1. Microsoft Confidential

2. We look at the world... with our own eyes...

3. And no matter what our perspective is...

4. We understand that people play the most important role

5. People interact, communicate and relate each other every day...

6. They relate in a one to many relationships...

7. On a one to one relationships...

8. And in a many-to-many relationships...

9. With all the pros and cons... for each one of them

10. So for the SSAS …... Cube perspective of the world......

11. The way relationships are build… is extremelly important

12. Specially when we are talking about M2M relationships

14. 14 Data measure group M2M dimension Intermediate measure group Intermediate dimension

16. 16 QueryQuery By M2M Dim IN MEMORY INNER JOIN

17. As the data size of the records in the join increases The performance of the run-time join in SSAS suffers 17 IN MEMORY INNER JOIN

18. Query performance in SSAS is intrinsically and linearly tied to the size of the data being joined between Data and Intermediate measure groups 18

19. There are 3 optimization techniques: Defining aggregations Partitioning Matrix relationship 19

20. What is an aggregation? Pre-calculated summary of data that SSAS uses to enhance query performance Moves the calculation to the processing phase Summarizes measures by a combination of dimension attributes 20 1st1st

21. Aggregations Work by reducing the number of records that the storage engine needs to scan from disk in order to satisfy a query Provides a significant benefit only if the size of the aggregation is significantly smaller than the size of the original table 21

22. Where to define aggregations in a M2M scenario? 22

23. Data measure group aggregations for M2M queries Include in the aggregation the granularity attribute of all dimensions that join with the intermediate measure group Do not include the attribute from the M2M dimension (occurs at query time as part of the run-time join) 23

24. Benefits The benefit is directly related to the size of this aggregation compared to the size of the data measure group An aggregation whose size is >=1/3 the size of the facts themselves is not considered to be useful 24

25. Intermediate measure group aggregations for M2M queries Include in the aggregation the granularity attribute of the dimensions in the intermediate measure group that relates to the data measure group The attribute in the dimension that you wish to aggregate 25

26. Benefits The benefit is directly related to the resulting size of the aggregation compared to the size of intermediate measure group 26

27. Partitioning Enables SSAS to retrieve data from only a portion of data in a measure group Parallelize data retrieval when data must be retrieved from multiple partitions 27 2nd2nd

28. Where to define partitions in a M2M scenario? 28

29. Data measure group The data measure group should generally be partitioned by the same dimension attribute members as the intermediate measure group 29

30. Intermediate measure group You must relate the partitioning dimensions to the intermediate measure Benefit Reduces the data size used by the run-time join between intermediate and data measure groups When the M2M query can be resolved from only a few (or one) partitions 30

31. If the M2M query must be resolved by retrieving data from many or all partitions The technique provides little or no value and may even increase response times 31

32. Design patern The intermediate measure group must Be partitioned by one or more common dimensions used in queries Be related to each dimension that is used for partitioning The intermediate fact table must Contain the dimension surrogate key That is used for the measure group partitioning To relate with the dimension 32

33. Increases M2M query performance by reducing the size of the run-time join Uses a process of compression to eliminate unnecessary repetitiveness in the intermediate fact table Collapses the size of the intermediate fact table 33 3rd3rd

34. Two phases: Compression and creation of the matrix key Implementing the matrix optimization 34

35. Creates a compressed intermediate fact table by taking the following steps: Identify common dimension member combinations in the intermediate fact table Each set of common dimension member combinations is assigned a surrogate key (matrix key) Repeated combinations are eliminated 35

36. 36

37. Requires changes to aspects of the M2M relationship at the following levels: The relational data warehouse implementation level The cube design level The ETL implementation level 37

38. 38

39. Matrix dimension table creation 39

40. Requires an intermediate fact table that relates the M2M dimensions to the base fact through the matrix key Will have the matrix key column and one or more dimension key columns 40

41. Add the Matrix Key column to the base fact table 41

42. Works in the same way that any M2M dimension relationship The difference is Shared dimension between the base measure group and the intermediate measure group is the matrix dimension, rather than the fact dimension 42

43. In Data Source View, add two tables for Matrix dimension New intermediate fact table 43

44. Add a new SSAS database dimension based on the matrix dimension table Add this dimension to the cube that contains the base measure group 44

45. Add a new SSAS measure group to act as the intermediate measure group for resolving the M2M relationships Remove the existing Sales Reasons measure group 45

46. Define the Dimension Usage to associate the dimension tables to the measure groups Both the base and intermediate measure group must have a direct relationship to the matrix dimension 46

47. The intermediate measure group also needs a direct relationship to the dimension or dimensions that participate in the M2M relationship The dimensions referenced in the second point must also be related to the base measure group 47

48. The ETL involves Updating the relational data warehouse tables with new matrix relationships Associating the fact tables to the matrix dimensions 48

49. The general guidelines are: Adding new key concatenation strings to the matrix dimension table for new records being added to the fact table Adding the matrix dimension key associated with each transaction ID to the base fact table Adding the key of the matrix dimension with each M2M dimension key record 49

50. Questions and Answers 50