDX: A Comprehensive Guide to Multidimensional Data Querying
In the intricate domain of data analytics and business intelligence, the ability to efficiently query and manipulate vast datasets stored in complex, multidimensional structures is paramount. Enter MDX, or Multi-Dimensional eXpressions, a specialized query language designed precisely for this purpose. Unlike the familiar two-dimensional landscape of relational databases, MDX navigates the intricate, n-dimensional constructs characteristic of analytical processing. This comprehensive exploration will demystify MDX, elucidating its fundamental concepts, dissecting its query syntax, explaining its diverse clauses, and shedding light on its powerful functions and operators, ultimately showcasing its indispensable role in extracting profound insights from multidimensional data environments.
The Core of Multidimensionality: Understanding MDX Fundamentals
MDX is not merely another query language; it is a standards-based query language specifically tailored for retrieving data from multidimensional databases, often referred to as cubes. These analytical structures are the bedrock of Online Analytical Processing (OLAP), enabling rapid, interactive analysis of large data volumes from multiple perspectives. MDX serves as the primary conduit for interacting with these sophisticated data models, supporting distinct operational modes to cater to various analytical needs.
While MDX shares superficial syntactic similarities with SQL, the ubiquitous language for relational databases, its underlying philosophy and conceptual framework are profoundly divergent. SQL is inherently designed to operate on two-dimensional tables, where data is organized into rows and columns. In stark contrast, MDX is purpose-built to navigate and extract information from multidimensional cubes, which, by their very nature, typically encompass more than two dimensions. This fundamental difference necessitates a distinct approach to data retrieval, leveraging concepts unique to the OLAP paradigm.
MDX is not a proprietary creation; it is an open standard, notably part of the OLE DB for OLAP specification championed by Microsoft. Its widespread adoption across the business intelligence ecosystem is a testament to its efficacy and versatility. Numerous prominent OLAP providers, including but not limited to Microstrategy’s Intelligence Server, Hyperion’s Essbase Server, and SAS’s Enterprise BI Server, natively support MDX, underscoring its role as the de facto language for multidimensional data interaction. When individuals refer to MDX, they might be referencing either the comprehensive MDX query language itself or more granular MDX expressions, each playing a crucial role in the broader analytical framework.
The Foundational Cube: A Multidimensional Construct
At the heart of any multidimensional database lies the cube. This conceptual structure serves as the foundational element, encapsulating the entire analytical dataset. Unlike a flat table, a cube allows data to be viewed and analyzed along multiple axes or dimensions, providing a richer, more contextualized understanding. Each cube is meticulously designed to contain a minimum of two dimensions, often extending to many more, reflecting the complex interrelationships within a business domain. For instance, sales data might be organized by dimensions such as Time, Product, Customer, and Geography, allowing analysts to slice and dice information in myriad ways.
Sets and Tuples: Building Blocks of MDX Queries
In the MDX universe, sets and tuples are the fundamental constituents for defining data selections. A tuple represents a unique intersection of members from different dimensions. For example, (Customer.Country.Australia, Product.ProductLine.Bikes, Time.Year.2024) would be a tuple representing sales of bikes in Australia in 2024. A tuple with only one member, such as Customer.Country.Australia, can also be implicitly treated as a tuple.
A set, conversely, is an ordered collection of zero, one, or more tuples. A set containing no tuples is termed an empty set, typically represented as { }. For instance, a set could be {[Customer.Country.Australia], [Customer.Country.Canada], [Customer.Country.Australia]}. It is crucial to note that while a member of a dimension inherently forms a tuple, and can thus be directly utilized in MDX queries, curly braces are only strictly necessary when defining a set with multiple tuples or an empty set. For a single tuple, even if it constitutes a set, the curly braces can often be omitted for conciseness. This flexibility allows for more intuitive and readable query construction.
Architecting Data Retrieval: The Anatomy of MDX Queries
The fundamental structure of an MDX query is both declarative and highly expressive, enabling precise retrieval of multidimensional data. While it shares some high-level keywords with SQL, their functional roles within the multidimensional paradigm are distinct.
The canonical syntax for an MDX query adheres to the following template:
Code snippet
[WITH <formula_expression> [, <formula_expression> …]]
SELECT [ <axis_expression> , [ <axis_expression> …]]
FROM [ <cube_expression> ]
[WHERE [slicer_expression]]
In this structure, any elements enclosed within square brackets [ ] denote optional components; their inclusion is not mandatory for a valid MDX query. This indicates that the WITH clause and the WHERE clause are not always required, depending on the complexity and specificity of the data retrieval task. The keywords WITH, SELECT, FROM, and WHERE, along with their accompanying expressions, are collectively referred to as clauses. Each clause plays a specific and crucial role in defining the scope and nature of the data retrieved from the multidimensional cube.
The SELECT Statement: Orchestrating Multidimensional Views
The MDX SELECT statement is the cornerstone of data retrieval, meticulously employed to extract a specific subset of the multidimensional data housed within an OLAP cube. In the relational context of SQL, the SELECT statement permits the specification of columns to be included in a two-dimensional result set, analogous to defining X and Y axes. However, MDX transcends this two-dimensional limitation, providing the formidable capability to retrieve data along one, two, or indeed, numerous axes.
The syntax for the SELECT statement itself is:
Code snippet
SELECT [ <axis_expression> , [ <axis_expression> …]]
The axis_expressions specified immediately following the SELECT keyword are critically important; they delineate the dimensional data that the query is intended to retrieve. These particular dimensions are aptly termed axis dimensions because the data derived from them is dynamically projected onto their corresponding axes in the resultant data set.
The syntax for an axis_expression is further defined as:
Code snippet
<axis_expression> := < set > ON (axis | AXIS(axis number) | axis number)
Axis dimensions are instrumental in constructing multidimensional result sets. A set, which is a meticulously curated collection of tuples, is precisely defined to form an axis dimension. MDX provides an extraordinary capacity, allowing for the specification of up to 128 distinct axes within a single SELECT statement. For convenience and intuitive readability, the first five axes are assigned specific aliases: COLUMNS (representing axis 0), ROWS (axis 1), PAGES (axis 2), SECTIONS (axis 3), and CHAPTERS (axis 4). Beyond these named aliases, axes can also be numerically designated, which becomes essential when a query necessitates more than five dimensions in its SELECT statement, enabling truly complex analytical views.
Consider the following illustrative example:
Code snippet
SELECT Measures.[Internet Sales Amount] ON COLUMNS,
[Customer].[Country].MEMBERS ON ROWS,
[Product].[Product Line].MEMBERS ON PAGES
FROM [Adventure Works]
In this exemplar MDX query, three distinct axes are explicitly specified within the SELECT statement. Data originating from the Measures dimension (specifically Internet Sales Amount), the Customers dimension (specifically Country members), and the Product dimension (specifically Product Line members) are meticulously mapped onto these three designated axes. This arrangement constructs the axis dimensions, allowing for a three-dimensional view of sales data.
This identical statement could be equivalently, and numerically, articulated as:
Code snippet
SELECT Measures.[Internet Sales Amount] ON 0,
[Customer].[Country].MEMBERS ON 1,
[Product].[Product Line].MEMBERS ON 2
FROM [Adventure Works]
This numerical representation underscores the inherent flexibility in MDX’s axis specification, facilitating complex queries without relying solely on aliases.
The Significance of Axis Dimensions
Axis dimensions are the foundational constructs dynamically built when an MDX SELECT statement is defined. A SELECT statement meticulously specifies a unique set for each designated dimension, whether it be COLUMNS, ROWS, or any of the additional axes. Crucially, and in contradistinction to the slicer dimension (which is elaborated upon later), axis dimensions are designed to retrieve and retain data for multiple members of a dimension, rather than being confined to just single member selections. This characteristic allows for the creation of rich, comprehensive multidimensional result sets where multiple data points along each specified axis are displayed.
The FROM Clause: Defining the Data Context
The FROM clause in an MDX query is absolutely fundamental, serving the pivotal role of determining the specific cube from which data will be retrieved and subsequently analyzed. Its function is analogous to the FROM clause in a traditional SQL query, where one designates a particular table name as the data source. In MDX, however, the FROM clause is an unequivocal necessity for any valid query, establishing the primary data context.
The syntax of the FROM clause is straightforward:
Code snippet
FROM <cube_expression>
The <cube_expression> component specifically denotes the explicit name of a cube or, alternatively, a precisely defined subsection of a cube from which the desired data is to be extracted. A critical distinction from SQL’s FROM clause is that, in MDX, one is limited to defining just one cube name within this clause. This singular cube specified in the FROM clause is formally referred to as the cube context, and it dictates the operational environment within which the entire query is executed. Consequently, every component of the axis_expressions within the query is retrieved from and evaluated against this meticulously defined cube context.
Consider the following valid MDX query:
Code snippet
SELECT [Measures].[Internet Sales Amount] ON COLUMNS
FROM [Adventure Works]
This query succinctly retrieves data for the [Internet Sales Amount] measure, projecting it onto the X-axis (COLUMNS). The measure data is unequivocally sourced from the cube context designated as [Adventure Works]. While the FROM clause inherently restricts operations to a singular cube or a specific section thereof, MDX provides an advanced mechanism for accessing data from alternative cubes: the LookupCube function. When two or more cubes share common dimension members, the LookupCube function facilitates the retrieval of measures that exist outside the current cube’s context, leveraging these shared dimension members to bridge data across different analytical structures, offering a powerful avenue for inter-cube analysis.
The WHERE Clause: Slicing and Dicing Data
In the realm of relational database operations, queries are frequently constructed to return only specific subsets of the total available data within a given table, a set of joined tables, or even interconnected databases. This precise data subsetting is typically achieved through the strategic application of SQL statements that meticulously delineate which data is desired and which is to be excluded from the query’s resultant output.
Consider a simple SQL query on a table named Product that contains comprehensive sales information for various products:
SQL
SELECT *
FROM Product
Assuming this query yields five columns and four rows of data, as might be represented in a tabular format (e.g., Product ID, Product Line, Color, Weight, Sales), the asterisk * conventionally signifies «all,» indicating that the query will retrieve the entire contents of the table. However, if the objective is to extract only the Color and Product Line for each record, the query can be explicitly restricted to return just these desired columns:
SQL
SELECT ProductLine, Color
FROM Product
This modified query would return a simplified result set containing only the specified columns.
In the context of MDX, the SELECT statement is utilized to precisely identify the dimensions and members that a query will return, analogous to selecting columns in SQL. Conversely, the WHERE statement serves to limit the result set based on specific criteria, much like the WHERE clause in SQL. However, its application in MDX involves the concept of a slicer dimension.
Returning to the SQL example, if the goal was to retrieve only records where Color = ‘Silver’, the SQL WHERE clause would perform a string comparison. In MDX, members are the constituent elements that form a dimension’s hierarchy. If the Product table were modeled as a cube, it would typically contain measures such as Sales and Weight, and a Product dimension with hierarchies like ProductID, ProductLine, and Color. In this illustrative scenario, the Product table serves both as a fact table (containing measures) and a dimension table (containing descriptive attributes). An MDX query crafted to yield identical results to the SQL query (where Color is ‘Silver’) would appear as follows:
Code snippet
SELECT Measures.[Sales] ON COLUMNS,
[Product].[Product Line].MEMBERS ON ROWS
FROM [ProductsCube]
WHERE ([Product].[Color].[Silver])
This MDX query produces a result set where the Sales measure is on the COLUMNS axis and Product Line members are on the ROWS axis. The crucial difference lies in the WHERE clause: instead of a simple string comparison as in SQL, the MDX WHERE clause refers to a slice on the cube. This slice effectively filters the entire cube context to include only those products that possess the color ‘Silver’. The result would similarly display Accessories and Road product lines with their corresponding sales figures, as they are the products colored ‘Silver’. This demonstrates the fundamental shift from row-level filtering to cube-wide slicing in MDX.
The Slicer Dimension: Filtering the Cube Context
The slicer dimension is the conceptual construct established when the WHERE statement is defined within an MDX query. Its fundamental role is to act as a potent filter, meticulously removing unwanted dimensions and members from the overall cube context, thereby narrowing the scope of the data that is considered for the query’s evaluation.
The slicer dimension inherently includes any axis present in the cube, even those dimensions that are not explicitly included or referenced within any of the queried axes (i.e., COLUMNS, ROWS, PAGES, etc.). For hierarchies not explicitly incorporated into the query axes, their default members are implicitly utilized within the slicer axis, ensuring that the filter operates across the entire multidimensional space. When specific tuples are designated for the slicer axis, MDX undertakes a meticulous evaluation of these tuples as a precisely defined set. The resultant values derived from these tuples are then comprehensively aggregated, with the aggregation methodology being predicated upon the specific measures included in the primary query and the intrinsic aggregation function associated with each particular measure. This powerful filtering mechanism allows for highly targeted data analysis within the vastness of the multidimensional cube.
Expanding Query Capabilities: The WITH Clause and Calculated Members
Beyond simple data retrieval and filtering, MDX provides sophisticated mechanisms for creating dynamic, on-the-fly calculations and extending the capabilities of queries. The WITH clause is central to this advanced functionality, allowing for computations that are often essential for complex business analysis.
Often, evolving business requirements necessitate the formulation of intricate calculations that must be precisely computed within the singular scope of a specific query. The MDX WITH clause is expressly designed to furnish users with the formidable ability to create such transient calculations and seamlessly integrate them into the analytical context of the query itself. Furthermore, it empowers the retrieval of data from external sources, specifically from outside the immediate context of the current cube, through the powerful LookupCube MDX function. This inter-cube data access significantly enhances the analytical reach of MDX.
The typical categories of calculations that are judiciously created using the WITH clause encompass named sets and calculated members. In addition to these fundamental constructs, the WITH clause extends its utility to provide functionality for defining granular cell calculations, facilitating the loading of an entire cube into an Analysis Server cache for a substantial improvement in query performance, and even enabling the alteration of cell contents through programmatic calls to functions residing in external libraries. Moreover, it offers advanced capabilities such as defining solve order and pass order, which are critical for controlling the sequence of complex calculations in scenarios involving multiple interdependent computed values.
The generic syntax of the WITH clause is:
Code snippet
[WITH <formula_expression> [, <formula_expression> …]]
The specific <formula_expression> will naturally vary in accordance with the precise type of calculation being defined. Multiple calculations within a single WITH clause are delineated by commas, fostering a clear and concise structure for complex query augmentation.
Named Sets: Aliases for Complex Selections
A named set is, in essence, a convenient alias or a symbolic placeholder for an intricately defined MDX set expression. Its primary utility lies in its capacity to be employed ubiquitously throughout a query as an elegant alternative to repetitively specifying the actual, potentially convoluted, set expression. This not only enhances the readability and maintainability of complex MDX queries but also promotes reusability of common set definitions, significantly streamlining query development.
Calculated Members: Deriving New Measures and Dimensions
Calculated members represent dynamic computations precisely specified by MDX expressions. Unlike measures or members directly derived from the original fact data within the cube, calculated members are resolved and their values determined as a direct result of the meticulous evaluation of these MDX expressions during query execution. This allows for the creation of new metrics or hierarchical elements that do not physically exist in the underlying data but are analytically valuable.
The <formula_expression> within the WITH clause, specifically for defining calculated members, adheres to the following structure:
Code snippet
Formula_expression := MEMBER <MemberName> AS [‘] <MDX_Expression> [‘],
[ , SOLVE_ORDER = < integer > ]
[ ,<CellProperty> = <PropertyExpression> ]
MDX explicitly utilizes the keywords MEMBER and AS within the WITH clause as the designated syntax for creating these dynamic calculated members, clearly delineating their definition.
Consider the following illustrative example of a calculated member statement:
Code snippet
WITH MEMBER MEASURES.[Profit] AS [Measures].[Internet Sales Amount] —
[Measures].[Internet Standard Product Cost]
SELECT measures.Profit ON COLUMNS,
[Customer].[Country].MEMBERS ON ROWS
FROM [Adventure Works]
In this exemplar, a calculated member named Profit has been precisely defined as the arithmetic difference between the measures [Internet Sales Amount] and [Internet Standard Product Cost]. When this MDX query is executed, the Profit value will be dynamically computed for every country represented in the dataset, with the calculation being performed entirely based on the specified MDX expression. This demonstrates the power of calculated members to derive new, insightful metrics on-the-fly, without requiring pre-aggregation or physical storage in the cube.
The Language of Multidimensional Logic: MDX Expressions and Operators
At the heart of MDX’s computational power lie its expressions and operators. MDX expressions are self-contained fragments of MDX statements that, when evaluated, yield a specific value. These expressions are ubiquitously employed in defining complex calculations, setting default values for objects like measures or members, or constructing intricate security expressions to govern data access permissions. Typically, an MDX expression takes a member, a tuple, or a set as a parameter and returns a resultant value. If the evaluation of an MDX expression does not yield a discernible value, a Null value is conventionally returned, indicating the absence of a defined result.
Consider a simple example of an MDX expression:
Code snippet
Customer.[Customer Geography].DEFAULTMEMBER
This expression, upon evaluation, will return the default member that has been explicitly specified for the Customer Geography hierarchy within the Customer dimension. This illustrates how expressions can traverse hierarchies and retrieve specific predefined elements.
Operators: Performing Actions within MDX
An operator in MDX functions as a specialized utility, meticulously designed to perform a specific action. It accepts one or more arguments as input and, upon execution, invariably returns a resultant value. MDX encompasses several distinct categories of operators, each tailored for different computational or logical operations: arithmetic operators, logical operators, and specialized MDX operators that facilitate unique multidimensional manipulations.
Arithmetic Operators: Standard Numerical Computations
Standard arithmetic operators such as addition (+), subtraction (-), multiplication (*), and division (/) are fully available within MDX. Consistent with other programming languages, these operators are conventionally applied to two numerical operands. Additionally, the + and — operators can function as unary operators, operating on a single numerical operand within MDX expressions, as exemplified by +100 or -100, which simply denote positive or negative values respectively.
Set Operators: Manipulating Collections of Data
Beyond their conventional arithmetic roles, the +, -, and * operators are ingeniously repurposed in MDX to perform powerful operations on MDX sets.
The + operator, when applied to sets, functions as a union operator, returning a new set that comprises all unique tuples from both input sets. Example:
Code snippet
{[Customer].[Country].[Australia]} + {[Customer].[Country].[Canada]}
This expression results in the union of the two sets, yielding:
Code snippet
{[Customer].[Country].[Australia], [Customer].[Country].[Canada]}
- The — operator, when applied to sets, functions as a difference operator, returning a new set containing only those tuples present in the first set but absent from the second.
The * operator, when applied to sets, performs a cross product operation. This operation generates a new set comprising all possible combinations of the tuples from each input set. The cross product is particularly useful for retrieving data in a matrix-like format, where every combination of elements from different sets is represented. Example:
Code snippet
{[Customer].[Country].[Australia],[Customer].[Country].[Canada]} *
{[Product].[Product Line].[Mountain],[Product].[Product Line].[Road]}
This expression, representing the cross product of the two sets, yields:
Code snippet
{([Customer].[Country].[Australia],[Product].[Product Line].[Mountain]),
([Customer].[Country].[Australia],[Product].[Product Line].[Road]),
([Customer].[Country].[Canada],[Product].[Product Line].[Mountain]),
([Customer].[Country].[Canada],[Product].[Product Line].[Road])}
- This result clearly illustrates how the cross product generates every possible tuple combination, forming a comprehensive matrix view.
Comparison Operators: Evaluating Relationships
MDX natively supports a comprehensive suite of comparison operators: less than (<), less than or equal to (<=), greater than (>), greater than or equal to (>=), equal to (=), and not equal to (<>). These operators invariably take two MDX expressions as arguments. Upon evaluation, they return either TRUE or FALSE based on the outcome of comparing the respective values of each expression.
Example:
Code snippet
Count (Customer.[Country].members) > 3
In this example, Count is an MDX function utilized to enumerate the total number of members within the Country hierarchy of the Customer dimension. If the count of members exceeds three, the evaluation of this MDX expression will yield TRUE, otherwise FALSE. This demonstrates how comparison operators are used to establish conditions based on calculated or retrieved values.
Logical Operators: Building Complex Conditions
The logical operators integral to MDX encompass AND, OR, XOR, NOT, and IS. These operators are employed for performing logical conjunction, logical disjunction, logical exclusion, logical negation, and comparison, respectively. Each of these operators accepts two MDX expressions as arguments and, upon evaluation, returns either TRUE or FALSE based on the outcome of the specific logical operation. Logical operators are predominantly deployed within MDX expressions for defining robust cell and dimension security, allowing for granular control over data visibility and access permissions based on complex conditional logic.
Special MDX Operators: Shaping Tuples and Sets
Beyond the standard arithmetic and logical operators, MDX employs several special characters that play crucial roles in defining the structure of sets and tuples: curly braces ({}), commas (,), and colons (:).
- Curly Braces ({}): The curly braces are fundamental in MDX for enclosing a tuple or a collection of tuples to explicitly form an MDX set. When dealing with a set that contains only a single tuple, the curly braces become optional, as Analysis Services implicitly converts a single tuple into a set when the context necessitates it. However, for sets comprising more than one tuple, or for representing an empty set, the explicit use of curly braces is mandatory, ensuring clear structural definition.
- Commas (,): The comma character serves a dual purpose in MDX. Primarily, it is utilized to construct a tuple that incorporates more than one member (e.g., (Gender.Male, Year.2003)). This creates a specific «slice» of data on the cube, pinpointing a unique intersection of dimensional elements. Additionally, the comma character is strategically employed to delineate and separate multiple tuples when they are specified to define a comprehensive set. For instance, in the set {(Male,2003), (Male,2004), (Male,2005), (Female,2003), (Female,2004), (Female,2005)}, the comma character is used both to form individual tuples and to separate these tuples within the overarching set definition.
- Colons (:): The colon character is specifically deployed to delineate a range of members within a set. It is positioned between two non-consecutive members in a set to implicitly indicate the inclusion of all members situated between them, with the selection being based on the defined set ordering (which can be either key-based or name-based). This provides a concise way to select contiguous blocks of members without listing each one individually.
For example, consider an explicit set definition:
Code snippet
{[Customer].[Country].[Australia], [Customer].[Country].[Canada],
[Customer].[Country].[France], [Customer].[Country].[Germany],
[Customer].[Country].[United Kingdom], [Customer].[Country].[United States]}
The following MDX expression, utilizing the colon operator, achieves a subset of this range:
Code snippet
{[Customer].[Country].[Canada] : [Customer].[Country].[United Kingdom]}
This concise expression results in the following expanded set:
Code snippet
{[Customer].[Country].[Canada], [Customer].[Country].[France],
[Customer].[Country].[Germany], [Customer].[Country].[United Kingdom]}
This demonstrates how the colon operator efficiently defines contiguous ranges of members within a dimension, enhancing query conciseness and readability.
Extending Functionality: A Deep Dive into MDX Functions
MDX functions are powerful programmatic constructs that can be seamlessly incorporated into both MDX expressions and comprehensive MDX queries. Their utility is vast, encompassing diverse operations such as meticulously ordering tuples within a set, precisely enumerating the total number of members within a dimension, and performing intricate string manipulations to transform raw user input into corresponding MDX objects. These functions are the workhorses of complex MDX logic, enabling dynamic data processing and analytical transformations.
Categorization of MDX Functions
MDX functions can be invoked and utilized in several distinct syntactical manners, each suited for specific contexts:
Dot Notation Functions (Function.Property/Name)
Some MDX functions are accessed using a dot notation, often resembling property access in object-oriented programming. Example: Dimension.Name returns the name of the object being referenced, which could be a hierarchy, level, or member expression. This is reminiscent of the dot operator in languages like VB.NET, allowing direct retrieval of attributes.
Code snippet
WITH MEMBER measures.LocationName AS [Customer].[Country].CurrentMember.Name
SELECT measures.LocationName ON COLUMNS,
Customer.Country.members on ROWS
FROM [Adventure Works]
In this example, CurrentMember.Name retrieves the name of the current country member being iterated through, demonstrating how a property-like function can be used to extract metadata.
Simple Function Calls (FunctionName)
Some MDX functions are invoked directly by their name, without arguments or parentheses, particularly when retrieving contextual information. Example: Username is used to acquire the username of the currently logged-in user. It returns a string typically formatted as domain-name\user-name. This function is most frequently employed in dimension or cell security-related MDX expressions, enabling personalized data views based on user identity.
Code snippet
WITH MEMBER Measures.User AS USERNAME
SELECT Measures.User ON 0 FROM [Adventure Works]
This query dynamically creates a measure that displays the username of the person executing the query, illustrating the direct utility of this function for security and auditing purposes.
Functions Requiring Parentheses (FunctionName())
Certain MDX functions necessitate the inclusion of parentheses, even when they do not accept any arguments. Example: The function CalculationCurrentPass() requires parentheses but takes no arguments. It’s often used in advanced calculation scenarios to determine the current pass of a calculation.
Functions with Arguments (FunctionName(arguments))
The majority of MDX functions accept one or more arguments, which can be expressions or references to MDX objects. Example: OpeningPeriod( [Level_Expression [ , Member_Expression] ] ) is an MDX function that takes optional arguments. It can specify both a level_expression with a member_expression, or just the member_expression itself. This function is most commonly utilized with Time dimensions but is also compatible with other dimension types. It returns the first member at the specified level relative to the given member expression. For instance, the following expression would return the first day member of the April member within the default time dimension:
Code snippet
OpeningPeriod (Day, [April])
This illustrates how functions with arguments enable highly specific data retrieval based on contextual parameters.
Categories of MDX Functions: Specialized Operations
MDX functions are broadly categorized based on the types of objects they operate on or the results they return.
Set Functions: Manipulating Collections of Tuples
Set functions are inherently designed to operate on sets of tuples. They typically take one or more sets as arguments and, in most cases, return a resultant set. Some of the most widely used set functions include Crossjoin and Filter.
Crossjoin: This function returns all possible combinations of tuples from the sets specified as its arguments. If N sets are provided as arguments to the Crossjoin function, the result will be a new set combining all possible members within those sets onto a single axis. This is invaluable for generating comprehensive analytical views where all permutations of dimensional members are required.
Example:
Code snippet
Crossjoin( Set_Expression [ ,Set_Expression …] )
SELECT Measures.[Internet Sales Amount] ON COLUMNS,
CROSSJOIN( {Product.[Product Line].[Product Line].MEMBERS},
{[Customer].[Country].MEMBERS}) on ROWS
FROM [Adventure Works]
- This query meticulously produces the cross product of each member in the Product dimension with each member of the Customer dimension, projecting their combined influence onto the Sales Amount measure. The resulting output would list every product line combined with every customer country, showing the corresponding sales amount, thereby creating a detailed matrix of sales performance across these two dimensions.
Example Output Snippet (illustrative):- Sales Amount: Accessory All Customers $604,053.30
- Sales Amount: Accessory Australia $127,128.61
- Sales Amount: Accessory Canada $82,736.07
- …and so on, for all combinations.
Member Functions: Navigating Hierarchies
Member functions are specifically utilized for operations directly on individual members within a dimension’s hierarchy. These functions enable sophisticated navigation through hierarchical structures, allowing the retrieval of the current member, its ancestors, parent, children, siblings, or the next member in a sequence. All member functions, by definition, return a single member as their result. One of the most pervasively used member functions is ParallelPeriod. The ParallelPeriod function is instrumental in retrieving a specific member within a Time dimension, based on a given member and certain contextual conditions. This is exceptionally useful for time-based comparisons and trend analysis.
The function definition for ParallelPeriod is:
Code snippet
ParallelPeriod( [ Level_Expression [ ,Numeric_Expression [ , Member_Expression ] ] ] )
The ParallelPeriod function is frequently employed to compare measure values across various time periods, for instance, comparing current month sales to sales from the same month in the previous year, or quarter-over-quarter analysis.
Numeric Functions: Quantitative Operations
Numeric functions are indispensable when defining parameters for an MDX query or when constructing any calculated measure that involves quantitative computations.
The most commonly encountered numeric function is the straightforward Count, along with its closely related counterpart, DistinctCount.
- The Count function is specifically designed to enumerate the total number of items within a collection of a particular object, such as a Dimension, a Tuple, a Set, or a Level. It provides a simple tally of all elements.
- Conversely, the DistinctCount function takes a Set_Expression as its argument and returns a numerical value that precisely indicates the number of distinct items within that Set_Expression, not the cumulative total count of all items, thereby excluding duplicates.
Here are the function definitions for each:
Code snippet
Count ( Dimension | Tuples | Set| Level)
DistinctCount( Set_Expression )
Example:
Code snippet
WITH MEMBER Measures.CustomerCount AS DistinctCount(
Exists([Customer].[Customer].MEMBERS,[Product].[Product Line].Mountain,
“Internet Sales”))
SELECT Measures.CustomerCount ON COLUMNS
FROM [Adventure Works]
In this example, the DistinctCount function is utilized to enumerate the number of distinct members within the Customer dimension who have specifically purchased products falling under the Mountain product line. If a particular customer has engaged in multiple purchases from the specified product line, the DistinctCount function will judiciously count that customer only once, ensuring an accurate tally of unique individuals. The MDX function Exists is strategically employed here to filter the customer set, including only those customers who have exclusively purchased products from the Mountain product line via the Internet sales channel, demonstrating a powerful combination of functions for targeted analysis.
Dimension Functions, Level Functions, and Hierarchy Functions: Structural Navigation
Functions within these distinct groups are primarily employed for navigation and manipulation within the hierarchical structures of a cube. They enable users to traverse dimensions, access properties of levels, and interact with hierarchies. Here is an illustrative example of such a function, the Level function, derived from the Level group:
Code snippet
SELECT [Date].[Calendar].[Calendar Quarter].[Q1 CY 2004].LEVEL ON COLUMNS
FROM [Adventure Works]
This query, when executed, might initially appear to select a single quarter. However, [Date].[Calendar].[Calendar Quarter].[Q1 CY 2004].LEVEL evaluates to the level object itself, which is [Date].[Calendar Year].[Calendar Semester].[Calendar Quarter]. Consequently, the query results in a list of all quarters from all calendar years within the cube, rather than just the specified quarter. This illustrates how these functions enable programmatic interaction with the cube’s metadata and structure, allowing for dynamic selection based on hierarchical properties.
Conclusion
In the evolving landscape of data analysis, MDX stands as a robust and indispensable language for interacting with multidimensional databases. Its distinct syntax, nuanced clauses, and powerful array of functions and operators enable analysts and developers to transcend the limitations of traditional two-dimensional data views. From defining foundational cubes and specifying intricate sets and tuples to orchestrating complex queries with axis and slicer dimensions, MDX provides unparalleled control over data extraction. The WITH clause, with its capacity for defining calculated members and named sets, further augments its analytical prowess, allowing for dynamic, on-the-fly computations crucial for deep business insights.
Furthermore, the diverse categories of MDX functions, spanning set operations, member navigation, numeric computations, and hierarchical manipulations, empower users to perform highly specific and complex data transformations. Understanding MDX is not merely about learning a syntax; it’s about embracing a new paradigm of data interaction that reflects the multidimensional reality of modern business. As organizations increasingly rely on OLAP for strategic decision-making, proficiency in MDX remains a critical skill, allowing for the precise, efficient, and insightful interrogation of vast analytical datasets, ultimately unlocking their full value.