learn to write mdx queries in sql server

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Learn to Write Custom MDX Query First Time

• Download MDX queries - 3.1 KB

Introduction

In this article, we will go through some basic concepts and terminologies used while writing MDX Queries on your OLAP Cube, We will also look into Why-What and How of MDX Query.

While we Google, we can find some good articles on this topic, but I did not find an article with all stuff which I am looking for in one when I was searching as a beginner in this direction. So I have taken this small step to write an article and share with you all, so you can Learn Custom MDX with ease and enjoy.

Multi Dimensional Expression (MDX)

MDX Query Language is used to retrieve information stored in OLAP Cube created in various technologies like Microsoft SQL Server Analysis Services(SSAS), Oracle, Tera data, etc. Key difference between MDX and T-SQL is MDX Query build Multidimensional View of the data, where T-SQL builds Relational View. SQL Query designed to handle only two dimension while processing tabular data. While MDX Can process more dimensions in Query.

MDX is also used to write expressions for custom calculation in Power Pivot and for creation of Calculated member in OLAP Cube.

What do you mean by dimensions in Query? In general, we can say entities with related member details using which you have planned to study & analyze Data in OLAP Cube.

Introduction to Basic Concepts Used within MDX Query

We need to have a clear idea in our mind about the various concepts and terminologies used while working with MDX Query. Initially, I found it very confusing to understand all these when I was new, but I don't want you to be stuck on this all, so let us begin.

OLAP Cube is the basic unit of storage for Multidimensional data, on which we can do analysis on stored data and study the various patterns. You can take further idea on OLAP cube creation using this article Create First OLAP Cube in SSAS .

The primary functions of dimensions are to provide Filtering, Grouping and Labeling on your data. Dimension tables contain textual descriptions about the subjects of the business. Dimensions in general we can say are the Master entities with related member attributes using which we can study data stored in OLAP Cube Quickly and effectively.

Measure & Measure Groups

Metrics value stored in your Fact Tables is called Measure. Measures are used to analyze performance of the Business. Measure usually contains numeric data, which can be aggregated against usage of associated dimensions. Measure Group holds collection of related Measures.

To learn about Data Warehouse quickly refer to the article Create First Data Warehouse .

Take a look at the image given below which represents terminologies discussed above.

OLAP Database is container of Cubes. It is important to identify Cube Name before we start writing our query. Then after we need to select Measure from appropriate Measure Group and use related dimensions.

Introduction to Level, Member, Hierarchy

Let us take a look at brief descriptions of frequent terms used in MDX query.

Generally Attributes under Dimension are considered as levels, they are also called as Attribute Hierarchy .

Let's take an example of Date Dimension in this we have various levels like Quarter of the Year, Semester of the Year, Week of the Year, Calendar Year, etc.

Key component of the MDX query is member. Each Level contains one or more members.

e.g. Calendar Quarter of Year contains various members like CY Q1, CY Q2, CY Q3, CY Q4 .

User Defined Hierarchy

We usually create this type of hierarchy while designing OLAP Cube as per their relations. You can refer Date Hierarchy shown in the figure shown below.

This hierarchy also contains various levels, by default Level 0 is reserved for [ALL] .

Please refer to my previous articles if you are more interested to know about Data Warehouse and OLAP Cube Creation using Microsoft Business Intelligence.

Here we are going to work with Microsoft SQL Server 2008 R2 (Standard, Enterprise edition) .

Using the Code

Let us make our test environment ready.

1. Here you need to download Adventure Works Data Warehouse from CodePlex Site.

• Download Adventure Works Data Warehouse 2008 R2

2. Also download Analysis Services Solution created using this AdventureWorksDW2008 R2 from CodePlex Site.

• Download Adventure Works 2008 R2 Analysis Services Project

3.Check in Services.msc that your SQL Server Analysis services were up and running.

4. Configure Connection string in above SSAS Solution and Deploy your Cube.

5. Now Open Microsoft SQL Server Management Studio ( SSMS ) and connect Analysis Services using Windows Authentication .

Select Server type : Analysis Services-->Specify your SQL Server name : e.g. mubin-pc\fairy or localhost -->Click: Connect

7. Open New MDX Query Editor Window

Right Click on Database Name ( Adventure Works DW 2008 R2 )--> Select New Query --> Click MDX

Introduction to Axis in MDX Query

MDX queries can have 0, 1, 2 or up to 129 query axes in the SELECT statement. Each axis behaves in exactly the same way, unlike SQL where there are significant differences between how the rows and the columns of a query behave.

Refer to the following table for Axis Numbers reserved and Alias given to them:

Using SQL Server Management Studio (SSMS), we can only browse values on two axis, Columns (Axis 0 ) and Rows (Axis 1 ).

Getting Started With MDX

1. start with simple mdx query, select from [your cube name] ;.

Which will give you aggregated result as shown in result pane, MDX is not Case Sensitive except member keys defined within dimension. This query will use default member defined in all the dimensions and use default measure defined by OLAP cube designer.

You can do drag and drop of cube name, dimension members from left pane to query window instead of typing. This query is also known as no axis query.

2. Dropping Dimensions on Axis

If we will not specify Axis for dimensions and measures, it may lead us to wrong result while design change take place.

Retrieve all customer names on Columns from Adventure Works Cube.

Select Dimension.Member on Column From [OLAPCubeName ] or Select Dimension.Member on 0 From [OLAPCubeName ]

As you can notice one thing here if your dimension is not associated with Measure Group, you can have same values in each result cell against every customer.

3. Using Both the Axis (Rows & Columns)

You can select Dimension or Measure on any Axis.

Retrieve Internet Sales Amount As Per Customer. In other words, we can say show the Detail of amount spent by customers during purchase from Internet.

Select [Measure] on Columns, [Dimension].[Members] on Rows From [Cube Name] ;

Select [measure] on rows, [dimension].[members] on columns from [cube name] ;.

Here, you can see Measure Value (Internet Sales Amount) is properly getting divided as per the customer.

4. Introduction to .members, and .children in MDX Query

If you will use this with hierarchy level, then it will retrieve all the values below it and also bring agreegation of that in the form of [ALL].

Select [Dimension].[Hierarchy].members on Columns from CubeName or Select [Dimension].[Hierarchy].[Level].members on Columns from CubeName

When we want to retrieve all members values under particular level of a dimension at that time we use .children ,This will exclude aggregation values [ALL] in your result set.

Select [Dimension].[Hierarchy].[Level].children on Columns from CubeName

5. Introduction to Tuple and Set

When we need to place more than one members of a dimension or hierarchy of that dimension on a axis at that time tuple comes into the picture, tuple is enclosed within curly bracket { }, for single tuple bracket is optional.

We can say Tuple is used to identify particular location in the cube using your dimension members. Tuple will define slice of your cube. Tuple can contain one or more members, but it cannot have members from the same dimension.

This is example of tuples from same date dimension members. Combination of more than one tuple will make a set.

View Internet Sales amount detail between year 2005 to 2007 using tuples.

A set is an ordered collection of zero, one or more tuples. A set is most commonly used to define axis and slicer dimensions in an MDX query.

Combination of tuple or tuples will give you set , When You want to include range at that time you can use : instead of separating tuple members by comma if they are belonging to same dimension member.

{[Date].[CY 2008] : [Date].[CY 2005]} or {[Date].[CY 2005], [Date].[CY 2006] , [Date].[CY 2007]} or ( [Date].[Calendar Year].members , [Product].[Product].members )

View Internet Sales amount detail between year 2005 to 2008

To use combination of tuples from various dimensions, we have to use Cross Join that we will learn soon.

6. Using CROSS JOIN

Cross Join Function returns cross product of one or more sets.

Whenever we need to combine more than one member from same or different dimension at that time we can use cross join. * sign can be use to implement cross join between dimension members.

We can also use Cross Join Function to implement cross join between different dimension members, but result will stay same if you use * or CrossJoin Function.

7. Using Non Empty or NonEmpty

To element Null values from the result set, we can use NonEmpty() or Non Empty. Right now, I am not discussing difference between Non Empty and NonEmpty function.

NonEmpty function evaluated first so it will remove rows if there was no data in first measure. Let us take a look at the below example how we can remove null values from result set.

Non Empty or NonEmpty() function can be used on any Axis.

Let us take a look on Cross join applied in below example, here you can see how we are retrieving multiple measures by placing them between curly bracket{ } .

You can see null values in the result set while using following MDX Query.

Now to eliminate these Null Values from Result Set using Non Empty.

8. Apply Slicing using Where Clause

To Slice Data from cube we can use Where clause, it is similar to “ where ” clause we have in T-SQL.

I want to see detail of Internet Sales Amount for each product in the Year 2007.

If I want to see detail of Internet Sales Amount for each product in the Year 2007 and 2009.

9. Apply Filtering on data using Filter function

Filter function will also be used to apply filtering on members available in specified set as per the specified Boolean condition.

Filter( <Set>, Boolean Condition)

Example: If I want to retrieve only those products whose names begin with “A” and Internet sales amount <5000.

10 . Apply Sorting on your Data using Order Function

To sort your data you can use order function, using this function you can override default order specified in the cube design.

Order(<set>, Context, Asc | Desc|Bsc|Bdesc)

Retrieve all the products in descending order of their Internet sales amount of year 2007

Hope you have enjoyed this article. In this beginner article, I have tried to give Initial start up to technical newbies working in Microsoft BI and want to initiate in Learning Custom MDX.

We will learn about different MDX Functions and their usage in my next article on Advance Custom MDX.

I have included many sample queries with appropriate comments , Please download for further practise.

If you find this article helpful, then please do not forget to vote for me.

Source code may contain reference to the following where appropriate:

• Microsoft Technet
• M icrosoft MSDN

By uploading my code to codeproject.com, I assume I inherit all open source terms of use, licenses and those specified by codeproject.com. However if you use this code for any purpose, I would really like to hear about it. It is my belief that by referencing the credited people, I demonstrate the ability to effectively read and re-use source code, rather than re-invent the wheel. I expect you would do the same.

I always welcome suggestions from readers and experts for improvements.

Enjoy Learning MDX.

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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Foundations of SQL Server 2008 R2 Business Intelligence pp 347–367 Cite as

Introduction to MDX

• Guy Fouché &
• Lynn Langit  

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In this chapter, you’ll work with Multidimensional Expressions (MDX), which is the native query language for SQL Server Analysis Services (SSAS). MDX is a SQL-like language used to query SSAS cubes.

• Calculated Measure
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MDX (MultiDimensional Expressions) in 148 minutes

arthur , 2019-05-07

If you want to understand the fundamentals of MDX in less than 3 hours - this article can be helpful. I came to the conclusion that the best way of learning are video tutorials. 143 minutes of watching and 5 minutes of reading. Let's get started.

Some information about MDX

MDX (MultiDimensional Expressions) is a query language for OLAP databases and it's designed to provide a effective way of querying multidimensional data. MDX is used to interact with data in Microsoft SQL Server Analysis Services (SSAS) cubes. Business’s data analysis provide valuable insight into its operations and new opportunities can be identified.

MDX commands are designed specifically to retrieve data as multidimensional data structures and can process one, two, three, or more dimensions in queries. MDX expressions are composed of values, statements, functions, identifiers and operators that SSAS can evaluate to retrieve scalar value (number, string) or an object (member, set).

Level 1: Creating database and OLAP cube

To work with MDX queries, we need to create a SQL Server database and an OLAP cube in SQL Server Analysis Services. You can watch this step here:

or if you prefer the short version:

Level 2: MDX Introduction

We already have an OLAP cube, so let's start creating the first queries in the MDX language.

Level 3: Working with Sets

MDX is created to work with data sets. Below are some practical examples:

Level 4: Expressions

Let's dive into some expressions:

Level 5: Functions

A bit of knowledge about functions will also be useful:

Level 6: Aggregations

Working with aggregations, hierarchies and time is essential when it comes to MDX and OLAP analysis:

Level 7: Hierarchies

If you survived the aggregation lesson, it's time for the hierarchies

Level 8: Time

The last step of this course is a few minutes about time.

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• 11 videos | 1h 9m 47s
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WHAT YOU WILL LEARN

In this course.

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• Playable 2.  The MDX Query Structure 5m 49s Upon completion of this video, you will be able to describe the basic structure of an MDX query. FREE ACCESS
• Locked 3.  Identifying Common MDX Functions 4m 38s Upon completion of this video, you will be able to describe the common functions of MDX. FREE ACCESS
• Locked 4.  Using MDX Set Functions (Part 1) 9m 29s In this video, find out how to use MDX set functions. FREE ACCESS
• Locked 5.  Using MDX Set Functions (Part 2) 5m 50s In this video, find out how to use MDX set functions. FREE ACCESS
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Microsoft Copilot for Azure enables natural language queries for Azure Cosmos DB data

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We are thrilled to announce Microsoft Copilot for Azure in Azure Cosmos DB is now in public preview! Microsoft Copilot for Azure  is an AI companion that simplifies how you design, operate, optimize, and troubleshoot from cloud to edge. Copilot’s integration into Azure Cosmos DB is an amazing AI-powered capability that can create Azure Cosmos DB NoSQL queries based on your natural language questions about your data!

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As preview capability, Microsoft Copilot for Azure in Azure Cosmos DB needs to be enabled by your Azure subscription’s administrator in the Subscription’s preview features control as shown below. Learn how to setup preview features in an Azure subscription here.

Once your subscription has Copilot to write NoSQL queries faster and easier, you can follow these steps:

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2. Navigate to the features blade under Settings and enable Copilot.

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4. Select the database and collection you’d like to query:

• You can select any of your databases and collections that you have access to in the MY DATA section of the left-hand navigation pane. Once the collection is chosen, you can right-click select New SQL Query or select New SQL Query in the toolbar.

b. Or you can choose the sample database CopilotSampleDb from the SAMPLE DATA to use a pre-populated collection. This can also be accessed quickly by selecting the “Query faster with Copilot” card.

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6. Type a clear and concise question about the data you are looking for in the prompt box. Use keywords and context that are relevant to the Azure Cosmos DB document structure. Specify properties and any filtering criteria as explicitly as possible. Avoid ambiguous or overly complex language in your prompts.

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Create a DMX Query in SQL Server Management Studio

• 2 contributors

Data mining was deprecated in SQL Server 2017 Analysis Services and now discontinued in SQL Server 2022 Analysis Services. Documentation is not updated for deprecated and discontinued features. To learn more, see Analysis Services backward compatibility .

SQL Server provides a set of features to help you create prediction queries, content queries, and data definition queries against mining models and mining structures.

The graphical Prediction Query Builder is available in both SQL Server Data Tools and SQL Server Management Studio, to simplify the process of writing prediction queries and mapping data sets to a model.

The query templates provided in the Template Explorer jump-start the creation of many kinds of DMX queries, including many types of prediction queries. Templates are provided for content queries, queries used nested data sets, queries that return cases from the mining structure, and even data definition queries.

The Metadata Explorer in the MDX and DMX query panes provides a list of available models and structures that you can drag and drop into the query builder, as well as a list of DMX functions. This feature makes it easy to get object names right, without typing.

This topic describes how to build a DMX query by using the Metadata Explorer and the DMX query editor.

DMX Query Templates

Templates for creating basic DMX queries are available in Template Explorer. The DMX folder contains data mining templates, which are divided into these categories:

Model Content

Model Management

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You can also create custom templates, for queries or commands that you run frequently.

XMLA Query Templates

SQL Server Analysis Services also provides templates for XMLA queries.

There is some overlap between the types of queries that you can perform by using XMLA and DMX. For example, you can create some model content queries by using either DMX or the data mining schema rowsets, but the schema rowsets sometimes contain information that is not exposed in DMX content queries.

There are also some key differences in the way that operations are handled in DMX and in XMLA. For example, you can use XMLA to perform administrative operations such as backup of an entire SQL Server Analysis Services database, but if you want to back up a single mining model, DMX provides a simple command, EXPORT (DMX) , that is better suited to that purpose.

Build and Run a DMX Query

Open a new dmx query window.

Click New Query in Management Studio, and then select New Analysis Server DMX Query .

When the Connect to Server dialog box appears, select the instance of SQL Server Analysis Services that contains the mining models you want to work with.

Open Template Explorer

In SQL Server Management Studio, on the View menu, select Template Explorer .

Click Analysis Server to see a tree view of the templates that apply to SQL Server Analysis Services.

Apply a template to build a query

Right-click the appropriate query type and select Open .

Or, drag the template into the query editor.

You can also fill in the parameters for the query by using the option, Specify Values for Parameters , from the Query menu.

For examples of how to create specific types of queries from templates, see the following topics:

Create a Singleton Prediction Query from a Template

Create a Content Query on a Mining Model

Data Mining Query Tools Data Mining Extensions (DMX) Reference

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Learn to Write MDX Queries in Analysis Services (SSAS)

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MDX (MultiDimensional eXpressions)

–> To work with Normal two – dimensional applications, two-dimensional programming languages are enough ( C, C ++, .NET …etc) –> To work with Two – Dimensional databases, two- dimensional Query language SQL is wrought (Oracle SQL, T – SQL, Teradata SQL – etc.) –> To work with multidimensional allocations and multidimensional databases the above specified or not enough so we go for a separate expression and Query language ‘ MDX ‘.

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Important terminology in MDX : a) Member: Dimension attribute is called member –> Syntax:- [Dimension table name] . [Attribute Name]                 Ex:         [ Product ] . [ Product Name ] [ Location ] . [ Location name ] Measure: Fact attribute is called Measure. Syntax: [ Measures ] . [ Measures Name ] Ex:         [ Measures ] . [ Actual Cost ] [ Measures ] . [ Estimated Cost ] TUPLE: Collection of Measures or Members is called Tuple. a. Starts with ( b. Ends with   ) Ex:      ( [ Measures ] . [ Actual Cost ] , [ Measures ] . [ Estimated Cost ] ) SET:     Group of Tuples are called as SET. a. Stores with { b. Ends with    } Ex:          { ( [ Measures ] . [ Actual Cost ] , [ Measures ] . [ Estimated Cost ] , ( [ Measures ] . [ Actual Cost ] , [ Measures ] . [ Estimated Cost ] ) }

1. Generally, MDX queries we write in analysis services cube database. 2. For retrieving data from cube database we use Select statements. Syntax: Select { Measures / Members } ON columns , { Measures / Members } ON rows From < cube name > where < condition > ; Some Functions In MDX And There Meanings: There  are two types functions 1) Some Functions takes Parameters Ex:  TOP COUNT, BOTTOM COUNT, ISEMPTY etc (Top count) ,  (Bottom Count) 2) Functions without Parameters Ex: MEMBERS,ALLMEMBERS,CHILDREN,PREVMEMBER,CURRENTMEMBER Etc... .MEMBERS: It displays the child members without including the total. .ALLMEMBERS: Display All members and their total. .PREVMEMBER: Display previous members to the current member. .CURRENTMEMBER: Display the current cell member value

3) Functions with Parameters ISEMPTY: It verifies whether the member is empty or not. Syntax: Is Empty (set) TOPCOUNT: Display Top values. Syntax: Top Count (set, value) BOTTOMCOUNT: Display bottom count of values Syntax: Bottom Count (set, value) FILTER : It filters the given set based on the condition Syntax: Filter (set, condition) ORDER : It displays the set by keeping a descending & ascending on the given column. DISTINCT ( ) : It displays the set values DISTINCT ( {set} ) 4) Working with Hierarchies We refer hierarchies member values in 2 ways. a. [Dimension] . [Hierarchy] . [Members] b. [Dimension] . [Hierarchy] . [Level] . [Members] NOTE: If we don’t specify the level, it displays all member values. CROSS JOIN: CROSS JOIN ( {set} , {set} ) (OR) {set} * {set} Important MDX queries: Navigation: SSMS –> Analysis Services –> TEXTILES_CUBE –> RC –> New Query –> MDX

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MDX Queries

1) DISPLAY FIRST MEASURE SUM Syntax: Select from [TEXTFILES_CUBE]  cube name 2) DISPLAY NO. OF ROWS IN THE CUBE Syntax: Select [Measures] . [TEXT FACT COUNT] on columns from [TEXTFILES_CUBE] 3) DISPLAY ALL BRANCHES ACTUAL COST Syntax: Select [Measures] . [ActualCost] on columns , [Product] . [Brand] on rows from [DSV_textfiles_cube] Actual cost    9800. 4) DISPALY BRANCHES AND THEIR ACTUAL COST Syntax: Select [Measures] . [ActualCost] on columns , [Product] . [Brand] . children on rows from [TextFile_cube] 5) DISPALY ALL BRANCHE ACTUAL COST AND SUM OF ALL ACTUAL COST Syntax: Select [Measures] . [ActualCost] on columns , [Product] . [Brand] . ALLMEMBERS on rows from [TEXTFILES_CUBE] 6) DISPLAYING EVERY RAW MATERIAL AND LOCATION, THEIR ACTUAL COST Syntax: Select ([Measures] . [ActualCost] , [Measures] . [Estimated cost]) on columns , ([RawMaterial] . [Rawmaterial ID] . children [Location] . [Loc Name] . children) on Rows from [TEXTFILES_CUBE] 7) DISPLAY THE FIRST RAW MATERIAL ACTUAL COST: Syntax: Select [Measures] . [ActualCost] on columns . [RawMaterial] . [Rawmaterial ID] first child on rows from [TEXTFILES_CUBE] 8) DISPLAYING TOP TWO VALUES OF THE LOCATION Syntax: Select [Measures] . [ActualCost] on columns , TopCount ([Location] . [ LocName] . children,2 ) on rows from [TEXTFILES_CUBE] 9) DISPLAYING BOTTOM TWO LOCATION VALUES Syntax: Select [Measures] . [ActualCost] on columns , Bottom Count ([Location] . [LocName] . children, 2 ) on rows from [TEXTFILES_CUBE] 10) DISPLAY THE LOCATION TOOLS ACTUAL COST is > 1000 Syntax: Select [Measures] . [ActualCost] on columns , filter ([Location] . [ LocName] . children,    [Measures] . [ActualCost] > 1000) on rows from [TEXTFILES_CUBE] 11) DISPLAY THE DATA IN ACTUAL COST SORTED ORDER IN ASC Syntax: Select [Measures] . [ActualCost] on columns , order ([Location] . [ LocName] . children,    [Measures] . [ActualCost] , ASC ] on rows  12) DISPLAY THE CRO PRODUCT OF LOCATION PRODUCT AS WELL AS THEIR ACTUAL, ESTIMATED COST. Syntax: Select {[Measures] . [ActualCost] , [Measures] . [Estimated cost]} on columns, cross join ([Location] . [ LocName] . children , [Product] . [Product Name] . children) on rows from [TEXTFILES_CUBE]                                 OR Select {[Measures] . [ActualCost] , [Measures] . [Estimated cost]} on columns, cross join ([Location] . [ LocName] . children) * ([EProduct] . [Product Name] . children) on rows from [TEXTFILES_CUBE] 13) DISPLAY DISTINCT FROM DISTINCTWISE VALUES AND LACATIONWISE Syntax: Select {[Measures] . [ActualCost] , [Measures] . [Estimated cost]} on columns, DISTINCT ([Location] . [ LocName] . children , [Product] . [Product Name] . children) on rows from [TEXTFILES_CUBE] 14) DISPLAY 2009 YEAR ACTUAL COST SUM Syntax: Select [Measures] . [ActualCost] on columns from [TextFile_Cube] where [Time] .[Year] . & [2009] OR Select [Measures] . [ActualCost] on columns , [Location] . [ LocName] . children on rows from [DSV_TextFiles_cube] where {[Time] . [Year] . & [2009]}

Conditional Expressions

IF: IF ( , success stmt , failed stmt ) Eg: IF ([Measure] .[ActualCost] – [Measures] . [Estimated cost] > 0,1,0) CASE : Evaluates against multiple conditions CASE When < condition1 > then < statement1 > When < condition2 > then < statement2 > When < condition3 > then < statement3 > When < condition4 > then < statement4 > ELSE < statement 5 > END Ex: Case when [Measure] .[ActualCost] – [Measures] . [Estimated cost] > 0 Then -1 / where [Measure] .[ActualCost] – [Measures] . [Estimated cost] < 0 Then 1 / When [Measure] .[ActualCost] – [Measures] . [Estimated cost] = 0 Then 0 END                

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