This document provides an introduction to MDX (Multidimensional Expressions), which is the query language used for multidimensional databases. It explains that MDX allows for very concise queries with relatively complex results, such as year-to-date, rolling averages, and net performance. The document also discusses key MDX concepts like cubes, measures, tuples, and sets. It provides an example MDX query and demonstrates how to use functions like WITH, MEMBER, TOPCOUNT, and WHERE to retrieve specific data from an Analysis Services cube.

1. Drive your life.
Einführung in MDX
Matthias Gessenay
Matthias.gessenay@corporatesoftware.ch

2. Was ist MDX
 «SQL» für multidimensionale Datenbanken
 Sehr kurze Abfragen mit relativ komplexen Ergebnissen
 YTD
 Rolling Average
 Net Performance
 Beispiel:
SELECT
{
([Measures].[Reseller Sales Amount]),
([Measures].[Reseller Transaction Count]),
([Measures].[Reseller Order Count])
} ON COLUMNS,
TopCount(
{[Product].[Subcategory].[Subcategory].Members},
5,
([Measures].[Reseller Sales Amount])
) +
{([Product].[Subcategory].[All Products])} ON ROWS
FROM [Step-by-Step]
;
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3. Ergebnis
/*
Reseller Sales Amount Reseller Transaction Count Reseller Order Count
Road Bikes $29,358,206.96 12,850 1,460
Mountain Bikes $26,492,684.38 7,487 1,215
Touring Bikes $10,451,490.22 4,463 478
Mountain Frames $4,713,672.15 4,476 860
Road Frames $3,849,853.34 4,708 1,013
All Products $80,450,596.98 60,855 3,796
 Liegt immer als Pivot vor
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4. CUBE: EINFÜHRUNG
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5. Der Microsoft BI Solution Stack
Data Infrastructure & BI
Platform
Analysis Services
Reporting Services
Master Data Services
Integration Services
Data Mining
Data Warehousing

6. Business Productivity
Infrastructure
Dashboards &
Scorecards
Excel Services
Web based forms &
workflow
Collaboration
Search
Content Management
LOB data integration
PowerPivot for
SharePoint

7. Delivered through a
Familiar Interface
Self-Service access &
insight
Data exploration & analysis
Predictive analysis
Data visualization
Contextual visualization
PowerPivot for Excel 2010

8. OLTP vs. Cube
 OLTP
 Optimiert für Write
 Normalisierung
 NICHT optimiert für Reporting
 Cube
 De-Normalisiert
 Optimiert für Reporting
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9. SQL Day 2012 9

10. Tuple
 SQL
 Wenn ich nichts auswähle, kommen alle Datensätze (Rows)
 MDX
 Wenn ich nichts auswähle, kommt das default Measure als
Summe
 Die Schnittmenge von den Cube-Kanten nennt man Tuple
 Der Cube ist gefüllt mit Measures (Zahlen)
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11. SELECT
{
([Measures].[Reseller Sales Amount]),
([Measures].[Reseller Transaction Count]),
([Measures].[Reseller Order Count])
} ON COLUMNS,
TopCount(
{[Product].[Subcategory].[Subcategory].Members},
5,
([Measures].[Reseller Sales Amount])
) +
{([Product].[Subcategory].[All Products])} ON
ROWS
FROM [Step-by-Step]
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12. SELECT
{
([Measures].[Reseller Sales Amount]),
([Measures].[Reseller Transaction Count]),
([Measures].[Reseller Order Count])
} ON COLUMNS,
TopCount(
{[Product].[Subcategory].[Subcategory].Members},
5,
([Measures].[Reseller Sales Amount])
) +
{([Product].[Subcategory].[All Products])} ON
ROWS
FROM [Step-by-Step]
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13. DEMO: CUBE
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14. MDX SCHRITT FÜR SCHRITT
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15. Wir möchten gerne dies:
/*
EnglishProductName TotalSalesAmount
Mountain-200 Black, 38 1327957.4077
Mountain-200 Black, 42 1139429.4872
Mountain-200 Silver, 38 1136622.4928
Mountain-200 Black, 46 1011074.3685
Mountain-200 Black, 38 NULL
Mountain-200 Silver, 42 1011486.1762
Touring-1000 Blue, 60 835290.1559
Road-350-W Yellow, 48 897217.9635
Mountain-200 Silver, 46 1029170.7639
Road-350-W Yellow, 40 840970.6467
*/
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16. Variante SQL:
USE MdxStepByStep ON m.ProductKey=n.ProductKey
LEFT OUTER JOIN ( --PRODUCT SALES IN 2004
SELECT SELECT
m.EnglishProductName, o.TotalSalesAmount a.ProductKey, SUM(a.SalesAmount) AS TotalSalesAmount
FROM dbo.DimProduct m FROM (
INNER JOIN ( -- TOP 10 PRODUCTS OF 2003 SELECT
SELECT TOP 10 x.productkey, x.salesamount
a.ProductKey, SUM(a.SalesAmount) AS TotalSalesAmount FROM dbo.FactInternetSales x
FROM ( INNER JOIN dbo.DimDate y
SELECT ON x.OrderDateKey=y.DateKey
x.productkey, x.salesamount WHERE y.CalendarYear=2004
FROM dbo.FactInternetSales x UNION ALL
INNER JOIN dbo.DimDate y SELECT
ON x.OrderDateKey=y.DateKey x.productkey, x.salesamount
WHERE y.CalendarYear=2003 FROM dbo.FactResellerSales x
UNION ALL INNER JOIN dbo.DimDate y
SELECT ON x.OrderDateKey=y.DateKey
x.productkey, x.salesamount WHERE y.CalendarYear=2004
FROM dbo.FactResellerSales x ) a
INNER JOIN dbo.DimDate y GROUP BY a.ProductKey
ON x.OrderDateKey=y.DateKey ) o
WHERE y.CalendarYear=2003 ON m.ProductKey=o.productkey
) a ORDER BY n.TotalSalesAmount DESC
GROUP BY a.ProductKey
ORDER BY TotalSalesAmount DESC
) n
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17. Variante MDX:
WITH
MEMBER [Measures].[Total Sales Amount] AS
([Measures].[Internet Sales Amount]) + ([Measures].[Reseller Sales
Amount])
SET [Top 10 Products of 2003] AS
TOPCOUNT(
{[Product].[Product].[Product].Members},
10,
([Measures].[Total Sales Amount], [Date].[Calendar Year].[CY
2003])
)
SELECT
{([Measures].[Total Sales Amount])} ON COLUMNS,
{[Top 10 Products of 2003]} ON ROWS
FROM [Step-by-Step]
WHERE ([Date].[Calendar Year].[CY 2004])
;
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18. DEMO
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19. Funktionen & With
1. WITH
2. MEMBER [Measures].[Total Sales Amount] AS
3. [Measures].[Internet Sales Amount]+[Measures].[Reseller Sales
Amount]
4. SET [Top 10 Products of 2003] AS
5. TOPCOUNT( [Product].[Product].[Product].Members, 10,
([Measures].[Total Sales Amount], [Date].[Calendar Year].[CY
2003]))
6. SELECT
7. {([Measures].[Total Sales Amount])} ON COLUMNS,
8. {[Top 10 Products of 2003]} ON ROWS
9. FROM [Step-by-Step]
10.WHERE [Date].[Calendar Year].&[2004]
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20. Where
 Schränkt den Cube ein (Slicing)
 Verändert damit die Resultate
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21. WHERE: DEMO
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22. MDX Anwendungsbereiche
 Reporting Services
 Excel
 PerformancePoint
 Eigene Apps ...
 SSIS
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23. FRAGEN?
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