Making Sure All Columns Appear When You Combine Data From Multiple Files In Power BI/Power Query M

Here’s a really common problem that occurs when combining data from multiple files, or indeed any type of data source, in Power BI/Power Query/Excel Get&Transform. Imagine you have a folder with two Excel files in, and each Excel file contains a table called SalesTable:

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You use the “From Folder” data source to combine all the data from all the Excel files in this folder, you get a table like this:

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…and you’re happy. Then, at some later date a third file is added to the folder that has an extra column in its SalesTable table called Comments:

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You refresh your query, though, and you don’t see the Comments column anywhere:

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Why not? If you look at the query that has been generated and go back to the “Removed Other Columns1” step you’ll see a table containing a column containing table values:

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…and you’ll also see that the next step in the query, “Expanded Table Column1”, uses the Table.ExpandTableColumn function – the M function that gets called if you click the Expand/Aggregate button in the column header highlighted in the previous screenshot – to flatten these nested tables out. And the problem is that Table.ExpandTableColumn needs to know in advance the names of the columns you want to expand.

Now this is an extremely common, and powerful, Power Query/M pattern. Apart from the “From Folder” functionality for automatically combining data from multiple files it’s something I find myself building manually all the time: write a function, for example to make a single call to a web service; create a table containing one row for each call to the web service that I want to make, use the Invoke Custom Function button to call my function for each row, and then expand to get all the data from all the function calls. And the more I use this pattern, the more I run into situations where I don’t see columns I’m expecting to see because I’ve done an Expand in an earlier step that has a hard-coded list of column names in it (it’s a very similar problem to the one that Ken Puls blogged about here). It’s a pain to have to keep changing this list, and the real problem comes when you don’t actually know in advance what the names of the columns to expand are.

One solution would be to do something similar to what I show in this post: iterate through all the tables in the table column, find a distinct list of column names, and then use this list with Table.ExpandTableColumn. However, there is an easier way to handle this: use Table.Combine instead of Table.ExpandTableColumn. The great thing about Table.Combine is that it will always return all of the columns from all of the tables it’s combining.

Here’s a function that shows how it can be used:

(TableColumn as list, optional SourceNameColumn as list) =>
let
  AddIDs = 
    if 
    SourceNameColumn=null 
    then 
      TableColumn 
    else 
      let
        ZipNames = 
          List.Zip({TableColumn, SourceNameColumn}),
        AddColumnFunction = 
          (ListIn as list) => 
          Table.AddColumn(ListIn{0}, "Source", each ListIn{1}),
        AddColumns = 
          List.Transform(ZipNames, each AddColumnFunction(_))
      in
        AddColumns,
  Combine = Table.Combine(AddIDs)
in
  Combine

This function takes a list of tables and, optionally, a list of text values that contain a name for each table (this optional parameter accounts for the majority of the code – without it all you would need is the Combine step). If you paste this code into a new query called, say, CombineTables, you can either call it by adding some M code to an existing query or more easily just call it direct from the UI. In the latter case when you click on the function query in the Query Editor window you’ll see this:

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Assuming you already have a query like the one shown above that contains a column with table values in it and another column containing the original Excel file names, you need to click the Choose Column button for the TableColumn parameter and select the column that contains the table values in the dialog that appears:

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…and then do the same thing for the SourceNameColumn parameter:

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…and then click the Invoke button back in the Query Editor, and you’ll get a table containing all of the data from the SalesTable table in each workbook, including the Comments column from the third Excel workbook:

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With no hard-coded column names you’ll now always get all of the data from all of the columns in the tables you’re trying to combine.

The Extension.Contents() M Function

Following on from my post last week about M functions that are only available in custom data extensions, here’s a quick explanation of one of those functions: Extension.Contents().

Basically, it allows you to access the contents of any file you include in the .mez file of your custom data connector. Say you have a text file called MyTextFile.txt:

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If you create a new Power BI Custom Data Connector project using the SDK, you can add this file to your project in Visual Studio like any other file:

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Next, select the file and in the Visual Studio Properties pane set the Build Action property to Compile:

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Setting this property means that when your custom data connector is built, this file is included inside it (the .mez file is just a zip file – if you unzip it you’ll now find this file inside).

Next, in the .pq file that contains the M code for your custom data connector, you can access the contents of this file as binary data using Extension.Contents(“MyTextFile.txt”). Here’s an example function for use in a custom data connector that does this:

[DataSource.Kind="ExtensionContentsDemo", 
Publish="ExtensionContentsDemo.Publish"]
shared ExtensionContentsDemo.Contents = () =>
    let
        GetFileContents = Extension.Contents("MyTextFile.txt"),
        ConvertToText = Text.FromBinary(GetFileContents)
    in
        ConvertToText;

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In the let expression here the GetFileContents step returns the contents of the text file as binary data and the ConvertToText step calls Text.FromBinary() to turn the binary data into a text value.

When this function is, in turn, called it returns the text from the text file. Here’s a screenshot of a query run from Power BI Desktop (after the custom data connector has been compiled and loaded into Power BI) that does this:

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Which M Functions Are Only Available To Custom Data Connectors?

Here’s one for all you M geeks out there. If you look at the example code for custom data connectors on the Power BI custom data connectors GitHub repo it’s clear that there are several M functions that are available in custom data connectors that aren’t available in Power BI Desktop. But what are they? As I’m sure you know, you can get a list of all the functions, types and enums available in M using the #shared keyword. Well, I created a simple custom data connector that calls #shared and returned a table of all the functions, types and enums available to a custom data connector, and then compared that table with what #shared returns when you run it in Power BI Desktop. This made it easy to find a list of M functions, types and enums that are only available in custom data connectors, and here are all 34:

  • CryptoAlgorithm.Type
  • CryptoAlgorithm.SHA1
  • CryptoAlgorithm.SHA256
  • Crypto.CreateHmac
  • Crypto.CreateHash
  • Web.SignForOAuth1
  • OAuth1.Type
  • OAuth1.HMACSHA1
  • OAuth1.RSASHA1
  • Extension.Module
  • Extension.CurrentCredential
  • Extension.CurrentApplication
  • Extension.CredentialError
  • Extension.LoadString
  • Extension.Contents
  • Credential.AccessDenied
  • Credential.AccessForbidden
  • Credential.EncryptionNotSupported
  • Credential.NativeQueryPermission
  • Error.Unexpected
  • Uri.Type
  • Binary.End
  • Action.Type
  • Action.Sequence
  • Action.Return
  • Action.Try
  • Action.DoNothing
  • ValueAction.Replace
  • ValueAction.NativeStatement
  • TableAction.InsertRows
  • TableAction.UpdateRows
  • TableAction.DeleteRows
  • WebAction.Request
  • Delta.Since

Some of these, like the Action functions, are documented in the Power Query function reference, and as I said others are mentioned in the Power BI custom data connectors GitHub repo, but there’s definitely some detective work to do here…

Creating A Partitioned Table In SSAS Tabular 2017 And SSDT Using M Functions

One of the coolest new features in SSAS Tabular 2017 and Azure Analysis Services is the integration of Power Query and M for data loading. Over the last year or so the Analysis Services team blog has posted a lot of fairly complex examples of how to use this functionality, but now that the latest release of SSDT has proper support for shared expressions I thought it would be a good idea to show a simple example of how to use it to create a partitioned table using M functions.

For this example I’ll be using the FactInternetSales fact table from the Adventure Works DW sample database, and the aim is to create a table in an SSAS Tabular project that has one partition for each year of data in FactInternetSales. Assuming that a new SSAS Tabular project has been created at the 1400 compatibility level with an integrated workspace:

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…the first thing to do is to right-click on the Data Sources folder in the Tabular Model Explorer pane and select Import From Data Source:

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This brings up the Get Data dialog:

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Select SQL Server database and then click Connect. Enter the server name and database name in the SQL Server database dialog:

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Choose how SSAS is to authenticate when it connects to the SQL Server database and click Connect:

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Select the FactInternetSales table from the list of tables in the Adventure Works DW database:

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This opens the Query Editor window; in it there is one query called FactInternetSales:

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Here’s where it gets interesting. The first thing to do is to create a function that returns a filtered subset of the rows in the FactInternetSales table using the technique I blogged about here for Power BI. On the Query Editor menu bar, click Query/Parameters/New Parameter and create two new parameters called StartDate and EndDate that return the numbers 20010101 and 20011231. Here’s what they should look like:

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These parameters are going to be used to filter the OrderDateKey column on the FactInternetSales table. Do this by clicking on the down arrow on the column header of OrderDateKey then selecting Number Filters and then Between:

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In the Filter Rows dialog use the StartDate parameter for the start of the filter range and the EndDate parameter for the end of the filter range, then click OK:

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Because the OrderDateKey contains dates stored as numbers in the YYYYMMDD format the result is a table that only contains sales where the order date is in the year 2001. This table should not be loaded into SSAS though, so right click on the FactInternetSales in the Queries pane and make sure that the Create New Table is not checked:

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Next, on the same right-click menu, select Create Function:

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In the Create Function dialog name the new function GetFactData then click OK:

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The new GetFactData function will now be visible in the Queries pane; enter 20010101 for the StartDate parameter and 20011231 for the EndDate parameter and click Invoke:

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This creates yet another new query called Invoked Function which should be renamed Internet Sales:

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Right-click on this query and make sure Create New Table is selected. Next, click the Import button on the toolbar to close the Query Editor and load the Internet Sales table into SSAS.

At this point the Tabular Model Explorer will show all of the queries created above listed under the Expressions folder, and a single table called Internet Sales with a single partition:

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Next, right-click on the Internet Sales table in the Tables folder and select Partitions:

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This opens the Partition Manager dialog. Rename the existing partition to Internet Sales 2001:

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Note that the M query for this partition calls the GetFactData() function to get the rows from FactInternetSales where OrderDateKey is between 20010101 and 20011231:

let
    Source = GetFactData(20010101, 20011231)
in
    Source

Click the New button to create new partitions, one for each year of data in the FactInternetSales table. Each new partition will initially contain the same M code shown above and should be edited so that the query gets data for the appropriate year:

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Click OK, and the end result is a table with one partition per year:

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What’s the point of using M functions to return the data for a partition, rather than the traditional method of using a SQL query embedded in each partition? One reason to do this would be to make maintenance easier: if you need to do something like add a new column to a fact table, rather than editing lots of partitions you just need to edit the function and all the partitions will reflect that change. I can think of a few others, but I’ll save them for future blog posts…

Setting Data Types On Columns Containing Table Values In M

Last week I was working on a Power BI custom data connector for a customer and I came across a problem that has been bothering me for a while. The “Invoke Custom Function” button is a very powerful feature (see here for how to use it) and I was doing something very similar in my code, but after you click it and expand the table column it returns, you always have to set the data types on the new columns that appear – even if the function you have invoked returns a table with data types set on columns. I wanted to avoid this extra step. I knew it was possible because some data sources like SQL Server give you typed columns when you expand a table column; I also saw that TripPin custom data connector tutorial aims to cover this topic… soon. Luckily it wasn’t too hard to work out.

Imagine you have the following function:

(x as number, y as number) as table =>
let
    Addition = x + y,
    Multiplication = x * y,
    Output = #table(
	type table [Operation=text, Result=number],
	{
	{"Addition", Addition},
	{"Multiplication", Multiplication}
	})
in
    Output

It takes two numbers and returns a table with two columns and two rows showing the sum and the product of the two numbers. If the function is called AddAndMult, then the following expression:

AddAndMult(5, 6)

…returns the following table:

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Notice that the function returns a table with data types set for the columns: Operation is a text column and Result is a number column. I’ve done this by specifying a table type in the first parameter of the #table() function as described here.

Now, here’s a query that calls the function on a table containing several rows using the Invoke Custom Function button:

let
    Source = #table(
	type table[x=number, y=number],
	{
	{4,7},
	{8,2},
	{1,9}
	}),
    #"Invoked Custom Function" = Table.AddColumn(
	Source,
	"AddAndMult",
	each AddAndMult([x], [y])),
    #"Expanded AddAndMult" = Table.ExpandTableColumn(
	#"Invoked Custom Function",
	"AddAndMult",
	{"Operation", "Result"},
	{"AddAndMult.Operation", "AddAndMult.Result"})
in
    #"Expanded AddAndMult"

This is the table returned by the Source step:

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This is what the Invoked Custom Function step looks like:

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And this is what the Expanded AddAndMult step looks like:

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In the last two screenshots the ABC123 icon in the column headers show that they are set to use the Any data type; the columns returned by calling the function have lost their data types.

The key to solving this problem is using the optional fourth parameter of the Table.AddColumn() function, which allows you to set a data type for the column that function adds to a table. Altering the Invoked Custom Function step of the previous query to do this, setting the new column to be a table type like so:

#"Invoked Custom Function" =
	Table.AddColumn(
		Source,
		"AddAndMult",
		each AddAndMult([x], [y]),
		type table [Operation=text, Result=number]
	),

 

…means the Invoked Custom Function step now returns a column of type table, with the appropriate icon in the top left column:

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…and once the AddAndMult column is expanded, the new columns have the desired data types set:

DT

The Power Query Branding Problem

A few years ago I started blogging about Power Query. Back then life was simple: I put “Power Query” in the title of a post and everyone knew what I was writing about, because Power Query was an Excel add-in you could download and install. Now, however, the technology has been renamed “Get & Transform” in Excel 2016 and is a native feature of Excel; the name “Power Query” only applies to the add-in for Excel 2010 and 2013. What’s more, the same technology is used in Power BI’s Query Editor and it’s also now in Azure Analysis Services, Analysis Services 2017 Tabular and the Common Data Service. This is obviously a good thing – I think Power Query is one of the best things to come out of Microsoft in the last decade – but it also presents me with a problem. How can I write about this technology if it doesn’t have a single, official, easily identifiable name?

In more recent times I’ve written posts with unwieldy names like “Introduction to Insert Topic Name Here in Power Query/Power BI/Excel 2016 Get & Transform” and in the future I suppose this will have to grow to “Introduction to Insert Topic Name Here in Power Query/Power BI/Excel 2016 Get & Transform/Analysis Services Data Loading/Common Data Service”. Tagging and categorising blog posts can help here, I know, but it’s the title of a blog post that’s the main determining factor as to whether it gets read or not when someone is looking at a list of search results. It’s getting ridiculous, but how else can I ensure that someone searching for the solution to a data loading problem in Excel 2016 Get & Transform will find a post I’ve written that contains the answer but shown in Power BI?

Inside Microsoft I understand that the team that builds this technology is known as the Power Query team. I certainly think about this technology as being called Power Query, as do a lot of other people in the community. However, my argument is that I can’t just use the name “Power Query” when I’m writing or speaking about this technology because most of its users – especially those who are new to it and who need the most help – don’t think of it as “Power Query”. They think of it as Excel 2016 Get & Transform, the Query Editor in Power BI Desktop and so on, the specific instances of it.

Maybe I’m making too big a deal of this, but in my opinion this is a problem not just for me but for Microsoft too. We all know how much developers rely on internet searches to find solutions to problems, and not having a single name for this technology makes it much harder to search successfully. This in turn makes it less likely that when a developer runs into a problem they will be able to solve it, which in turn means they are less likely to want to use this technology in future.

What’s the answer? It has to be to make the “Power Query” brand visible somewhere in the UI of all the products that use Power Query technology. I know there’s a risk of confusing users instead of helping them here (am I using Power Query or Power BI?), but it could be as simple as making a few small changes like renaming the “Query Editor” window to be the “Power Query Editor”:

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I think that would be enough to let people know that “Power Query” is a technology in its own right and that content referring to “Power Query” is relevant to Excel, Power BI, SSAS and everywhere else that Power Query is used. It would also be nice if, now that M is the official name of the M language (and not Power Query Formula Language), the Advanced Editor window and the Custom Column dialog let users know that the code they were writing in them was in a language called M and not some mysterious, nameless scripting language.

What do you think? I’m interested to hear your comments and opinions…

UPDATE: victory is ours! See this comment from Faisal Mohamood of the Power Query team below
Hey there Chris – what you are saying makes complete sense. Power Query is the name of this capability and we will highlight the name of this capability as such in experiences where you are working with Power Query (and M).

New M Functionality And Behaviour In Power BI Custom Data Connectors

Over the past few weeks I’ve spent some time playing around with Power BI custom data connectors and while I don’t have anything to share publicly yet (other people are way ahead of me in this respect – see the work of Igor Cotruta, Miguel Escobar and Kasper de Jonge among others) I have learned some interesting things that are worth blogging about.

First of all, the data privacy rules around combining data from different data sources do not apply in custom data connector code. As the docs say here:

Data combination checks do not occur when accessing multiple data sources from within an extension. Since all data source calls made from within the extension inherit the same authorization context, it is assumed they are “safe” to combine. Your extension will always be treated as a single data source when it comes to data combination rules. Users would still receive the regular privacy prompts when combining your source with other M sources.

Those of you who have followed my recent series on this topic, or who have struggled with the Formula.Firewall error, will appreciate how much easier this makes combining data from different sources.

Secondly, you have a lot more flexibility when it comes to different types of authentication for web services. As I showed in my session on web services and M at the Data Insights Summit, there are a lot of limitations when it comes to working with web services in Power BI or Excel. Within a custom data connector, however, you can connect to web services that use OAuth for authentication, you can make POST requests to web services that require authentication and you can pass a web API key from the credentials store through an HTTP custom header and not just through a query parameter – none of which are possible in Power BI or Excel.

I’m sure there are a lot of other useful bits of functionality or behaviour that are only available in custom data connectors – I know I’ve only just begun to learn what’s possible. Even with what I’ve listed here, though, I get the feeling that there will be a lot of cases where you will have no choice but to build a custom data connector just to be able to access certain data sources, even if you only need to create a single report. There may also be cases where it’s preferable to build a custom data connector rather than embed lots of complex M code in a Power BI report or Excel workbook, perhaps to make code portability easier. It’s a bit of a pain to have to have Visual Studio and the SDK installed in order to do this, but building a custom data connector is fairly easy if you already know M and the development experience in Visual Studio (with intellisense!) is much better than in the Advanced Query Editor window.