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

Alberto Ferrari

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

Filter on Dim_CustomerFilter on Dim_Account

This filter is applied by the Tabular data model

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

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

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

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

All the rows in the Account table survived the FILTER

Two rows in the Account table survived the FILTER

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

The context is moved in a single Vertipaq operation

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

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

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

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

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

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

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

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

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

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

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

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

Inactive Relationships

1° M2M Pattern 2° M2M Pattern

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

2,700, , ,440 9,000 4,000,000,000

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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