Data Privacy Settings In Power BI/Power Query, Part 1: Performance Implications

One of the most confusing, under-documented and widely-misunderstood features of Power BI and Power Query (or Excel “Get & Transform” or whatever you want to call it) are the data privacy settings. I get caught out by them all the time, so I thought it would be a good idea to write a series of blog posts demonstrating how they work and what effect they have in different scenarios using example M queries.

Before carrying on, I suggest you read the official Microsoft documentation on the subject here:
It gives you a good grounding in what the different data privacy levels are and where you can set them in the Power BI UI. The same options are available in Excel with Power Query/Get & Transform.

In this first post I’m going to look at what the performance implications of different privacy levels can be. Let’s say you have two data sources. First, an Excel workbook with a single table in that contains the name of a day of the week:


The second is the DimDate table in the Adventure Works DW SQL Server sample database:


Here’s an M query called FilterDay that returns the day name from the table in the Excel workbook:

    Source = Excel.Workbook(
		, null, true),
    FilterDay_Table = Source{[Item="FilterDay",Kind="Table"]}[Data],
    ChangedType = 
		{{"Parameter", type text}}),
    Output = ChangedType{0}[#"Parameter"]


Here’s an M query called DimDate that filters data from the DimDate table in the Adventure Works DW database, returning only the rows where the EnglishDayNameOfWeek column matches the value returned by the FilterDay query above.

    Source = Sql.Databases("localhost"),
    DB = Source{[Name="Adventure Works DW"]}[Data],
    dbo_DimDate = DB{[Schema="dbo",Item="DimDate"]}[Data],
    RemovedColumns = Table.SelectColumns(dbo_DimDate,
		{"DateKey", "EnglishDayNameOfWeek"}),
    FilteredRows = Table.SelectRows(RemovedColumns, 
		each ([EnglishDayNameOfWeek] = FilterDay))

The first time you run this second query you’ll be prompted to enter credentials to connect to SQL Server, and then (assuming you haven’t set any of the options that get Power BI to ignore privacy levels) you’ll see the “Information is required about data privacy” prompt:


Clicking “Continue” allows you to set a privacy level for the SQL Server database (but, interestingly, not for the Excel workbook):


You can choose any of the three privacy levels for the SQL Server database and the query will still run:


In fact it is the the privacy level for the Excel workbook that is important here and I’m not really sure why Power BI doesn’t prompt you to set that too. At this point the workbook has a privacy level set to None, which is the default for newly-created data sources (I’m still researching what this level actually means and hope to cover it in a future blog post):


If you right-click on the FilteredRows step of the query that returns data from the DimDate table in SQL Server and select “View Native Query” (see here for more details on this feature) to see the SQL query generated in the background for this step like so:


You’ll see that query folding is taking place for this step and the filter on the EnglishDayNameOfWeek column is taking place in the SQL query:


select [_].[DateKey],
    select [DateKey],
    from [dbo].[DimDate] as [$Table]
) as [_]
where [_].[EnglishDayNameOfWeek] = 'Friday'

Query folding is almost always a good thing for the performance of a query. For more details on what query folding is, see here.

Nothing changes if you set the privacy level of the Excel workbook to Public. If, however, you set the privacy level for the Excel workbook to Private like so:


…even though the DimDate query still works, query folding does not take place for the Filtered Rows step. The View Native Query right-click option is greyed out, and Profiler shows that the following SQL is executed when the query is refreshed:

select [$Ordered].[DateKey],
    select [DateKey],
    from [dbo].[DimDate] as [$Table]
) as [$Ordered]
order by [$Ordered].[DateKey]

Note that there is no WHERE clause in this query and that the whole of the DimDate table is returned from SQL Server.

Data from a data source that has a privacy level of Private can never be sent to another data source. That is exactly what needs to happen for query folding to take place though: a value from the Excel workbook – the text “Friday” – needs to be embedded in the WHERE clause of the SQL query sent to SQL Server in order for filtering to happen inside the database.  The risk with query folding is that a DBA could monitor the queries that are being run on SQL Server, look at the WHERE clauses, and see data from your Excel workbook. That’s maybe not a problem with day names, but potentially an issue if you were working with more sensitive data like customer names or addresses. Therefore, with the Excel workbook’s privacy level set to Private, the whole of the DimDate table is downloaded into the M engine and the filtering has to take place there to maintain the privacy of the data in Excel. The query still runs but it will probably be a lot slower than it would have been had query folding taken place. With the privacy level of the Excel workbook set to Public, on the other hand, it is ok to send data from Excel to SQL Server so query folding does take place.

To sum up, in this post I have shown how different data privacy settings can affect the performance of a query by determining whether query folding takes place or not. In part 2 of this series I will show how different data privacy settings can determine whether a query executes at all.

Power BI Custom Data Connectors

Amid all the other announcements at Microsoft Build today came one I’ve been waiting for a long time: we can now build our own custom data connectors for Power BI. Here’s the announcement on the Power BI blog:

…and here are the docs:

Although it might not sound that exciting, I think this is one of the biggest things that’s happened to Power BI in a long time. Microsoft’s most successful products are always the ones that are platforms for its partners to build their own solutions on top of, and Power BI is no exception. Custom visuals have made an important contribution to the success of Power BI as a whole, allowing third parties to build visuals that Microsoft either didn’t have the resources to build itself or didn’t realise people wanted. Similarly, custom data connectors will only increase the already impressive number of data sources that Power BI can connect to. My guess is that a lot of SaaS and data providers will want to build their own custom data connectors, and that the number of custom data connectors available is going to explode. Why go to all the trouble of trying to build BI and reporting features into your product when it’s quicker and easier to send the data to Power BI and your users get a much better experience as a result?

This doesn’t just affect Power BI though. Since the new Get & Transform functionality for loading data into Excel (see here) is based on the same Power Query technology that is used by Power BI, these custom data connectors will at some point work with Excel too. Although it will be a while before most Excel users upgrade to a version that supports custom data connectors, when that happens the sheer size of the Excel market means that there will be even more demand for custom connectors and this will in turn strengthen the Power BI ecosystem. Don’t forget that Analysis Services Tabular 2017 also now uses Power Query for loading data, so this will benefit corporate BI users too.

From a purely technical point of view today’s announcement included some welcome new capabilities for M coders like me. These include Visual Studio integration (intellisense for M!) and the ability to connect to web services and use OAuth for authentication, something that wasn’t really possible before. It’s also interesting that you can create custom data connectors that enable DirectQuery connections from Power BI, and that you aren’t limited to importing data.

I can’t wait to start using all these features and I’m sure they will be the subject of many future blog posts. Also, if you’re looking to hire someone with a good understanding of M to build a custom data connector, I’m your guy. You can find my contact details at

DAX Functions, DirectQuery And Unrestricted Measures

If you use Power BI to connect to a data source such as SQL Server in DirectQuery mode, you’ll find that by default you cannot use the complete range of DAX functions inside your calculations. For example, here’s a screenshot from Power BI Desktop in DirectQuery mode with a measure where I’m trying to use the RankX() function:


Whenever you get an error message like “Function ‘RANKX’ is not supported in DirectQuery mode” you know you’re trying to use a DAX function that isn’t allowed by default in DirectQuery mode.

Why does this limitation exist? Well, as the documentation points out here:

…this is done for performance reasons. In Power BI, every time you interact with a report (by opening it, clicking on a slicer etc) a series of DAX queries is sent back to the Data Model to get the data needed by the report. In DirectQuery mode, all of these DAX queries – including any calculations – are translated into queries that are sent back to the data source. If that data source is SQL Server, then Power BI generates SQL queries. Not everything that you can do in a DAX calculation can be translated back into efficient SQL, so to prevent you from accidentally building calculations that will perform badly in DirectQuery mode Power BI tries to prevent you from using any DAX functions that it thinks it can’t generate efficient SQL for.

You can remove this restriction by clicking File/Options and Settings/Options to open the Options dialog and then going to the DirectQuery pane and selecting “Allow unrestricted measures in DirectQuery mode”:


There is no documented list of functions that work in DirectQuery mode (probably because it might change with each release) but there is a way to get one. Using SQL Server Management Studio it is possible to connect to Power BI Desktop and run DMV queries against the Power BI Data Model. If you query the MDSCHEMA_FUNCTIONS DMV like so:


You’ll see that it returns a list of all of the DAX and MDX functions understood by Power BI; the last column, called DIRECTQUERY_PUSHABLE contains information used by Power BI to decide whether a calculation is available in DirectQuery mode or not:


[At the time of writing this query doesn’t work in DAX Studio for some reason, I think because of the data returned in the PARAMETERINFO column, but if you try to restrict the rows or columns returned by the DMV in any way the DIRECTQUERY_PUSHABLE column only returns empty values. This is why I say you have to use SQL Server Management Studio]

For the DAX functions in the list, if DIRECTQUERY_PUSHABLE is 0 then it is not available in DirectQuery mode by default. If it returns 1 then it is available in measures but not calculated columns; if it returns 3 it is available in both measures and calculated columns (I guess 2 means that it is only available in calculated columns, but there are no functions with this property value right now). However this is not a definitive guide to whether a function is actually ‘pushable’ or not: some functions could be pushable in some scenarios, or with certain combinations of parameters, and not others and the UI may perform additional checks to decide whether a function can be used.

Thanks to Jeffrey Wang and Marius Dumitru for answering my questions about this.

By the way, if you’re reading this, you might also be interested in this post I wrote a while back on the functionality available in the Query Editor in DirectQuery mode:

UPDATE – as Marco points out in his comments below, there *is* documentation on which functions are supported in DirectQuery mode now. I just didn’t know about it…

Using Text.BetweenDelimiters() To Extract URLs From A Web Page In Power BI/Power Query M

The Add Column By Example functionality that appeared in the April 2017 release of Power BI Desktop is, I have to say, very cool: I’ve used it quite a bit in the last few weeks and it really does work well. One thing I noticed while using it is that three new functions have been added to the M language to support this feature:

  • Text.BetweenDelimiters() which takes a text value and extracts the part that appears between two given delimiter values
  • Text.BeforeDelimiter() which takes a text value and extracts the part that appears before a given delimiter value
  • Text.AfterDelimiter() which takes a text value and extracts the part that appears after a given delimiter value

The functions themselves are quite straightforward and the online documentation has some good examples of how to use them. To save you the click here’s an extra example – the expression:

Text.BetweenDelimiters("Hello *world!??", "*", "!")

…returns the text “world”:


As it happens last week I received an email from a reader who wanted to know if it was possible to extract all the links from the href attributes in the source of a web page using M, and I realised that Text.BetweenDelimiters() would be very useful for doing this. I wrote the following M function to demonstrate:

(SourceURL as text, AttributeDelimiter as text) =>
	//Get HTML source
    Source = Text.FromBinary(Web.Contents(SourceURL)),
	//Function to find each link
    GetLink = (Counter as number) =>
                        CurrentLink = 
			"href=" & AttributeDelimiter, 
                        if CurrentLink="" 
	//Call function
    Output = GetLink(0)

A few things to note:

  • I’m using a combination of Text.FromBinary() and Web.Contents() to get the HTML source for the web page whose links we’re extracting
  • Since HTML allows the use of single and double quotes for attributes, I’ve added a parameter to my function called AttributeDelimiter to allow either to be passed in
  • Text.BetweenDelimiters only extracts one piece of text at a time, but you can specify which occurrence of the start delimiter it uses. I therefore used recursion to extract the contents of every href attribute in the HTML: I declare a function called GetLink, and from within that function I can make a recursive call by putting an @ before the function name as in line 22 above. It would probably be better to use List.Generate() instead of recursion here though.

Assuming the query that returns this function is called GetAllLinks


…then it can be called in a new query like so:


One other thing to point out is how, in order to pass a double quote character to the function as text, since text has itself to be enclosed in double quotes I need to use four double quotes: “”””

The output of this query is a list containing all of the links from the href attributes on the page that are enclosed in double quotes:


I guess this could be taken even further to create a function that crawls a series of web pages and returns the links in all of them, then uses the Force Directed Graph custom visual or better still NodeXL in Excel to show which pages link to each other. I’ll leave that to someone else to do though…!

You can download a pbix file with all of the examples in this post here.

Power BI, SSAS Multidimensional And Dynamic Format Strings

If you’re building reports in Power BI against SSAS Multidimensional cubes then you may have encountered situations where the formatting on your measures disappears. For example, take a very simple SSAS Multidimensional cube with a single measure called Sales Amount whose FormatString property is set in SSDT to display values with a £ sign:


When you build a report using the Table visualisation in Power BI Desktop using this measure, the formatted values are displayed correctly:


However, if you add a SCOPE statement to the cube to alter the format string of the measure for certain cells, as in this example which sets the format string for the Sales Amount measure to $ for Bikes:

SCOPE([Measures].[Sales Amount], [Product].[Category].&[1]);

…then you’ll find that Power BI displays no formatting at all for the measure:


What’s more (and this is a bit strange) if you look at the DAX queries that are generated by Power BI to get data from the cube, they now request a new column to get the format string for the measure even though that format string isn’t used. Since it increases the amount of data returned by the query much larger, this extra column can have a negative impact on query performance if you’re bringing back large amounts of data.

There is no way of avoiding this problem at the moment, unfortunately. If you need to display formatted values in Power BI you will have to create a calculated measure that returns the value of your original measure, set the format string property on that calculated measure appropriately, and use that calculated measure in your Power BI reports instead:

SCOPE([Measures].[Sales Amount], [Product].[Category].&[1]);

[Measures].[Sales Amount],


Thanks to Kevin Jourdain for bringing this to my attention and telling me about the workaround, and also to Greg Galloway for confirming the workaround and providing extra details.

The DAX Unichar() Function And How To Use It In Measures For Data Visualisation

A few weeks ago I was asked whether it was possible to display line breaks in text in a Power BI visualisation. It turns out it isn’t possible – at the moment Power BI always strips line breaks out of text when it gets loaded into the Data Model. However while researching this I came across the DAX Unichar() function, which returns the unicode character associated with an integer value – and which also seems to be completely undocumented for some reason, I guess because it’s new (it isn’t in Excel 2016 DAX yet as far as I can see).

It’s very straightforward to use: for example, the DAX expression UNICHAR(65) returns the character A; see here for a list of unicode characters and their associated codes. You can have a lot of fun with this function in Power BI when you use it to return symbols that in turn can be used to represent data, so I thought I would put together a few examples to show you.

Take the following table which contains scores for restaurants in the range of 0 to 5:


The following measure:

Stars = 
REPT(UNICHAR(9733), AVERAGE('Restaurants'[Score])) 
REPT(UNICHAR(9734), 5-AVERAGE('Restaurants'[Score]))

…which uses the Unichar() function to return characters 9733 and 9734, filled and unfilled stars, and the Rept() function to return a string with those characters repeated N times, can be used to create a table like this in Power BI:


[I’m sure I read a blog post somewhere that describes this trick with Rept() but I can’t find it anywhere – if you know the one I’m talking about please leave a link in the comments]

Similarly, with the following source data showing the days that customers made a purchase in a week:


…you can use the following measure, which returns characters 9635 (a square with a black dot inside) and 9634 (an empty black square), in a matrix to visualise this information:

Purchase Indicator = 
	COUNTROWS('Purchase Days')>0, 
& REPT(" ", 5)


Finally, an example using the box drawing unicode block to visualise the following date ranges (nb the dates are in dd/mm/yyyy format). Here’s the source data:


Here’s the measure, which uses characters 9500, 9472 and 9508 to draw bars:

Employment Range = 
VAR OverallMinimumDate = 
		MIN('Employment Dates'[Start Date]), 
		ALLSELECTED('Employment Dates'))
VAR DaysBeforeStart = 
	MAX('Employment Dates'[Start Date]) - 
VAR DaysBetweenStartAndEnd = 
	MAX('Employment Dates'[End Date]) - 
	MAX('Employment Dates'[Start Date])
VAR BarsBetween = 
	REPT(" ", DaysBeforeStart) & 
	UNICHAR(9500) & 
	REPT(UNICHAR(9472), BarsBetween) & 

And here’s the output in a table:


You can download the Power BI .pbix file with these examples in here.

Is this going to revolutionise how you design reports? No of course not, but I think it could be a useful trick in certain scenarios. If you do come up with other creative ways to use unicode characters in your reports I would be interested to see the results!

Daylight Saving Time And Time Zones In M

Quite a few people (Ken Puls, Reza Rad and today Chris Koester) have blogged about how the M date/time zone functions work in Power Query/Power BI, and the related problem of turning a UTC time into the actual time in any given location taking Daylight Saving Time changes into account. I don’t have much to add to what they have written, but I did learn something new about this subject from a customer last week: it is possible to do the conversion in pure M if the time zone you’re converting to is your PC’s own local time zone.

Here’s an example. In 2017 the clocks in the UK went forward at 1am on Sunday March 26th. Given the following table with two times in it, one of which is 1pm on Saturday March 25th and one of which is 1pm on Sunday March 26th:


…if you assume that these times are both UTC, here’s a query that returns the correct equivalent local time for me (because my PC is set to UK time) for both of them:

    //Read data from Excel table
    Source = 
    //Change the type of the column to datetime
    ChangeType1 = 
		{{"Time", type datetime}}),
    //Turn this into a UTC time
    ConvertToUTC = 
		each DateTime.AddZone([Time],0)),
    //Change the data type to datetimezone
    ChangeType2 = 
		{{"UTC", type datetimezone}}),
    //Convert to local PC time
    ConvertToLocal = 
		each DateTimeZone.ToLocal([UTC])),
    //Change type to datetimezone
    ChangeType3 = 
		ConvertToLocal ,
		{{"Local", type datetimezone}})


Here’s a brief explanation of what the query does:

  • First it reads the times from the Excel table and sets the Time column to be datetime data type
  • It then creates a new column called UTC and then takes the values in the Time column and converts them to datetimezone values, using the DateTime.AddZone() function to add a time zone offset of 0 hours, making them UTC times
  • Finally it creates a column called Local and converts the UTC times to my PC’s local time zone using the DateTimeZone.ToLocal() function

As you can see from the output table, the DateTimeZone.ToLocal() function has correctly handled the UK Daylight Saving Time change by converting the UTC time 13:00 on March 25th 2017 to 13:00 UK time, and converting the UTC time 13:00 on March 26th 2017 to 14:00 UK time.

Frustratingly there doesn’t seem to be a way to convert a UTC time to the correct local time in any given time zone in M – the DateTimeZone.SwitchZone() function just adds/removes hours without taking Daylight Saving Time into account, so it’s not really very useful. Apart from all the blog posts linked to at the start of this post, when I posted this question on the Power Query forum I got a very helpful answer from Marcel Beug with a completely different approach to solving the problem here, although it’s still not a straightforward one. If anyone from Microsoft is reading, it would be great to have a new M function to handle this requirement!