Despite widespread adoption of OLAP technologies, the MDX query language remains a bit of an enigma. It's not until a very simple but seldom explored concept is understood that the power and elegance of the language is revealed. Join Bryan Smith, co-author of Microsoft SQL Server 2008 MDX Step by Step, in exploring this central concept, providing a foundation for your success with the MDX language.

1. Unlocking the
Mystery of MDX
Bryan Smith
brysmi@microsoft.com

3. Objectives
Obj ti
Explain h d behind
E l i the modell b hi d MDX
Explore lessons learned

4. The i
Th views and opinions expressed in this ....
d ii d i thi

5. (3)
x
-4 -3 -2 -1 0 1 2 3 4

6. y
4
(3, 2)
3
2
1
x
-4 -3 -2 -1 1 2 3 4
-2
-3
-4

7. y
4
(3, 2, 4)
3
2
1
x
-4 -3 -2 -1 1 2 3 4
-2
z
-3
-4

8. one-dimensional (x) single
two-dimensional (x, y) double
three-dimensionall
h di i (x, )
( y, z) triple
il
(a1, a2, a3, a4)
four-dimensional (x, y, z, ?) quadruple

9. four-dimensional
f di i l (a
( 1 , a2 , a3 , a4 ) quadruple
d l
five-dimensional (a1, a2, a3, a4, a5) quintuple
six-dimensional (a1, a2, a3, a4, a5, a6) sextuple
seven-dimensional (a1, a2, a3, a4, a5, a6, a7) septuple
eight dimensional
eight-dimensional (a1, a2, a3, a4, a5, a6, a7, a8) octuple

10. four-dimensional
four dimensional (a1, a2, a3, a4) quadruple
five-dimensional (a1, a2, a3, a4, a5) quintuple
six-dimensional (a1, a2, a3, a4, a5, a6) sextuple
seven-dimensional (a1, a2, a3, a4, a5, a6, a7) septuple
eight dimensional
eight-dimensional (a1, a2, a3, a4, a5, a6, a7, a8) octuple
n-dimensional (a1, a2, … an) tuple

11. My Simple Cube
Measure Group
Σ Measure
Dimension
Attribute

12. My Simple Cube
Reseller Sales Internet Sales
6 Σ Reseller Sales Amount Σ Internet Sales Amount
Date Product Geography
1 Fiscal Year 3 Category 5 Country
2 Calendar Year 4 Subcategory

13. Product
P d t
Category
All Products
Accessories Bikes Clothing Components

14. Components
C
([Bikes])
Clo
othing
Bike
es
Accessories
A
Alll Products
Category
[Product].[Category]
P d t
Product

15. ( (
a1,
[CY 2002], [Date].[Calendar Year].[CY 2002],
[All Periods],
a2, [Date].[Fiscal Year].[All Periods],
[Bikes],
a3, [Product].[Category].[Bikes],
[All Products],
a4, [Product].[Subcategory].[All Products],
[All Geographies],
a5, [Geography].[Country].[All Geographies],
[Reseller Sales Amount]
a6 [Measures].[Reseller Sales Amount]
) )
(
[Geography].[Country].[All Geographies],
[Product].[Category].[Bikes],
[Measures].[Reseller Sales Amount],
[ ][
[Date].[Fiscal Year].[All Periods],
][ ]
[Product].[Subcategory].[All Products],
[Date].[Calendar Year].[CY 2002]
)

16. (
( [Date].[Calendar Year].[CY 2002],
[Date].[Calendar Y ] [CY
[D ] [C l d Year].[CY 2002], ] [ ][
[Date].[Fiscal Year].[All Periods],
][ ],
[Product].[Category].[Bikes] [Product].[Category].[Bikes]
) [Product].[Subcategory].[All Products],
[Geography].[Country].[All Geographies],
[Measures].[Reseller Sales Amount]
)
Attributes Partial Rule 1 Rule 2 Rule 3 Completed
Tuple Default Member (All) Member First Member Tuple
Date.Calendar Year CY 2002 CY 2002
Date.Fiscal Year All Periods All Periods
Product.Category Bikes Bikes
Product.Subcategory All Products All Products
Geography.Country All Geographies All Geographies
Measures Reseller Sales Amount Reseller Sales Amount

17. SELECT
[Date].[Calendar Year].Members ON COLUMNS,
[Product].[Category].Members ON ROWS
FROM [My Simple Cube]
All Periods CY 2001 CY 2002 CY 2003 CY 2004
All Products $80,450,597 $8,065,435 $24,144,430 $32,202,669 $16,038,063
Accessories $571,298 $20,235 $92,735 $296,533 $161,794
Bikes $66,302,382 $7,395,349 $19,956,015 $25,551,775 $13,399,243
Clothing $1,777,840 $34,376 $485,587 $871,864 $386,013
Components $11,799,077 $34,376 $3,610,092 $5,482,497 $2,091,012

18. MDX
SELECT
[Date].[Calendar Y ] M b
[D ] [C l d Year].Members ON COLUMNS,
COLUMNS
[Product].[Category].Members ON ROWS
FROM [My Simple Cube]
SQL
SELECT
e.CalendarYear,
d.EnglishProductCategoryName,
SUM(a.SalesAmount)
FROM FactResellerSales a
INNER JOIN DimProduct b
ON a.ProductKey=b.ProductKey
d b d
INNER JOIN DimProductSubcategory c
ON b.ProductSubcategoryKey=c.ProductSubcategoryKey
INNER JOIN DimProductCategory d
ON c.ProductCategoryKey=d.ProductCategoryKey
INNER JOIN DimDate e
ON a.OrderDateKey=e.DateKey
GROUP BY
e.CalendarYear,
d.EnglishProductCategoryName

19. MDX
WITH MEMBER [Measures].[Total Sales Amount] AS
[Measures].[Internet Sales Amount] + [Measures].[Reseller Sales Amount]
[M ] [I Sl A ] [M ] [R ll S l A ]
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])
SQL
SELECT
m.EnglishProductName,
o.TotalSalesAmount
FROM dbo.DimProduct m
INNER JOIN ( -- TOP 10 PRODUCTS OF 2003
SELECT TOP 10
a.ProductKey, SUM(a.SalesAmount) AS TotalSalesAmount
d ( l ) l l
FROM (
SELECT
x.productkey, x.salesamount
FROM dbo.FactInternetSales x
INNER JOIN dbo.DimDate y
ON x.OrderDateKey=y.DateKey
WHERE y.CalendarYear=2003
UNION ALL
SELECT
x.productkey, x.salesamount

22. Lessons Learned
L L d
Clearly d fi
Cl l define audience
di
Align technology with audience
Actively engage audience

23. Thank You!
brysmi@microsoft.com