Automatic cross filtering between columns of the same table or related tables is a very powerful feature of DAX. It allows a measure to evaluate to different values for different cells in a pivot table even though the DAX expression for the measure does not change. Filter context is the underlying mechanism that enables this magic behavior. But it is also a very tricky concept that even befuddles some DAX experts. Marco Russo and Alberto Ferrari have introduced DAX filter context in Chapter 6 of their book

*Microsoft PowerPivot for Excel 2010*. Marco has also blogged about how Calculate function works. Recently I have run into many questions from advanced DAX users which tell me that people are still confused about how filter context works exactly. And this will be the subject of today’s post.This post assumes that you already have basic knowledge about measures, row context, filter context, and DAX functions Calculate, Values, All, etc.

**A level 200 pop quiz on DAX**

If you think you already know how filter context works, let me ask you a couple of level 200 questions on DAX to see if you can explain the nuances of some DAX expressions. If you don’t feel like being challenged now, it is still beneficial to read the questions so you have some examples to better understand the following sections. The questions are based on the data model inside the publicly available sample PowerPivot workbook

*Contoso Samples DAX Formulas.xlsx*. You can download the sample workbook to try out the formulas yourself if you want to, but it is not required to answer the questions.**Question #1**.

People have heard that fact tables are automatically filtered by slices on dimension tables, but not the other way around, or in more general terms, if there is a relationship from table A to table B, A is automatically filtered by any slices on columns of B but B is not automatically filtered by any slices on columns of A. So if you select

DimProductSubcategory[ProductSubcategoryName] = “Air Conditioners”

on a pivot table slicer, measure

CountRows(DimProduct)

returns 62 as DimProduct is limited to air conditioners. On the other hand, if you select

DimProduct[ProductLabel] = “0101001”,

measure

CountRows(DimProductSubcategory)

returns 44 instead of just 1 although only a single product is selected. To filter DimProductSubcategory by the selected product label, you can define a measure as

Calculate(CountRows(DimProductSubcategory), DimProduct)

which returns 1. So it seems like when you explicitly add DimProduct as a setfilter argument of Calculate, DimProductSubcategory will be filtered by DimProduct. But if I define a measure as

Calculate(CountRows(DimProductSubcategory), Values(DimProduct[ProductLabel]))

to explicitly add the column that I know having a slice from the pivot table to the Calculate function , the measure formula returns 44 again. So what makes setfilter expression DimProduct work but Values(DimProduct[ProductLabel]) not work even though the filter only comes from [ProductLabel] column? If you think you have to add foreign key DimProduct[ProductSubcategoryKey] to the filter context in order for DimProductSubcategory to be filtered by DimProduct, you can try

Calculate(CountRows(DimProductSubcategory), Values(DimProduct[ProductSubcategoryKey]))

but it still returns 44. If you have enough patience, you can use Values function to explicitly add all 33 columns in DimProduct one by one as setfilter arguments to Calculate function and you still will get 44 back. So what is the difference between table expression DimProduct and the enumeration of all 33 columns in that table?

**Question #2**.

There are 2556 records in DimDate table, therefore if you add a measure with expression

CountRows(DimDate)

to a pivot table without any filters, the measure value would be 2556. Now if you add a second measure with expression

Calculate(CountRows(DimDate), FactSales)

to the same pivot table, the measure value would be 1096 since DimDate table is filtered by FactSales table and only dates with sales records are included. But if you add a third measure with expression

Calculate(CountRows(DimDate), All(FactSales))

to the pivot table, the measure value becomes 2556 again. Since this pivot table has no filters anywhere, shouldn’t FactSales and All(FactSales) return the same table? Now add a fourth measure with expression

Calculate(CountRows(DimDate), Filter(All(FactSales), true))

to the pivot table, the measure value becomes 1096 again. All three setfilter arguments return exactly the same table, why would we get back different results?

With these questions in mind, let’s examine the logic foundation upon which the magic world of DAX is built. At the end of the post, you will be able to find a logical explanation to all these seemingly inconsistent results.

**The expanded view of a DAX base table**

The best way to understand DAX cross table filtering is to think of each base table as extended by its related tables. When a relationship is created from table A to table B, the new A, which is really A left outer join B, includes both columns of A and columns of B. So in DAX, a table reference FactSales really refers to

FastSales

LOJ DimProduct LOJ DimProductSubcategory LOJ DimProductCategory

LOJ DimStore LOJ DimGeography LOJ DimDate LOJ DimChannel LOJ DimPromotion,

where LOJ means left outer join. This interpretation makes it easy to understand some other DAX syntax. For example, in DAX expression

Filter(FactSales, Related(DimProduct[ProductLabel]) = “0101001”),

Related(DimProduct[ProductLabel]) refers to the value of column DimProduct[ProductLabel] in the extended FactSales table. As a second example, DAX expression

AllExcept(FactSales, DimProduct[ProductLabel])

returns a table with all columns of extended FactSales table except for column DimProduct[ProductLabel].

**Build initial filter context**

DAX filter context is a stack of tables. At the beginning, the stack is empty. Given a pivot table, a filter context is initially populated by adding slicers and page filters. For each cell in a pivot table, current members of row labels and column labels also add filters to filter context. Other pivot table operations like visual totals add to initial filter context as well but I will keep things simple here. At this point, we have set up an initial filter context in which the measure expression of the current cell is to be evaluated.

**Measure invocation**

If SumOfSales is the name of a measure and Sum(Sales[Amount]) is its DAX formula, DAX expression

[SumOfSales]

is equivalent to

Calculate(Sum(Sales[Amount]))

and DAX expression

[SumOfSales](Date[Year] = 2001, Store[Country] = “USA”)

is equivalent to

Calculate(Sum(Sales[Amount]), Date[Year] = 2001, Store[Country] = “USA”).

So the syntax sugar which makes a measure name look like a function name is just a clever way to add tables to filter context before evaluating the expression associated with the measure. Since invoking a measure implicitly calls Calculate, from now on I’ll just focus on Calculate function as the same rules apply equally to measures.

**Add tables to filter context**

Calculate function performs the following operations:

1. Create a new filter context by cloning the existing one.

2. Move current rows in the row context to the new filter context one by one and apply blocking semantics against all previous tables.

3. Evaluate each setfilter argument in the old filter context and then add setfilter tables to the new filter context one by one and apply blocking semantics against all tables that exist in the new filter context before the first setfilter table is added.

4. Evaluate the first argument in the newly constructed filter context.

If a new table is added to filter context and it has blocking semantics against some tables already in the filter context, the affected tables are checked one by one, all common columns with the new table are marked as blocked on the existing table.

Let’s look at an example. Assume the current filter context has two filters: one filter is Date[Year] = 2011, the other filter is Store[Country] = “Canada”. We want to evaluate the following expression in the context

AverageX(Distinct(Date[Month]), Calculate(Sum(Sales[Amount]), Store[Country] = “USA”)).

The first argument of AverageX sets a month in row context. When it comes to Calculate, it first removes the month from row context and adds it to filter context, it does not block anything since there is no [Month] column in existing filters. Next Calculate adds Store[Country] = “USA” to filter context which blocks existing filter Store[Country] = “Canada”. When Sum(Sales[Amount]) is evaluated, Sales table is filtered by the current month in 2011 and stores in USA.

**Targets of filter context**

After so much effort populating and modifying a filter context, when will the filters be applied? In DAX, the filters in a filter context apply to following DAX table expressions:

1. A table expression that is simply a table reference, such as FactSales.

2. Values(Table[Column]).

3. Distinct(Table[Column]).

In cases of 2 and 3, the Table is filtered by filter context and then distinct values of [Column] are extracted from the filtered table.

So if your expression is

Calculate(SumX(Filter(FactSales, [SalesQuantity] > 1000), [SalesAmount]), Date[Year] = 2011),

the filter context only restricts FactSales and has no effect whatsoever on other parts of the formula. If you image every DAX formula is represented as a tree of parent and child function calls, a filter context is built at the top or in the middle of the tree but takes effect at leaf level table nodes.

Note that DAX function Sum(T[C]) is just a shorthand for SumX(T, [C]), the same is true for other aggregation functions which take a single column reference as argument. Therefore the table in those aggregation functions is filtered by filter context.

**Apply filters to a target table**

Finally we have identified a target table and are ready to apply filters from filter context. For each filter table in the filter context, we check to see if there are any common columns between the target table and the unblocked columns of the filter table. If there is at least one common column, the target table is semi-joined with the filter table, or in SQL-like terms

SELECT *

FROM TargetTable AS t

WHERE EXISTS

(

SELECT *

FROM FilterTable AS f

WHERE t.CommonColumns = f.CommonColumns

)

Each filter table is applied to the target table independently, so the target table is filtered by all relevant filters.

**All, AllExcept, AllNoBlankRow**

So far I have said that each setfilter argument of Calculate function returns a table which is added to filter context. Well, that is true as long as the setfilter is not one of the All functions. The All functions should really be renamed as BlockColumns when they are used as setfilter arguments. If one of the All functions is used as the top-level function of setfilter, it

**only**blocks common columns of earlier tables but does**not**add itself to filter context.In all other places, including as a sub-expression of a setfilter but not at the top level, All functions behave like any other DAX table expressions and always return a table. One special feature of All functions is that the Table argument inside All(Table), All(Table[Column]), AllExcept(Table, …), AllNoBlankRow(Table), etc. is not filtered by the current filter context.

**Pop quiz answers**

**Answer to question #1**.

When the initial filter context contains column DimProduct[ProductLabel], table DimProductSubcategory is not filtered as it does not have that column.

Now look at the next formula

Calculate(CountRows(DimProductSubcategory), DimProduct).

The setfilter argument DimProduct is filtered by [ProductLabel], and then table DimProductSubcategory is filtered by table DimProduct since they both share the columns from table DimProductSubcategory and table DimProductCategory.

Move onto the next two formulas

Calculate(CountRows(DimProductSubcategory), Values(DimProduct[ProductLabel]))

Calculate(CountRows(DimProductSubcategory), Values(DimProduct[ProductSubcategoryKey]))

Both setfilter arguments are a single column table and the column comes from table DimProduct. Since table DimProductSubcategory does not have any column from DimProduct, it is not filtered by filter context. For the same reason, you can add any columns from DimProduct to the filter context and none of them would impact DimProductSubcategory.

**Answer to question #2**.

In the first formula

Calculate(CountRows(DimDate), FactSales)

Both table DimDate and table FactSales share columns from DimDate, so DimDate is filtered by FactSales.

In the second formula

Calculate(CountRows(DimDate), All(FactSales))

All(FactSales) blocks any columns from FactSales, but since the filter context is empty, it has no effect. When DimDate is evaluated, filter context is still empty.

In the third formula

Calculate(CountRows(DimDate), Filter(All(FactSales), true))

The All function is not at the top level of setfilter argument, table Filter(All(FactSales), true) is added to filter context, table DimDate is filtered by filter context for the same reason as in the first formula.

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Jeffrey, this is a very interesting post - thank you so much for sharing this knowledge on DAX internals

ReplyDeleteI have a question regarding the first example.

For the following DAX expression

Calculate(CountRows(DimProductSubcategory), Values(DimProduct[ProductLabel]))

You said that

[Since table DimProductSubcategory does not have any column from DimProduct, it is not filtered by filter cont]

Does that mean that because VALUES return a 1 column table, it does not have the same base table + extended table configuration as a regular table reference would have? In other words, VALUES would only have one column, hence it won't block any other table column [except the one that is returning]?

Thanks!

- Javier Guillen

Did you heard what Rob Matts said about that?

ReplyDeletegeneric nolvadex

The statement is a bit confusing. Let me clarify. The extended version of DimProductSubcategory does not have any common columns with the non-extended part of DimProduct. Column DimProduct[ProductLabel] comes from the non-extended part of DimProduct, therefore does not filter DimProductSubcategory directly.

ReplyDeleteYou are right that Values() function returns a single column table therefore can only block one column in the filter context.

Can you please tell me why Calculate(CountRows(DimProductSubcategory), Values(DimProduct[ProductSubcategoryKey])) doesn't have common shared column? I think they are both contain ProductSubcategoryKey. I guess the reason why it doesn't filter is because DimProduct[ProductLabel] has no common column with Values(DimProduct[ProductSubcategoryKey]), so DimProduct[ProductLabel] = “0101001” will not filter Values(DimProduct[ProductSubcategoryKey]).

Deletenever mind, I think I understand now.

DeleteHi Jeffrey,

ReplyDeleteFirstly amazing post !

I have a question WRT to Filter functions in a filter context of pivot table.

Why is it an error if we write Calculate(Sum(Quota),Filter(Budget,Budget[Month]=Date[Month])) When i have Date[Month] on my rows ? Is the filter unable to equate Budget[Month]=Date[Month]to the existing Date[Month] on Rows ? Secondly, If I replace Date[Month] with Max(Date[Month]) it works . Can you share your thoughts ?

A column added to Rows on a pivot table is in the filter context, but row context. A column reference like Date[Month] in DAX is used to get a value in the current row context, you have to use Values(Date[Month]) to retrieve values from the current filter context.

ReplyDeleteMax(Date[Month]) is equivalent to MaxX(Date, Date[Month]), here MaxX function starts a new scan over the Date table, hence establishing a new row context from which the second argument Date[Month] can retrieve a value.

Thank you

DeleteThis comment has been removed by the author.

ReplyDeleteHi Jeffrey,

ReplyDeleteCould you please explain what the term "blocking semantic" means ?

The more recent terminology should be overwrite semantics. That means an inner filter will overwrite an outer filter on the same column. For example, in the following DAX expression with nested Calculate's:

ReplyDeleteCalculate(Calculate(Sum(Sales[Amount]), Customer[Name]="Ian"), Customer[Name]="Oliver")

the outer filter on Oliver is overwritten by the inner filter therefore the expression returns sum of sales amount by Ian. The outer filter on Oliver has no effect (because it's overwritten or blocked) when DAX engine calculates Sum(Sales[Amount]).

Filtering in Pivot table

ReplyDeleteI have a question regarding this sentences.

ReplyDelete"DAX filter context is a stack of tables."

Is a filter like

"Store[Country] = “USA”

either as a pivot filter or as a filter in a calculate function, "converted" into the full table from where the column is taken and than added to the filter context?

Store[Country] = "USA" is equivalent to Filter(All(Store[Country]), Store[Country] = "USA"). Therefore, the filter added to the filter context is a table of one column Store[Country] and one row "USA". This is different from Filter(All(Store), Store[Country] = "USA") which is a table containing all columns fore the underlying physical table 'Store'.

ReplyDeleteThis comment has been removed by the author.

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ReplyDeleteSorry the new comment notifications have been routed to my Junk Mail box. If you have a question, please post it under my new blog site: pbidax.wordpress.com

DeleteHello. Thank you for the article. I am becoming mad about the Calculate function, and I am a bit desperated. In your example:

ReplyDeleteAverageX(Distinct(Date[Month]), Calculate(Sum(Sales[Amount]), Store[Country] = “USA”)).

You say that, after setting a month in the row context as a result of the iterator AverageX, Calculate turns it into the filter context, but why is it that if that expression is not surrounded by the Calculate function? As far as I understand it, it should do filter transition with the row context created by its own first argument. Could you tell me what is the logic of operators and why calculate does context transition with row contexts generated otherwhere? Many thanks.

Hi there. Thanks for the effort to explain this difficult subject. However, I think that Marco Russo's and Alberto Ferrari's attempts at explanation are much more readable for the average user as presented in their ultimate book on DAX (can't remember the full title right now). I find the explanations given by them more user-friendly, even though very detailed (which is a plus as well). The 'blocking semantics' is certainly something that does not make things clearer, unfortunately, as it does not immediately translate well into intuition. The calculate function applies two types of interaction between filters---either filters are put in an AND state or one OVERWRITES the other. OVERWRITING is the right name and immediately forms the right intuition. I hope you'll agree.

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