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Many-to-many Relationships in DAX

Many-to-many Relationships in DAX. When Many To Many Are Really Too Many . Alberto Ferrari Senior Consultant SQLBI.COM. Who’s Speaking ?. BI Expert and Consultant Problem Solving Complex Project Assistance DataWarehouse Assesments and Development Courses, Trainings and Workshops

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Many-to-many Relationships in DAX

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  1. Many-to-many Relationships in DAX When Many To Many Are Really Too Many  Alberto Ferrari Senior Consultant SQLBI.COM

  2. Who’sSpeaking? • BI Expert and Consultant • ProblemSolving • Complex Project Assistance • DataWarehouseAssesments and Development • Courses, Trainings and Workshops • SQLBI: Branch of a Microsoft BI Gold Partner • Book Author, SSAS Maestro • alberto.ferrari@sqlbi.com • «Spaghetti English» • Getprepared, I can’t help it

  3. Agenda • Many To ManyRelationships • Data Model • DAX Formula pattern • ClassicalMany To Many • CascadingMany To Many • Survey Data Model • Basket Analysis

  4. Prerequisites • Wewill use, butnotdescribe: • Tabular basics • DAX basics • Evaluation contexts and interactions with relationships • Data modeling with Many To Many • Afterall… thisis a 400 session 

  5. Current Accounts Model M2M

  6. No support for M2M in Tabular • Facts • Multidimensionalhandles M2M relationships • Tabular doesnot • Thus, myfastestever session endshere • Or… we can dive into DAX and play with it

  7. Demo – Classical M2M • Wewillalways start lookingat the finalresult • Then, we dive into the DAX code

  8. The M2M DAX Pattern • Leverages • CALCULATE • RowContexts • FilterContexts • Automatictransformation of RowContextintoFilterContextusingCALCULATE • Next slide: the formula. Keepit in mind Itwillappearquiteoften from now on

  9. The DAX Formula of M2M Pattern AmountM2M:=CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,CALCULATE (COUNTROWS(Bridge_AccountCustomer) ) > 0 ))

  10. What the formula shouldperform AmountM2M:=CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,CALCULATE (COUNTROWS(Bridge_AccountCustomer) ) > 0 )) Filter on Dim_Customer Filter on Dim_Account

  11. How the formula works AmountM2M:=CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,CALCULATE (COUNTROWS(Bridge_AccountCustomer) ) > 0 )) The filterisapplied by theTabular data model

  12. How the formula works AmountM2M:=CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,CALCULATE (COUNTROWS(Bridge_AccountCustomer) ) > 0 )) FILTER: For eachrow in Dim_AccountCALCULATE: filters the bridge The filterisapplied by theTabular data model

  13. How the formula works AmountM2M:=CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,CALCULATE (COUNTROWS(Bridge_AccountCustomer) ) > 0 )) FILTER: For eachrow in Dim_AccountCALCULATE: filters the bridge Onlyrows in Dim_AccountwhereCOUNTROWS () > 0 survive the FILTER The filterisapplied by theTabular data model

  14. How the formula works AmountM2M :=CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,CALCULATE (COUNTROWS(Bridge_AccountCustomer) ) > 0 )) FILTER: For eachrow in Dim_AccountCALCULATE: filters the bridge Onlyrows in Dim_AccountwhereCOUNTROWS () > 0 survive the FILTER Thisfilterisapplied by theTabular data model SUMisevaluatedonly for the accounts filtered from the customers

  15. BI123|many-to-many In DAX The Karma of CALCULATE AmountM2M_Wrong:= CALCULATE(SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,COUNTROWS(Bridge_AccountCustomer) > 0 )) AmountM2M_Correct := CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,CALCULATE (COUNTROWS (Bridge_AccountCustomer) ) > 0 ))

  16. Wrong formula in action AmountM2M:=CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,COUNTROWS(Bridge_AccountCustomer) > 0 )) All the rows in the Account tablesurvived the FILTER

  17. Many-to-many in action AmountM2M:=CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,CALCULATE (COUNTROWS(Bridge_AccountCustomer) ) > 0 )) Tworows in the Account tablesurvived the FILTER

  18. BI123|many-to-many In DAX The Karma of CALCULATE AmountM2M_Wrong:= CALCULATE(SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,COUNTROWS(Bridge_AccountCustomer) > 0 )) AmountM2M_Correct:= CALCULATE (SUM (Fact_Transaction[Amount]),FILTER (Dim_Account,CALCULATE (COUNTROWS (Bridge_AccountCustomer) ) > 0 ))

  19. Many to Many - Conclusions • Complexity • Not in the data model • Only in the formula Goodunderstanding of CALCULATE and of relationships in Tabular needed Thisis just the beginningof ourjourney

  20. CascadingMany To Many

  21. CascadingMany To Many The pattern is the same, butthis time weneed to jumptwosteps to complete ourtask Filter on Dim_Category Filter on Dim_Account

  22. Cascading M2M- Demo

  23. CascadingMany To Many CALCULATE ( CALCULATE ( SUM (Fact_Transaction[Amount]), FILTER (Dim_Account, CALCULATE ( COUNTROWS (Bridge_AccountCustomer) ) > 0 )), FILTER (Dim_Customer, CALCULATE ( COUNTROWS (Bridge_CustomerCategory) ) > 0))

  24. CascadingMany To Many • Generic Formula • Works with anynumber of steps • Be careful • Always start with the farthesttable • And moveonestepat a time in the direction of the facttable • One CALCULATE for eachstep • Complexity: M x N (geometric…)

  25. Cascading Alternative Need for someETL here

  26. Cascading Alternative The bridge now «feels»threedifferentfilters, butthe formula becomes a classicalmany to manyand runsfaster

  27. FlattenedCascadingMany To Many • Flattened Data Model • Fasterthan the cascadingone • Simpler formula • Needs ETL • Worth a try in Multidimensionaltoo…

  28. Survey

  29. Survey Data Model • Facts: • Onecustomeranswersmanyquestions • Onequestionisanswered by manycustomers • Wewant to use a PivotTable to query this

  30. Survey Scenario • Question: «Whatis the yearlyincome of consultants?» In otherwords… • Take the customerswhoanswered «Consultant» at the «Job» question • Slicethemusing the answer to the question «YearlyIncome»

  31. Survey - Demo

  32. SurveyAnalytical Data Model • Two «Filter» Dimensions • One for «Job» = «Consultants» • One for «YearlyIncome» Question1 = «Job»Answer1 = «Consultant» Question2 = «YearlyIncome»

  33. Survey: The FinalResult • Value = CustomerCount • The facttable, this time, actsas the bridge

  34. SurveyAnalytical Data Model No relationshipbetween the fact table and the twofilterdimensions Becausehereisonlyonevalue for ID_Answer

  35. SurveyAnalytical Data Model • The twofilters • Are applied on the Customerstable • Use separate instancesof the Answerstable

  36. Survey: The DAX Formula IF ( COUNTROWS (VALUES (Filter1[ID_Answer])) = 1 && COUNTROWS (VALUES (Filter2[ID_Answer])) = 1, CALCULATE ( CALCULATE ( COUNTROWS (Customers), FILTER ( Customers, CALCULATE ( COUNTROWS (Answers), Answers[ID_Answer] = VALUES (Filter2[ID_Answer])) > 0 ) ), FILTER ( Customers, CALCULATE ( COUNTROWS (Answers), Answers[ID_Answer] = VALUES (Filter1[ID_Answer])) > 0 ) ) ) Additionalconditions toset the relationships withtwotables in DAX only

  37. Survey: The DAX Formula IF ( COUNTROWS (VALUES (Filter1[ID_Answer])) = 1 && COUNTROWS (VALUES (Filter2[ID_Answer])) = 1, CALCULATE ( CALCULATE ( COUNTROWS (Customers), FILTER ( Customers, CALCULATE ( COUNTROWS (Answers), Answers[ID_Answer] = VALUES (Filter2[ID_Answer])) > 0 ) ), FILTER ( Customers, CALCULATE ( COUNTROWS (Answers), Answers[ID_Answer] = VALUES (Filter1[ID_Answer])) > 0 ) ) ) The tworeferences to «Answers» work on separate instances of the sametable

  38. Survey - Conclusions • Verypowerful data model • Very compact • Duplicatesonlydimensions • Different from the same pattern in Multidimensional • Handlesanynumber of questions • Wehaveshowntwo • Butitisnot a limit • Interestingtopics • Facttableas the bridge • Relationships set in DAX • Can be queried with a simplePivotTable

  39. Basket Analysis

  40. Basket Analysis: The Scenario Of all the customerswhohavebought a Mountain Bike, howmanyhaveneverbought a mountain tire tube?

  41. Basket Analysis in SQL Two iterations over the fact table needed SELECT COUNT (DISTINCT A.CustomerID) FROM FactSales A INNER JOIN FactSalesB ON A.CustomerID= B.CustomerID WHERE A.ProductModel= ‘MOUNTAIN TIRE TUBE' AND A.Year<= 2004 AND B.ProductModel= ‘MOUNTAIN-100' AND B.Year<= 2004

  42. Look the query plan… Thisis the facttable… Do youreallylike to self-join it?

  43. Basket Analysis: The Data Model Of all the customerswhohavebought a Mountain Bike, howmanyhaveneverbought a mountain tire tube? We can filter «Mountain Tire Tube» with thistablebut… where do wefilter «Mountain Bike»?

  44. Basket Analysis: The Data Model 2° filter: «Mountain Bike» No relationships 1° filter: «Mountain Tire Tube»

  45. Basket Analysis- Demo

  46. The FinalResult 2° filter: «Mountain Tire Tube» HavingProduct = BoughtBoth NotHavingProduct = Bought Bike, No Tire 1° filter: «Mountain Bike»

  47. HavingProduct := CALCULATE ( COUNTROWS (DISTINCT (FactSales[CustomerKey])), FILTER ( ALL (DimTime), DimTime[TimeKey] <= MAX (DimTime[TimeKey]) ), FILTER (DimCustomer, SUMX (ProductFilter, CALCULATE ( COUNTROWS (FactSales), ALL (FactSales), FactSales[CustomerKey] = EARLIER (DimCustomer[CustomerKey]), FactSales[ProductKey] = EARLIER (ProductFilter[ProductKey]), FILTER ( ALL (DimTime), DimTime[TimeKey] <= MAX (DimTime[TimeKey]) ) ) ) > 0 ))

  48. HavingProduct := CALCULATE (COUNTROWS (DISTINCT (FactSales[CustomerKey])), FILTER ( ALL (DimTime), DimTime[TimeKey] <= MAX (DimTime[TimeKey]) ), FILTER (DimCustomer, SUMX (ProductFilter, CALCULATE ( COUNTROWS (FactSales), ALL (FactSales), FactSales[CustomerKey] = EARLIER (DimCustomer[CustomerKey]), FactSales[ProductKey] = EARLIER (ProductFilter[ProductKey]), FILTER ( ALL (DimTime), DimTime[TimeKey] <= MAX (DimTime[TimeKey]) ) ) ) > 0 )) Count the number of customers…

  49. HavingProduct := CALCULATE ( COUNTROWS (DISTINCT (FactSales[CustomerKey])),FILTER ( ALL (DimTime), DimTime[TimeKey] <= MAX (DimTime[TimeKey]) ), FILTER (DimCustomer, SUMX (ProductFilter, CALCULATE ( COUNTROWS (FactSales), ALL (FactSales), FactSales[CustomerKey] = EARLIER (DimCustomer[CustomerKey]), FactSales[ProductKey] = EARLIER (ProductFilter[ProductKey]), FILTER ( ALL (DimTime), DimTime[TimeKey] <= MAX (DimTime[TimeKey]) ) ) ) > 0 )) In the period of time before the end of the currentlyselectedfilter

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