Power Query Performance And Expanding Columns

As a footnote to my previous post on storing large images in Power BI, I thought all you M nerds out there would be interested to read about a strange performance quirk I noticed while writing the code for that post.

My original attempt to write an M query to convert a folder full of images to text looked something like this:

    Source = 
        Folder.Files("C:\Users\Chris\Documents\PQ Pics"),
    SplitText = 
        (ImageBinaryText as text) => 
            SplitTextFunction = 
            SplitUpText = 
    #"Added Custom" = 
            each SplitText(
    #"Expanded Pic" = 
            #"Added Custom", 
    #"Expanded Pic"

The approach I took was the one that seemed natural to me at the time:

  1. Use the Folder data source to connect to the folder containing the image files
  2. Define a function called SplitText that takes a long piece of text and splits it up into a list of text values no longer than 30000 characters
  3. Call the function once per row on the table returned by step (1)
  4. Use the Expand/Aggregate button to expand the new column created by step (3) and get a table with one row for each of the split-up text values

When I ran this query, though, I caught sight of something that is every Power Query developer’s worst nightmare:


Power Query had read 118MB of data from a file that is only 1.6MB: the old problem of multiple file reads. Using Process Monitor (as I describe here) confirmed it. I suspect the query was reading the whole file once for each of the split sections of text the function returned although I admit I didn’t confirm this.

I can’t say I knew what I was doing but I rewrote the query from scratch and came up with the code that I gave in the blog post which only reads from each file once (without using buffering too, I should point out). What’s the difference? I guess it must be the pattern of calling the function once per row in a table and then expanding using Table.ExpandListColumn that was to blame; I tried returning a table from the function instead of a list and the same thing happened. Maybe this is something we should avoid doing? More research is necessary, and, as always, I would be interested to hear about your experiences – it is after all a fairly common pattern.

Storing Large Images In Power BI Datasets

Jason Thomas and Gerhard Brueckl have both blogged on the subject of storing images as text inside a Power BI dataset:



Since they wrote those posts, however, Power BI has added the ability to set the Data Category property on measures as well as columns in tables. This means it is now possible to have the output of a DAX measure displayed as an image in a Power BI report and this in turn opens up a lot of new possibilities – including the ability to work around the maximum size of a text value that can be loaded into Power BI (see my previous blog post for more details) and therefore work with larger images.

Here’s a rather lovely picture of a rose:

2014-08-18 17.02.10_20Pct

The original is about 2.1MB; I have a folder on my PC where different versions of this picture, saved at different percentages of the original size, are stored:


Using the technique that Gerhard wrote about, where the pictures can be stored as text in a single cell in a Power BI dataset and then displayed (in this case I’m using the Image by CloudScope custom visual) some truncation of the image occurs even with the smallest files because of the 32766 character limit on the length of a text value that can be loaded into Power BI. Here’s what you see when you display the version of the picture that is 20% of the original size, a file of only 113KB:


To work around this, what you need to do is to split the text representation of the image up into multiple smaller text values stored across multiple rows, each of which is less than the 32766 character limit, and then reassemble them in a DAX measure after the data has been loaded.

Splitting the text up in M is actually not that hard, but it is hard to do efficiently. Here’s an example of an M query that reads all the data from all of the files in the folder above and returns a table:

    //Get list of files in folder
    Source = Folder.Files("C:\Users\Chris\Documents\PQ Pics"),
    //Remove unnecessary columns
    RemoveOtherColumns = Table.SelectColumns(Source,{"Content", "Name"}),
    //Creates Splitter function
    SplitTextFunction = Splitter.SplitTextByRepeatedLengths(30000),
    //Converts table of files to list
    ListInput = Table.ToRows(RemoveOtherColumns),
    //Function to convert binary of photo to multiple
    //text values
    ConvertOneFile = (InputRow as list) =>
            BinaryIn = InputRow{0},
            FileName = InputRow{1},
            BinaryText = Binary.ToText(BinaryIn, BinaryEncoding.Base64),
            SplitUpText = SplitTextFunction(BinaryText),
            AddFileName = List.Transform(SplitUpText, each {FileName,_})
    //Loops over all photos and calls the above function
    ConvertAllFiles = List.Transform(ListInput, each ConvertOneFile(_)),
    //Combines lists together
    CombineLists = List.Combine(ConvertAllFiles),
    //Converts results to table
    ToTable = #table(type table[Name=text,Pic=text],CombineLists),
    //Adds index column to output table
    AddIndexColumn = Table.AddIndexColumn(ToTable, "Index", 0, 1)

In my next post I’ll show you my original version of this query, explain why it was slow, and try to explain how the version above works and why it is much faster.

Here’s what the query above returns:


The Pic column contains the split text values, each of which are less than the 32766 character limit, so when this table is loaded into Power BI no truncation occurs. The index column is necessary because without it we won’t be able to recombine all the split values in the correct order.

The only thing left to do is to create a measure that uses the DAX ConcatenateX() function to concatenate all of the pieces of text back into a single value, like so:

Display Image = 
    HASONEVALUE('PQ Pics'[Name]),
    "data:image/jpeg;base64, " & 
        'PQ Pics', 
        'PQ Pics'[Pic],
        'PQ Pics'[Index],

…set the data category of this measure to be “Image URL”:


…and then display the value of the image in a report:



Unfortunately, as I also mentioned in my previous post, most DAX functions (and that includes ConcatenateX()) have a limit of around 2.1 million characters so the original 2.1MB file still can’t be displayed, alas:


However, I do think this technique will be useful because it allows you to work with much larger pictures than before.

It can also be useful in other situations too. I recently came across a great new custom visual called PDF Viewer that can display PDF files stored in text form in a Power BI report:


The example file for this visual shows how a large PDF file can be split across two columns in a table; the technique I describe here is a more practical solution to this problem.

What Is The Maximum Length Of A Text Value In Power BI?

What is the maximum length of a text value in Power BI? It turns out that this is a more complex question than you might think!

The maximum length of a text value that the Power Query engine can load into a single cell in a table in a dataset is 32766 characters – any more than that and the text will be silently truncated. However, if you’re working with text inside the Power Query engine you’ll find that you can work with much longer text values.  To illustrate this, consider the following M query:

    Source = 
            type table[charcount = number],
    #"Added Custom" = 
            each Text.Repeat("1", [charcount]),
            type text
    #"Inserted Text Length" = 
            #"Added Custom", 
            each Text.Length([LongText]), 
    #"Inserted Text Length"

It creates a table with four rows and three columns. The first column contains the numbers 1, 10000, 30000 and 40000; the second column contains the character “1” repeated the number of times given in the first column; the third column returns the length of the text in the second column using the Text.Length() M function. Here’s the output in the Power Query Editor, which is pretty much as you’d expect:


I’m not sure if there is a maximum length for text values in M; I experimented with adding an extra row to the table above with a 900,000,000 character text value and Text.Length() was able to return the correct value, albeit after a bit of a wait.

Load the table above into your Power BI dataset though, and add a DAX calculated column with the following expression:

DAX Length = LEN('LengthsDemo'[LongText])

…and you can see in the Data pane of the main Power BI Desktop window that the long text value in the last row has been truncated to 32766 characters:


Once you’ve loaded your data into Power BI the documentation says that the maximum length of a text value is “268,435,456 Unicode characters (256 mega characters) or 536,870,912 bytes”. The bad news is that many DAX functions such as ConcatenateX() put a limit on the length of the text values that they can work with at around 2.1 million characters (thank you Jeffrey Wang for providing this information – it isn’t documented anywhere at the moment). If you exceed this limit you’ll get the following error:

Function ‘PLACEHOLDER’ encountered a Text that exceeds the maximum allowable length.

In summary, then, there are two different practical limits on the maximum length of a text value in Power BI: the 32766 character limit on text being loaded into Power BI, and the 2.1 million character limit in DAX functions. The first of these can be worked around with some clever M – you need to split long text values up into multiple smaller values stored in different columns or rows – but even if you do this, the second limit may stop you recreating the original value after the data has been loaded.

Why is this useful or important? How can you split text values up in M in the most efficient way? I’ll come to that in my next two posts!

Power Query Comes To Azure Data Factory With Wrangling Data Flows

One of the many big announcements at Build this week, and one that caused a lot of discussion on Twitter, was about Wrangling Data Flows in Azure Data Factory. You can read the blog post here:


…but what isn’t clear from this is that it’s basically Power Query Online integrated into ADF. You can see it in action by watching the following video – the demo of Wrangling Data Flows starts at around the 21 minute mark:



As the presenter says, the Power Query Online editor generates M in the background as you would expect and “we are going to take this M and translate it into Spark and run it over big data”. Query folding to Spark, basically. More technical detail about all this is available here:


…including a document discussing which M functions currently support query folding and which ones as yet don’t. Obviously, this feature will only work well if as much query folding as possible takes place.

This feels like a much more significant win for team Power Query than the integration with SSIS that was announced recently, if only because SSIS is a bit legacy and ADF is the cool new thing. I wonder if this opens up the possibility of integration between Power BI dataflows and ADF in the future, as another example of how self-service BI solutions can be easily transitioned into centrally-managed, enterprise-grade BI solutions? If that happens I hope someone sorts out the dataflow/data flow naming mess.

You can sign up for the preview of Wrangling Data Flows here.

Extracting All The M Code From A Power BI Dataset Using The DISCOVER_M_EXPRESSIONS DMV

DMVs (Dynamic Management Views) are, as the Analysis Services documentation states, “queries that return information about model objects, server operations, and server health”. They’re also available in Azure Analysis Service, Power BI and Power Pivot and are useful for a variety of reasons, for example for generating documentation.

Several as-yet undocumented DMVs have appeared in Power BI recently and one that caught my eye was DISCOVER_M_EXPRESSIONS. Unfortunately, when I tried to run it in DAX Studio against an open Power BI file I got an error saying it was only available in the Power BI Service:


Luckily, now that XMLA Endpoints are now in preview and SQL Server Management Studio 18 has been released (which supports connections to Power BI via XMLA Endpoints) we can test it against a published dataset stored in a Premium capacity. The following query can be run from a DAX query window in SQL Server Management Studio:

select * from

…returns a list of all the Power Query queries  in the selected dataset and their M code:


If you don’t have Premium you can run the same query from an Excel table against any published dataset using the technique I blogged about here:


I know there are other methods for doing this (for example using copy/paste) it’s useful to be able to do this via a DMV because it means you can automate the process of extracting all your M code easily.

Some of the other new DMVs look like they are worthy of a blog post too – I can guess what most of them do from their names, but others are more mysterious and perhaps hint at features that have not been announced yet.

DAX Machine Learning Functionality Used By The Key Influencers Visual In Power BI

I’m one of those people who can’t resist peeking behind the scenes, and so when the Key Influencers visual appeared in Power BI I couldn’t help wondering how it worked its machine learning magic. Using DAX Studio to look at the DAX queries generated by the visual proved to be very revealing: it turns out that it uses a number of new DAX functions that are undocumented and probably not meant to be used outside Microsoft. For example, the following screenshot shows a DAX query generated by the Key Influencers visual that uses functions called AI.SampleStratified, AI.Train, AI.KeyDrivers and AI.ExtractProfileFilters:


Using Profiler (in a similar way to what I describe in this post) to go into even more detail about what happens when these queries run, shows that they raise the DAX Extension events that I’ve been wondering about for a long time now:



So Power BI can train and query machine learning models inside its own database engine – which, when you think about it, is pretty darned cool. And then I thought: hold on, other visuals have had machine learning features for a long time. For example, the Line Chart visual can create forecasts, but although DAX Studio shows yet another undocumented function called SampleAxisWithLocalMinMax() this does not actually seem to perform the forecasting, which I assume must be done inside the code of the visual itself:


My guess is that the functionality used by the Key Influencers visual is new functionality in the engine.

A fascinating insight into how Power BI works, but is this any practical use to us? Let me be clear: I don’t think you should be using any of these functions yourself in a real-world report. I’m sure all this would be documented and publicised if Microsoft did want us to use it ourselves! Another consideration is that these new functions return tables and that makes them awkward to use in regular .pbix Power BI reports – I guess we could create calculated tables although that’s not as flexible as returning a table from a query as shown above. That said, even though we can’t write our own DAX queries in regular Power BI reports, we can write our own DAX queries in Paginated Reports and we can now create Paginated Reports that use a Power BI dataset as a data source. I tested putting one of the queries generated by the Key Influencers visual into a Paginated Report connected to the same dataset and it worked ok (even after publishing). You can also embed DAX queries connected to a published dataset in Excel too, as I show here. Hmm, plenty to think about then…

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