1. Many-To-Many in DAX Alberto Ferrari Senior Consultant SQLBI DBI413

2. Alberto Ferrari alberto.ferrari@sqlbi.com www.sqlbi.com @FerrariAlberto

6. Classical M2M We will start looking at the final result Then, we dive into the DAX code Demo – Classical M2M

9. Filter on Dim_CustomerFilter on Dim_Account

10. This filter is applied by the Tabular data model

11. FILTER: For each row in Dim_Account CALCULATE: filters the bridge

12. This filter is applied by the Tabular data model FILTER: For each row in Dim_Account CALCULATE: filters the bridge Only rows in Dim_Account where COUNTROWS () > 0 survive the FILTER

13. This filter is applied by the Tabular data model FILTER: For each row in Dim_Account CALCULATE: filters the bridge Only rows in Dim_Account where COUNTROWS () > 0 survive the FILTER SUM is evaluated only for the accounts filtered from the customers

14. AmountM2M_Correct := CALCULATE ( SUM (Fact_Transaction[Amount]), FILTER ( Dim_Account, CALCULATE ( COUNTROWS (Bridge_AccountCustomer) ) > 0 ) )

15. All the rows in the Account table survived the FILTER

16. Two rows in the Account table survived the FILTER

17. AmountM2M_Correct := CALCULATE ( SUM (Fact_Transaction[Amount]), FILTER ( Dim_Account, CALCULATE ( COUNTROWS (Bridge_AccountCustomer) ) > 0 ) )

19. The context is moved in a single Vertipaq operation

23. Cascading M2M Many-to-many with more than one bridge table Cascading many-to-many

24. Filter on Dim_CategoryFilter on Dim_Account The pattern is the same, but this time we need to jump two steps to complete our task

25. CALCULATE( SUM( Fact_Transaction[Amount] ), CALCULATETABLE ( SUMMARIZE ( BridgeAccountCustomer, DimAccount[ID_Account] ), SUMMARIZE ( Bridge_CustomerCategory, DimCustomer[ID_Customer] ) ) )

27. Need for some ETL here The bridge now «feels» three different filters, but the formula becomes a classical many to many and runs faster

29. Survey A first example of the usage of the many-to-many pattern Using M2M on Surveys

32. Question1 = «Job» Answer1 = «Consultant» Question2 = «Yearly Income»

35. Additional conditions to set the relationships with two tables in DAX only

37. Basket Analysis A pretty complex scenario, solved with the many-to-many pattern Basket Analysis Demo

40. This is the fact table… Do you really like to self-join it?

41. We can filter «Mountain Tire Tube» with this table but… where do we filter «Mountain Bike»?

42. 2° filter: «Mountain Bike» 1° filter: «Mountain Tire Tube»

43. 1° filter: «Mountain Bike» 2° filter: «Mountain Tire Tube» HavingProduct = Bought Both NotHavingProduct = Bought Bike, No Tire

44. Inactive Relationships

47. 1° M2M Pattern 2° M2M Pattern

49. Querying M2M Well… what about performance of m2m in Tabular? Let us see them! M2M Performance

51. 2,700,000 176,000 140 230 1,440 9,000 4,000,000,000

53. DBI305 Developing and Managing a BI Semantic Model in Analysis Services DBI413 Many-to-Many Relationships in BISM Tabular DBI319 BISM: Multidimensional vs. Tabular DBI62-HOL Optimizing a MS SQL Server 2012 Tabular BI Semantic Model Book Signing at Book Store on Wednesday, June 13 th, 12:30pm-1:00pm

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