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There are people (at least I assume they exist) that plan out the words flying out of their mouths before they actually start speaking.  Those people lack imagination and an appreciation for the true art of throwing caution to the wind and blurting out whatever pops into your head.  It works fine for me a good 80-90% of the time!  I like to write blog posts the same way!  Even I have no idea what is about to happen, but I’m pretty excited. Also, there are people (and I know these exist – Chris Webb comes to mind) that don’t like to write blog posts against topics that have already covered. Ya, I’m not that guy either. Heck, I repeat my own posts sometimes.  In this case, the topics of AutoExist and Cross Table Filtering were understood by like 3 or 4 people about six years ago… so pretty good chance you didn’t know about these topics and if you did… you probably didn’t understand the posts anyway.  I know I didn’t. For that matter, I still don’t, but do I seem like the kind of guy that is bothered by such trivialities?  AutoExist, Cross Table Filtering, and You


So, look. AutoExist has already been written about. Quite a lot. Feel free to comment with “Dude, we get it… ” and *I* promise not to write about it a 3rd time! Folks that have written about this:

All of them have a slightly different take on the concept and all are worth reading, imo.  That said… this is not something you are going to run into every week. AutoExist, Cross Table Filtering, and You

My claim is that if you work long enough with DAX, you will run into this… likely as a performance issue, but possibly as “weird results” from your measures, and then… you will be glad you at least heard about this concept.  So, store it away.  And if nothing else, it is interesting…

Let’s start by creating a simple model (at right) and one simple measure:

[Total Ticket Sales] := SUM(Sports[Ticket Sales])

AutoExist, Cross Table Filtering, and YouWe have a few professional sports team, using a Cities lookup table.  I have totally totally invented some bogus Ticket Sales data.  We build a pivot table and all looks about like what we would expect (the upper pivot table, at left).

Other than some unlikely data (in the States, there is no scenario where Soccer/Futbol is going to have more ticket sales than American Football.  We really really like our football) – proof the data is fake. AutoExist, Cross Table Filtering, and You

Okay, that was nice I guess – next step is to define a weird and mostly useless measure:

[Greeting] := “Hello”

First, let’s just recognize this is indeed a valid measure.  Measures can return text!  And hey, they don’t HAVE to do anything interesting/useful… like sum or average a column… they can just return a simple value like “Hello” or 1.23 if you want.

You probably already cheated and looked at the next pivot table, but just in case you have not… think for a moment about what you “expect” to see.  My first thought would be that the pivot table would look just like the Total Ticket Sales version… but replace all numbers with “Hello”.  Of course, I wouldn’t call it a “first” thought if that were correct.

Indeed the pivot table looks strangely “long”.  Though, at least it is friendly – look at how often it is telling us “Hello” ! AutoExist, Cross Table Filtering, and You  Where the first pivot correctly shows only Dallas teams (Cowboys and FC Dallas), the second pivot table is showing teams from other cities.  Dallas Earthquakes is not a thing!!  AutoExist, Cross Table Filtering, and You

Two questions:
1) Why does the first pivot table NOT show Earthquakes under Dallas like the second pivot table?
2) Why are some of the cells in the second pivot table BLANK?

You want the truth, you can’t handle the truth, blah, blah… a few good men.

On 1) the thing to realize is that Dallas is being evaluated against the Earthquakes – and every other sporting team.  It’s just that by default, Excel removes rows where every value is BLANK.  Our only value being [Total Ticket Sales] which doesn’t exist (eg, is BLANK) for Dallas Earthquakes… those rows are simple tossed out, showing a smaller pivot table.

On 2) is now a good time to admit that the results surprised me too? AutoExist, Cross Table Filtering, and You

Somehow the pivot table (or… something…) knows that the Cowboys are a football team, and the Earthquakes are a soccer team, so it doesn’t bother evaluating Dallas Cowboys… the soccer team.  And it doesn’t evaluate FC Dallas the football team.  But… well, how does it know?

Btw, what is the most impressive invention of modern times?  Computers? Airplanes?  No… it’s the thermos.  It keeps hot stuff hot.  It keeps cool stuff cool.  But… how does it know!??  I’m the worst – I’m sorry.

So.  Why is it smart enough to know the Cowboys don’t play soccer, but it is not smart enough to know the Earthquakes don’t play in Dallas?

The answer is simple:  the cities are in a different table.

There is magic down in the bowels of the DAX engine that knows which combinations of column values existautomatically.  exist. automatically. autoexist. AUTOEXIST!

The “autoexist” logic magically removes combinations that don’t exist, but… only for columns in the same table.  If the columns are in different tables, all bets are off… it is going to try to evaluate every combination.  And… in the vast majority of cases you do not care.  If everything is working and performance is good – awesome!  Grab yourself the beverage of your choice, congratulate yourself on a job well done, and move along w/ your life.

However, given the knowledge of how columns from multiple tables interact… you can see how building a pivot table with lots of columns from different tables could create some performance problems.  Grab a column from a geography table of 2000 rows, one from 4000 customer table and one from 500 rows of products, and you are evaluating 2000*4000*500 combinations… and throwing away the vast majority as blanks.  Something like 3 billion combinations?  Zoinks!

The important thing here is simply awareness, but a few points:

  • If possible, avoid “detail” reports – that have lots of columns.  They make the ouchy.
  • If you need detail reports, consider using the trick from Rob’s article linked above.
  • Denormalize tables to avoid snowflake schema… as more tables simply increases the odds of running into this problem.  In our example, if we simply pulled the cities into the sport table… problem solved!  No more Dallas Earthquakes…!
  • Beware that in some interesting situations… we aren’t just talking about a performance issue, but bad results from your measure – be sure to read the post linked above at SQLBI.

Cross Table Filtering

To continue our theme of discussing things that are hard to understand, I have read this post about Cross Table Filtering more times than I can count – and each time I get some new little nugget of info and a good heap of “what in the hell is he even talking about?”

This is a deep discussion about filter context, how it flows between tables, the impact of ALL( ) in both top and non-top level filters, and … well, just go read it.  It’s pretty intense.

How is it that I’m on my 4th year of using DAX about every day, and I still don’t understand everything in the article?  Two words:  Jeffrey. Freakin. Wang.  Jeffery is basically the father of DAX and current engineering manager overseeing the DAX engine.  I heard him speak at our local user group about a year ago and it was amazing how much Cross Table Filtering knowledge he was dumping on us.  I can’t wait to listen to him again.  I wonder when that will be…

Wait, what’s that?  He is speaking at the next Modern Excel / Power BI User Group in Seattle!?  Hurray for me!

Seriously, if you are in the area… this isn’t one to miss.

And btw… if you are in the Seattle area… I would love to meet you!  For almost 4 years, I have been running my independent consultancy at Tiny Lizard.  However, hopefully you guys saw the exciting announcement around the creation of Power Pivot Pro – Northwest, or as I like to call it P3NW.  Other than providing technical support on projects, my primary role for P3NW is in helping mentor and grow people.  People like yourselves.  I don’t care if you are seasoned or brand new – I’d love to chat about where you are in your data journey.  Drop me a line!  I’m “scott” “at” “powerpivotpro” “dot com”…

This Post Has 12 Comments
  1. Ok, that explains a lot. I’m not very careful about keeping down the number or size of tables I bring into Power Pivot with my queries. For 5+ years I’ve been making DAX cry, so no wonder it has been slow for me.

    BTW, how much are season tickets for the Portland Poopy Pants? Can we call them P3FC? Or would that be infringing on the trademark of P3NW?

  2. It might not be immediate obvious but ALL() with more than one column uses AUTOEXISTS. So do SUMMARIZE() and SUMMARIZECOLUMNS()

    ALL(Sports[team], Sports[sport])
    SUMMARIZE(Sports, Sports[team], Sports[sport])
    SUMMARIZECOLUMNS(Sports[team], Sports[sport])

    all return the combinations of the team and sport column values that exist in the ‘Sports’ table. That would be all the cells that have your ‘Hello’ greeting in the pivot table. That is opposed to CROSSJOIN in DAX that returns all the combinations of the column values in question. (9 x 2 cells instead of the 9 that actually exist)

    SUMMARIZECOLUMNS is a pivot table generator and incorporates both autoexists, blank row removal and in addition to this options for subtotal and grandtotal sums.

        1. @Sam could you please post a question on the forum of Matt’s website with the title ‘so what is VALUES(facttbl) doing Oxenskiold?’ It’s not possible for me to answer your question because of a limit to the number of indentations on the comments area following Matt’s blog post. If you post on the forum I’ll try to answer your question during the coming weekend.

  3. I love playing with DAX, so when I saw this post, I immediately wanted to try something to test a theory. With clear pictures, it was easy to recreate the DAX model with the Cities table on the one side and the Sports table on the many side. I first added a calculated column to the Cities table called Greet, that is Cities[Greet] = “Hello”. This was important so I could wrap a measure reflecting “Hello” in CALCULATE to later adjust the filter context. Using the newly created calculated column, I placed the following measure – NewGreet:=VALUES(Cities[Greet]) onto the pivot table with City and Team on rows and Sport on columns. The resulting pivot table looked exactly like the Hello pivot table in this blog post, with every team reflected under City. I wrapped the above measure in CALCULATE and noted the same results.

    Now the fun part (I’m starting to geek out) – I tried a new measure using the Sports table as a filter.

    NewGreetTableFilter:=CALCULATE(VALUES(Cities[Greet]), Sports)

    Using the Sports table as a filter within CALCULATE returns the expanded version of the Sports table, effectively filtering the Cities table. No more Dallas Earthquakes.

  4. Hello Scott:
    I read your very interesting post, got curious and then I wanted to see what was going on “under the hood”, so I did the following:
    1. Replicate your tables and measures creating a power pivot model in an excel file.
    2. Wrote two new additional measures:
    NumberOne:= 1
    3. Import the excel file in Power BI Desktop that has my recently created model with the two tables and four measures.
    4. Launch Dax Studio, connect to the Power BI instance that is open with the imported excel file.
    4. Copy the analysis services server label from the bottom right corner on the Dax Studio screen: localhost:xxxxx
    5. Open a new excel file and on Data/From Other Sources/From Analysis Services connect to the Analysis Services Server that is running on the Power BI Desktop instance using localhost:xxxxx
    6. Create 4 pivot tables and dropping on Values of each PivotTable one of the fourth measures and the same dimensions on rows and on colums as the pivot tables you created: “Total Ticket Sales”, “Greetings”, “NumberOne” and “NotNumberOne”.
    6. Download and install the excel addin “OLAP Pivot Table Extensions”.
    7. Rigth click on each of the four pivot tables, select “Olap Pivot Table Extensions” / MDX and copy the MDX query.
    5. Open SQL Server Management Studio, connect to Analysis Services using the Server name localhost:xxxxx, select MDX query to open a new window, paste and run each query.
    6. If you don’t have SSMS, download and install MDX Studio from , connect to the local server using localhost:xxxxx, paste the four MDX queries and run each one.
    7. When you run the query with the “NumberOne” measure you get the same results that we got using “Greetings”, but instead we get 1 on the Values area.
    When we run the query with the “NotNumberOne” measure we get the same rows as in the first pivot table with the “Total Ticket Sales” measure.
    8. Why I think we get different results:
    a. MDX executes the same CrossJoin of the two tables on every case.
    b. What NonEmpty does is: “Returns the set of tuples (rows) that are not empty from a specified set (dimensions), based on the cross product of the specified set with a second set.” according to microsoft documentation.
    c.Because the measures “Greetings” and “NumberOne” don’t have data from any of the two tables, MDX returns only the CrossJoin without any filtering and numeric data comming from any of the two tables.
    d. If you run the following MDX query with the WHERE line commented, you will get the exact same table when you use the “NumberOne” measure on the pivot table.
    NON EMPTY Hierarchize(DrilldownMember(CrossJoin({[Cities].[City].[All],[Cities].[City].[City].AllMembers},
    {([Sports].[Team].[All])}), [Cities].[City].[City].AllMembers, [Sports].[Team]))
    FROM [Model]
    –WHERE ([Measures].[Total Ticket Sales]) –Row commented

  5. Good to know that what one writes can come as a complete, I did not know that…as it appears on your blog post 🙂

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