SSAS Tabular 2019, Calculation Groups And Migration From SSAS Multidimensional

With the release of CTP 2.3 of SQL Server 2019 today there was big news for Analysis Services Tabular developers: Calculation Groups. You can read all about them in detail in this blog post:

In my opinion this is the most important new feature in DAX since… well, forever. It allows you to create a new type of calculation – which in most cases will be a time intelligence like a year-to-date or a previous period growth – that can be applied to multiple measures; basically the same thing that we have been doing in SSAS Multidimensional for years with the time utility/shell/date tool dimension technique. It’s certainly going to solve a lot of problems for a lot of SSAS Tabular implementations, many of which have hundreds or even thousands of measures for every combination of base measure and calculation type needed.

I’m not going to repeat any of the detailed technical information in the blog post here, though. Instead the point I want to make is that this is very big news for SSAS Multidimensional users too. In the past couple of years many people who have existing SSAS Multidimensional implementations have thought about migrating to SSAS Tabular so they can take advantage of its new features or move to the cloud, and indeed many of them have already migrated successfully. However, up to now, the biggest blocker for those wanting to migrate from Multidimensional to Tabular has been the fact that some complex calculations that can be expressed in MDX cannot be recreated (or recreated easily and efficiently) in DAX, because DAX has not had an equivalent of calculated members not on the Measures dimension or the MDX SCOPE statement.

Calculation groups do not remove this roadblock completely, but they do remove the roadblock for a large group of existing SSAS Multidimensional users whose only complex calculation requirement is a time utility/shell/date tool dimension. As a result these SSAS Multidimensional users will now be able to migrate to SSAS Tabular 2019, Azure Analysis Services or Power BI if they want to. Only those people who have more exotic uses for calculated members not on the Measures dimension (which are not very common at all) and those who use SCOPE statements (a larger group – many people working with financial data use SCOPE statements heavily) will find that Multidimensional is still the correct platform for them.

The Is Nullable Column Property In Power BI

The new Relationships view (or Modeling view – it seems to have two names) in Power BI Desktop that has been in preview since November 2018 not only makes it easier to work with complex models and set properties more easily, it also exposes a brand new property on a column: the “Is nullable” property. It’s visible at the bottom of the new Properties pane when you click on a column:


I was, naturally, curious about what it did and I couldn’t find any documentation so I did a bit of investigation of my own and asked a few people at Microsoft.

It turns out that it is primarily intended for validation purposes, so that if you know a column should never contain a null value and then, at a later date, a null value does appear in that column then you’ll get the following error when you try to refresh a table in Import mode:


Column ‘MyColumn’ in Table ‘TestTable’ contains blank values and this is not allowed for columns on the one side of a many-to-one relationship or for columns that are used as the primary key of a table

The only other use I have found for it is when it is used in combination with the DAX CombineValues() function in DirectQuery mode. In his excellent article on this subject, Marco notes that the SQL generated when you use CombineValues() to concatenate values from multiple columns and create a calculated column that can be used in a relationship includes a check for null values in those columns. I can confirm that if you turn off the ‘is nullable’ property on all the columns used by CombineValues(), there are no checks for null values in the SQL queries.

There may be a few other things that it does, but I’ve been told they are likely to be there to ensure correctness rather than performance optimisations. Nonetheless if you do find other scenarios where it has an effect please let me know in a comment.

[Thanks to Akshai Mirchandani and Jeffrey Wang for providing much of the information used in this post]

Complex Filter Conditions In Power BI Reports Using Visual-Level Filters And Measures

While Power BI has a lot of great functionality for filtering the data that appears in a visual using visual-level filters there are going to be situations where you need more control over how filtering works, and in this blog post I’m going to show you how you can use visual-level filters and measures in combination to achieve this. The example I show here may be quite specific but I think the general technique has a lot of other interesting applications.

Let’s say you have a table containing customer purchases, with potentially more than one purchase per customer, and want to display a table in Power BI showing only the most recent purchase for each customer relative to a given date.

Using data from the Adventure Works DW database, here’s the Internet Sales table from my Power BI example dataset that contains the customer purchases. Each purchase has a Sales Order Number and a Sales Order Line Number, and there may be multiple line items in a single sales order.


Here’s some data from this table, filtered down to show the purchases of one customer: Abby Subram.


[Note: all dates shown are in dd/mm/yyyy format]

The requirement is that the report user should be able to select a date in a slicer and show the details for the most recent sales order that occurs before the selected date. So, for example, if the user selects 1/1/2003 then no rows should appear; if 16/6/2003 is selected then only SO50934 should appear; if 30/9/2003 is selected only the four rows relating to SO54975 (and not SO50934) should appear; if 28/6/2004 is selected only the four rows relating to SO73938 should appear, and so on.

The first step is to create a create a date table that contains the ‘as of’ dates the user can select from, using the disconnected slicer technique: basically a date table that has no relationship at all to the Internet Sales table shown above.


Next comes the tricky part. Visual-level filters can be used to filter the contents of visuals in a Power BI report based on the value of a measure, and that measure does not need to be displayed in a report. What you therefore have to do is create a measure that will return the value 1 for data that should be displayed (in this case, the most recent purchase order for the customer) and blank otherwise, then apply a visual-level filter using this measure and filter on this measure equalling 1. Marco and Alberto have a great article going into the details of how measures behave when they are used in visual-level filters here that I recommend you read before carrying on.

For this particular scenario here’s my measure:

Is Latest SO =
VAR CurrentAsOfDate =
    SELECTEDVALUE ( 'As Of Date'[DateKey] )
VAR CurrentSODate =
    SELECTEDVALUE ( 'Internet Sales'[OrderDateKey] )
VAR CurrentCustomer =
    SELECTEDVALUE ( 'Internet Sales'[CustomerKey] )
VAR SameCustomerSOs =
    FILTER (
        ALL ( 'Internet Sales'[CustomerKey], 'Internet Sales'[OrderDateKey] ),
        'Internet Sales'[CustomerKey] = CurrentCustomer
&& 'Internet Sales'[OrderDateKey] <= CurrentAsOfDate
            && 'Internet Sales'[OrderDateKey] > CurrentSODate
VAR NoLaterSOs =
    IF ( COUNTROWS ( SameCustomerSOs ) > 0, BLANK (), 1 )
    IF ( CurrentSODate <= CurrentAsOfDate, NoLaterSOs )

The logic here is:

  • Assume that this measure will be used to filter a table visual in a Power BI report, where each row in the table visual displays data from a single row in the Internet Sales table in the dataset.
  • For each row in the Internet Sales table, find the current values for OrderDateKey and CustomerKey, as well as the date selected in the ‘as of date’ slicer, and store them in variables
  • Create a table of all possible combinations of CustomerKey and OrderDateKey ignoring the current filter context, then filter it so you get all the sales orders for the current customer and where the order date is greater than the date of the current sales order and less than or equal to the selected ‘as of’ date. This is stored in the SameCustomerSOs variable.
  • If there are rows in the SameCustomerSOs table then the current row in Internet Sales should not be displayed because it is not the most recent sales order as of the selected date; if SameCustomerSOs is empty then the current row should be displayed, because there are not later sales orders for the current customer before the selected as of date.
  • Finally, return 1 if SameCustomerSOs is blank and if the current row in Internet Sales has an order date before the selected as of date.

You can then create a table (or some other visual) in your Power BI report and, in the visual-level filters for that table, drag in the measure and apply a filter on the value of the measure equalling 1:


Here’s the result, showing the data for Abby Subram:



The one thing you do need to be careful of when using this technique is that the DAX you use in your measure must be as efficient as possible: inefficient DAX, complex filters and large data volumes will make your report unusably slow. I’m sure the DAX used in my measure above could be tuned to perform better (it currently takes around half a second to filter the full 60,000 rows in the Internet Sales table; Power BI only queries for and displays 500 rows at a time, which makes things faster) but the real point I wanted to make here is that using measures and visual-level filters in this way allows you to apply almost any complex filter condition you want to the data displayed in a visual.

You can download the sample pbix file for this post here.

Creating Sparklines And Small Multiples In Power BI Using The Google Image Charts API

The excellent work that David Eldersveld has been doing recently on using SVG images in Power BI has generated a lot of interest; now that the August 2018 release of Power BI Desktop allows you to set data categories on measures – and specifically the Image URL data category – David has been able to apply his work to the creation of sparklines and small multiples in Power BI. He’s already published an example in the Quick Measures gallery here; you might also want to check out this blog post and quick measure by Tom Martens too.

All this got me thinking: what if there was a simple web service that could generate charts, one that didn’t need any authentication, could work with simple GET requests and was free to use? Well guess what, there is one created by Google: the Google Image Charts API. It turns out that it makes it super easy to create sparklines and small multiples in Power BI with very little DAX needed.

Here are two examples of what’s possible with some simple DAX in a measure:



Both examples use data from a table called Sales with the following contents:


The first example, the Sales Sparkline measure, has the following definition:

Sales Sparkline =
var BaseURL =
var ChartType =
var ChartScale =
var ChartSize =
var SeriesColour =
var ChartData =
    "&chd=t:" &
    CONCATENATEX(Sales, Sales[Sales], ",", Sales[Month])
        BaseURL & ChartType & ChartScale &
	ChartSize & SeriesColour & ChartData

All I’m doing is building up the URL to call the API from the parameters listed here. The second measure is basically the same as the first, but the chart type is driven by a selection in a slicer. It’s not bullet-proof code but it shows how easy it is to use the API.

You can see the report live here, and download the sample .pbix file here.

I’m not going to pretend that I’m a data visualisation expert and I know these examples are very basic; however, I think there are a lot of possibilities here for someone with better data visualisation skills than me.

Now for the bad news: the Google Image Charts API has been deprecated since 2012! Here’s the statement on the Google website:

While the dynamic and interactive Google Charts are actively maintained, we officially deprecated the static Google Image Charts way back in 2012. This gives us the right to turn it off without notice, although we have no plans to do so.

Also, there’s a fair usage policy too:

There’s no limit to the number of calls per day you can make to the Google Chart API. However, we reserve the right to block any use that we regard as abusive.

As a result I would not be confident about using it in production. If thousands of Power BI users started using the API it might prompt Google to block traffic from Power BI or turn off the API completely!

I do think Microsoft really needs to address the lack of native support for sparklines and small multiples in Power BI – the fact we still don’t have them is frankly embarrassing (even Power View had them, for crying out loud). Although one solution could be a DAX function that returned a chart image – basically taking David’s work and wrapping it up in a more user-friendly form – another approach would be for Microsoft to build on the Report Page Tooltips functionality. Since we can already design mini reports and make them appear as tooltips, why not make it possible for the same mini reports to appear in a cell in a table or matrix? You could then have measures that returned the name of a report page as a text value, and a new data category for them that tells Power BI to display the contents of the report page in the cell instead of the text. That way you could create small multiples from any visual or set of visuals.

UPDATE: thanks also to Tom Martens for reminding me to point out that this technique involves sending data to a third party, something which might cause legal problems for you.

More Details On The RowsetSerializationLimit Server Property For Analysis Services

The recent blog post on the Analysis Services team blog about new memory options contained information about some very interesting functionality that has just been added. The first of the new features I decided to try was the RowsetSerializationLimit server property, which restricts the number of rows returned by a query and which is intended to stop rogue users running queries that return very large amounts of data. It certainly works but there are two things to point out about it that aren’t immediately obvious.

First of all, an example of it in action. In Azure Analysis Services, using a database that contains one very simple table with one column and 5000 rows, the following DAX query returns all 5000 rows from the table:



To change the RowsetSerializationLimit server property, open SQL Server Management Studio, right-click on your server name in the Object Explorer pane and select Properties. Then go to the General page, check the Show Advanced (All) Properties box, and you’ll see the property listed under OLAP\Query\RowsetSerializationLimit:


The default value is –1, meaning no limit is placed on the number of rows returned by a rowset. Changing it to 100 and then running the above query results in the following error:


Executing the query …
Obtained object of type: Microsoft.AnalysisServices.AdomdClient.AdomdDataReader
The maximum number of rows (100) was reached.

A query that returns less than 99 rows, for example


…is ok though.

The important thing to understand is that although this applies to both DAX and MDX queries, it only applies to queries that are returned as rowsets (a tabular format) and not as cellsets (a multidimensional format). Most client tools that generate MDX, including Excel, use cellsets so this property will not apply to them unfortunately.

For example, the following MDX query run from a SQL Server Management Studio MDX query window (which also returns a cellset) on the same database runs ok and returns 5000 rows:

{[Measures].[Number Of Rows]} ON 0,
[MyTable].[Column1].[Column1].MEMBERS ON 1



However the same query run from Power BI Desktop to import data from the same database:


…runs, but returns only 100 rows and then an error:


Something else to point out is that this applies to all queries that return rowsets, even Discover requests and DMV queries. As a result, setting this property to a very low value can cause problems in tools like SQL Server Management Studio: for example if you set the property to 10 and you had 11 tables in a database, you would see an error when you tried to expand the Tables node of a database in the Object Explorer pane!

Even though this property only applies to rowsets I think it’s still going to be very useful, especially in scenarios where Power BI Desktop users are importing vast amounts of data from Analysis Services and causing major problems on the server.

Line Breaks And Auto-Indent In The Power BI DAX Formula Bar

The other day I discovered something new (at least to me) while writing the DAX for a measure in Power BI Desktop: when you insert a new line in your DAX expression using SHIFT-ENTER it also auto-indents the code. I asked a few people if this was new because I was sure I hadn’t seen it before, even though I always put line breaks in my code; of course Marco had and said he thought it had been around for a while. Anyway, Marco then commented that most people didn’t know you could even put line breaks in DAX and I thought to myself I should probably write a short blog post about all this, because of course line breaks and indentation make your code much more readable.

Here’s what it looks like to write a DAX measure in Power BI Desktop using line breaks and auto-indent:


To summarise:

  • Press the SHIFT and ENTER keys on the keyboard simultaneously to insert a line break in your DAX code when you are working in the Power BI DAX formula bar
  • Do this after the opening bracket of each function and after the comma of each function argument and the auto-indent will kick in
  • Bonus tip: while typing DAX, if the dropdown list has the function, table, column or measure selected that you want to use, just hit the TAB key to auto-complete. If the dropdown list hasn’t selected what you are looking for and you don’t want to keep typing, use the up and down arrow keys on the keyboard to move up and down the list until you have selected what you want.

Of course there’s always Marco and Alberto’s excellent DAX Formatter service to format your DAX code but this is a lot more convenient.

A New Approach To Handling SSRS Multi-Valued Parameters in DAX Queries

Back in 2012 I wrote a blog post explaining how to handle multi-value parameters in DAX queries in Reporting Services reports. The approach I took back then was basically:

  1. Generate a string containing a pipe-delimited list of all the parameter values that the user has selected (I did this in DAX too, but to be honest it’s better to use the SSRS Join function to do this outside the DAX query)
  2. Use the DAX PathContains() function in a Filter() to check whether the value on the current row of the table being filtered appears in the pipe-delimited list

Here’s a deliberately simplified example of how this works based on Adventure Works DW data. The following query filters the FactInternetSales fact table and returns the rows for the Sales Order Numbers that are present in the OrderList variable:

VAR OrderList = "SO43713|SO43758|SO43784|SO43821"
    FILTER (
        PATHCONTAINS(OrderList, FactInternetSales[SalesOrderNumber])

The trouble with this approach is that is that it can be very slow. Running a trace in DAX Studio for the query above reveals the problem:


The presence of CallbackDataID shows that the Storage Engine is calling the Formula Engine to handle the use of PathContains() in the filter, and this is often a cause of poor query performance. However back when I wrote the post the only alternative was, as Chris Koester points out here, to dynamically generate the entire DAX query as an SSRS expression and that is very painful to do.

The good news is that recent changes in DAX mean that there is another way to tackle this problem that can give much better performance. Here’s an example of this new approach:

VAR OrderList = "SO43713|SO43758|SO43784|SO43821"
VAR OrderCount =
    PATHLENGTH ( OrderList )
VAR NumberTable =
    GENERATESERIES ( 1, OrderCount, 1 )
VAR OrderTable =
        VAR CurrentKey = [Value]
            ROW ( "Key", PATHITEM ( OrderList, CurrentKey ) )
VAR GetKeyColumn =
    SELECTCOLUMNS ( OrderTable, "Key", [Key] )
VAR FilterTable =
    TREATAS ( GetKeyColumn, FactInternetSales[SalesOrderNumber] )
    CALCULATETABLE ( FactInternetSales, FilterTable )

Broken down variable by variable, here’s how it works:

  1. OrderList is the pipe-delimited list of key values passed from SSRS
  2. OrderCount uses the PathLength() DAX function to find the number of parameter values in this list
  3. NumberTable uses the GenerateSeries() function to create a table of numbers with one row for each number between 1 and the number of parameter values in the list
  4. OrderTable uses the trick Marco describes here to iterate over NumberTable and, for each row, uses the PathItem() function to return one parameter value from the list for each row in the able
  5. GetKeyColumn uses the SelectColumns() DAX function to only return the column from OrderTable that contains the parameter values
  6. FilterTable uses the TreatAs() DAX function to take the table of values returned by GetKeyColumn and treat them as values in the FactInternetSales[SalesOrderNumber] column
  7. Finally, the query returns the contents of the FactInternetSales table filtered by the values in FilterTable using the CalculateTable() DAX function

There’s a lot of extra code here and in some cases you may find that performance with smaller data volumes is worse as a result, but in this particular case the new approach is twice as fast at the old one. There’s certainly no CallBackDataID:


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