OLAP Systems and Multidimensional Expressions II

Transcription

1 OLAP Systems and Multidimensional Expressions II Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master studies, first semester Academic year 2014/15 (winter course) 1 / 31

2 Review of the previous lectures Mining of massive datasets Evolution of database systems: operational vs. analytical systems. Dimensional modeling. Operational vs. analytical systems. Extraction, transformation and load of data. 2 / 31

3 Review of the previous lectures Mining of massive datasets Evolution of database systems: operational vs. analytical systems. Dimensional modeling. Operational vs. analytical systems. Extraction, transformation and load of data. OLAP Systems and Multidimensional Expressions III: 2 / 31

4 Review of the previous lectures Mining of massive datasets Evolution of database systems: operational vs. analytical systems. Dimensional modeling. Operational vs. analytical systems. Extraction, transformation and load of data. OLAP Systems and Multidimensional Expressions III: ROLAP SQL 2 / 31

5 Review of the previous lectures Mining of massive datasets Evolution of database systems: operational vs. analytical systems. Dimensional modeling. Operational vs. analytical systems. Extraction, transformation and load of data. OLAP Systems and Multidimensional Expressions III: ROLAP SQL MOLAP 2 / 31

6 Review of the previous lectures Mining of massive datasets Evolution of database systems: operational vs. analytical systems. Dimensional modeling. Operational vs. analytical systems. Extraction, transformation and load of data. OLAP Systems and Multidimensional Expressions III: ROLAP SQL MOLAP... 2 / 31

7 Outline 1 Motivation 2 MDX 3 Summary 3 / 31

8 Outline 1 Motivation 2 MDX 3 Summary 4 / 31

9 Multidimensional reports OLAP servers provide an effective solution for accessing and processing large volumes of high dimensional data. OLAP systems provide tools for multidimensional reporting. 5 / 31

10 Querying OLAP systems We can use SQL to query OLAP systems: SELECT Name, AVG(Grade) FROM Students grades G, Student S WHERE G.Student = S.ID GROUP BY Name; 6 / 31

11 Querying OLAP systems We can use SQL to query OLAP systems: SELECT Name, AVG(Grade) FROM Students grades G, Student S WHERE G.Student = S.ID GROUP BY Name; But the query result is a relational table, not a multidimensional report. 6 / 31

12 Querying OLAP systems We can use SQL to query OLAP systems: SELECT Name, AVG(Grade) FROM Students grades G, Student S WHERE G.Student = S.ID GROUP BY Name; But the query result is a relational table, not a multidimensional report. We need a query language for multidimensional reporting. 6 / 31

13 Querying OLAP systems We can use SQL to query OLAP systems: SELECT Name, AVG(Grade) FROM Students grades G, Student S WHERE G.Student = S.ID GROUP BY Name; But the query result is a relational table, not a multidimensional report. We need a query language for multidimensional reporting. MDX (Multidimensional Expressions) is a language of this kind. 6 / 31

14 Outline 1 Motivation 2 MDX 3 Summary 7 / 31

15 Multidimensional expressions For OLAP queries, MDX is an alternative to SQL. 8 / 31

16 Multidimensional expressions For OLAP queries, MDX is an alternative to SQL. MDX SELECT {[Time].[1997],[Time].[1998]} ON COLUMNS, {[Measures].[Sales],[Measures].[Cost]} ON ROWS FROM Warehouse WHERE ([Store].[All Stores].[USA]) 8 / 31

17 An MDX query must contain the following information: The number of axes on which the result is presented. The set of tuples to include on each axis of the MDX query. The name of the cube that sets the context of the MDX query. The set of members or tuples to include on the slicer axis. 9 / 31

18 The syntax of a basic MDX query that includes the use of the SELECT, FROM, and WHERE clauses: [WITH <SELECT WITH clause> [, <SELECT WITH clause>...]] SELECT [* (<SELECT query axis clause> [, <SELECT query axis clause>...])] FROM <SELECT subcube clause> [<SELECT slicer axis clause>] [<SELECT cell property list clause>] 10 / 31

19 Main concepts of MDX: Dimension, Hierarchy, Level, Member, Measure, Tuple, Set, Axis. 11 / 31

20 MDX SELECT {[CARS].[All CARS].[Chevy], [CARS].[All CARS].[Ford]} ON ROWS, {[DATE].[All DATE].[March], [DATE].[All DATE].[April]} ON COLUMNS FROM MDDBCARS; 12 / 31

21 MDX SELECT {[CARS].[All CARS].[Chevy], [CARS].[All CARS].[Ford]} ON ROWS, {[DATE].[All DATE].[March], [DATE].[All DATE].[April]} ON COLUMNS FROM MDDBCARS; March April Chevy $ $ Ford $ $ / 31

22 MDX SELECT {[CARS].[ALL CARS].[CHEVY], [CARS].[ALL CARS].[FORD]} ON ROWS, {[DATE].[ALL DATE].[MARCH], [DATE].[ALL DATE].[APRIL]} ON COLUMNS FROM MDDBCARS WHERE ([MEASURES].[SALES N]) 13 / 31

23 MDX SELECT {[CARS].[ALL CARS].[CHEVY], [CARS].[ALL CARS].[FORD]} ON ROWS, {[DATE].[ALL DATE].[MARCH], [DATE].[ALL DATE].[APRIL]} ON COLUMNS FROM MDDBCARS WHERE ([MEASURES].[SALES N]) Sales N March April Chevy Ford / 31

24 SQL vs. MDX Single member SQL: where City = Redmond MDX: [City].[Redmond] Multiple members (a set) SQL: where City IN ( Redmond, Seattle ) MDX: { [City].[Redmond], [City].[Seattle] } 14 / 31

25 SQL vs. MDX SQL: SELECT Sum(Sales), City FROM Sales_Table WHERE City IN ( Redmond, Seattle ) GROUP BY City MDX 15 / 31

26 SQL vs. MDX SQL: SELECT Sum(Sales), City FROM Sales_Table WHERE City IN ( Redmond, Seattle ) GROUP BY City MDX SELECT Measures.Sales ON 0, NON EMPTY {[City].[Redmond], [City].[Seattle]} ON 1 FROM Sales_Cube 15 / 31

27 SQL vs. MDX SQL: SELECT Sum(Sales) FROM Sales_Table WHERE City IN ( Redmond, Seattle ) MDX 16 / 31

28 SQL vs. MDX SQL: SELECT Sum(Sales) FROM Sales_Table WHERE City IN ( Redmond, Seattle ) MDX SELECT Measures.Sales ON 0 FROM Sales_Cube WHERE {Redmond, Seattle} 16 / 31

29 MDX SELECT {[CARS].[ALL CARS].[CHEVY], [CARS].[ALL CARS].[FORD]} ON COLUMNS, {[DATE].[ALL DATE].[JANUARY]:[DATE].[ALL DATE].[APRIL]} ON ROWS FROM MDDBCARS 17 / 31

30 MDX SELECT {[CARS].[ALL CARS].[CHEVY], [CARS].[ALL CARS].[FORD]} ON COLUMNS, {[DATE].[ALL DATE].[JANUARY]:[DATE].[ALL DATE].[APRIL]} ON ROWS FROM MDDBCARS Chevy Ford January $ $ February $ $ March $ $ April $ $ / 31

31 MDX SELECT {[CARS].[ALL CARS].[CHEVY], [CARS].[ALL CARS].[FORD]} ON COLUMNS, {[DATE].MEMBERS} ON ROWS FROM MDDBCARS 18 / 31

32 MDX SELECT {[CARS].[ALL CARS].[CHEVY], [CARS].[ALL CARS].[FORD]} ON COLUMNS, {[DATE].MEMBERS} ON ROWS FROM MDDBCARS Chevy Ford 1998 $ $ $ $ $ $ $ $ / 31

33 MDX SELECT {[CARS].[ALL CARS].[FORD].CHILDREN} ON COLUMNS, {[DATE].MEMBERS} ON ROWS FROM MDDBCARS 19 / 31

34 MDX SELECT {[CARS].[ALL CARS].[FORD].CHILDREN} ON COLUMNS, {[DATE].MEMBERS} ON ROWS FROM MDDBCARS Ford Mustang Ford Taurus $ $ $ $ $ $ $ $ / 31

35 MDX SELECT {([CARS].[ALL CARS].[CHEVY], [MEASURES].[SALES SUM]), ([CARS].[ALL CARS].[CHEVY], [MEASURES].[SALES N]), ([CARS].[ALL CARS].[FORD], [MEASURES].[SALES SUM]), ([CARS].[ALL CARS].[FORD], [MEASURES].[SALES N]) } ON COLUMNS, {[DATE].MEMBERS} ON ROWS FROM MDDBCARS 20 / 31

36 MDX SELECT {([CARS].[ALL CARS].[CHEVY], [MEASURES].[SALES SUM]), ([CARS].[ALL CARS].[CHEVY], [MEASURES].[SALES N]), ([CARS].[ALL CARS].[FORD], [MEASURES].[SALES SUM]), ([CARS].[ALL CARS].[FORD], [MEASURES].[SALES N]) } ON COLUMNS, {[DATE].MEMBERS} ON ROWS FROM MDDBCARS Chevy Ford Sales Sum Sales N Sales Sum Sales N 1998 $ $ $ $ $ $ $ $ / 31

37 MDX SELECT {CROSSJOIN({[CARS].[ALL CARS].[CHEVY], [CARS].[ALL CARS].[FORD]}, {[MEASURES].[SALES SUM], [MEASURES].[SALES N]}) } ON COLUMNS, {[DATE].MEMBERS} ON ROWS FROM MDDBCARS 21 / 31

38 MDX SELECT {CROSSJOIN({[CARS].[ALL CARS].[CHEVY], [CARS].[ALL CARS].[FORD]}, {[MEASURES].[SALES SUM], [MEASURES].[SALES N]}) } ON COLUMNS, {[DATE].MEMBERS} ON ROWS FROM MDDBCARS Chevy Ford Sales Sum Sales N Sales Sum Sales N 1998 $ $ $ $ $ $ $ $ / 31

39 MDX SELECT NON EMPTY [Store Type].[Store Type].MEMBERS ON COLUMNS, FILTER([Store].[Store City].MEMBERS, (Measures.[Profit], [Time].[1997]) > ) ON ROWS FROM [Sales] WHERE (Measures.[Profit], [Time].[Year].[1997]) 22 / 31

40 MDX SELECT NON EMPTY [Store Type].[Store Type].MEMBERS ON COLUMNS, FILTER([Store].[Store City].MEMBERS, (Measures.[Profit], [Time].[1997]) > ) ON ROWS FROM [Sales] WHERE (Measures.[Profit], [Time].[Year].[1997]) Profit Normal 24 Hours Toronto $ $ Vancouver $ $ New York $ $ Chicago $ $ / 31

41 MDX SELECT Measures.MEMBERS ON COLUMNS, ORDER({[Store].[Store City].MEMBERS}, Measures.[Sales Count], DESC) ON ROWS FROM [Sales] 23 / 31

42 MDX SELECT Measures.MEMBERS ON COLUMNS, ORDER({[Store].[Store City].MEMBERS}, Measures.[Sales Count], DESC) ON ROWS FROM [Sales] Profit Sales Count... New York $ Chicago $ Toronto $ Vancouver $ / 31

43 : MDX WITH MEMBER [Time].[Year Difference] AS [Time].[2nd half] - [Time].[1st half] SELECT { [Account].[Income], [Account].[Expenses] } ON COLUMNS, { [Time].[1st half], [Time].[2nd half], [Time].[Year Difference] } ON ROWS FROM [Financials] 24 / 31

44 : MDX WITH MEMBER [Time].[Year Difference] AS [Time].[2nd half] - [Time].[1st half] SELECT { [Account].[Income], [Account].[Expenses] } ON COLUMNS, { [Time].[1st half], [Time].[2nd half], [Time].[Year Difference] } ON ROWS FROM [Financials] Income Expenses 1st Half st Half Year Difference / 31

45 MDX WITH MEMBER [Account].[Net Income] AS ([Account].[Income] - [Account].[Expenses]) / [Account].[Income] SELECT { [Account].[Income], [Account].[Expenses], [Account].[Net Income] } ON COLUMNS, { [Time].[1st half], [Time].[2nd half] } ON ROWS FROM [Financials] 25 / 31

46 MDX WITH MEMBER [Account].[Net Income] AS ([Account].[Income] - [Account].[Expenses]) / [Account].[Income] SELECT { [Account].[Income], [Account].[Expenses], [Account].[Net Income] } ON COLUMNS, { [Time].[1st half], [Time].[2nd half] } ON ROWS FROM [Financials] Income Expenses Net Income 1st Half st Half / 31

47 MDX WITH MEMBER [Time].[Year Difference] AS [Time].[2nd half] - [Time].[1st half], SOLVE ORDER = 1 MEMBER [Account].[Net Income] AS ([Account].[Income] - [Account].[Expenses]) / [Account].[Income], SOLVE ORDER = 2 SELECT { [Account].[Income], [Account].[Expenses], [Account].[Net Income] } ON COLUMNS, { [Time].[1st half], [Time].[2nd half], [Time].[Year Difference] } ON ROWS FROM [Financials] 26 / 31

48 Income Expenses Net Income 1st Half st Half Year Difference / 31

49 MDX WITH MEMBER [Time].[Year Difference] AS [Time].[2nd half] - [Time].[1st half], SOLVE ORDER = 2 MEMBER [Account].[Net Income] AS ([Account].[Income] - [Account].[Expenses]) / [Account].[Income], SOLVE ORDER = 1 SELECT { [Account].[Income], [Account].[Expenses], [Account].[Net Income] } ON COLUMNS, { [Time].[1st half], [Time].[2nd half], [Time].[Year Difference] } ON ROWS FROM [Financials] 27 / 31

50 Income Expenses Net Income 1st Half st Half Year Difference / 31

51 MDX WITH SET [Quarter1] AS GENERATE([Time].[Year].MEMBERS, {[Time].CURRENTMEMBER.FIRSTCHILD}) SELECT [Quarter1] ON COLUMNS, [Store].[Store Name].MEMBERS ON ROWS FROM [Sales] WHERE (Measures.[Profit]) 28 / 31

52 MDX WITH SET [Quarter1] AS GENERATE([Time].[Year].MEMBERS, {[Time].CURRENTMEMBER.FIRSTCHILD}) SELECT [Quarter1] ON COLUMNS, [Store].[Store Name].MEMBERS ON ROWS FROM [Sales] WHERE (Measures.[Profit]) 1998Q1 1999Q1... Saturn $ $ Media Markt $ $ Avans $ $ / 31

53 Outline 1 Motivation 2 MDX 3 Summary 29 / 31

54 Summary Three types of OLAP servers: ROLAP, MOLAP, and HOLAP. Several approaches for querying data warehouses. ROLAP servers: SQL and its OLAP extensions. MOLAP servers: MDX. 30 / 31

55 Bibliography A. Pelikant, Hurtownie danych. Od przetwarzania analitycznego do raportowania, Helion 2011 SAS OLAP Server MDX Guide Third Edition, 2006 MSDN: SQL Server Developer Center, / 31