Value.ResourceExpression() And Query Folding In Power BI

When the June update for Power BI Desktop was released, I was extremely excited to discover an undocumented new feature that allows you to see when query folding is taking place while loading data in the Query Editor. Devin Knight beat me to blogging about it with an excellent post that I strongly recommend you to read:

https://devinknightsql.com/2016/07/03/power-bi-checking-query-folding-with-view-native-query/

As Devin says, this is very important because it’s the first time that we as developers can check to see whether query folding is taking place within the Power BI Query Editor (I guess this feature will come to Power Query soon, maybe sometime in autumn 2016), and getting query folding working is the key to getting good performance when loading data.

Looking at this more closely, I’ve found that it’s also possible to get the query generated in code as well as just seeing it in a dialog box. For example, consider the following M code that returns a few columns and some filtered rows from the DimDate table in the Adventure Works DW SQL Server database:

let
    Source = Sql.Database("localhost", "adventure works dw"),
    dbo_DimDate = Source{[Schema="dbo",Item="DimDate"]}[Data],
    #"Removed Other Columns" = 
	Table.SelectColumns(
		dbo_DimDate,
		{"DateKey", "FullDateAlternateKey", 
		"DayNumberOfWeek"}),
    #"Filtered Rows" = 
	Table.SelectRows(
		#"Removed Other Columns", 
		each ([DayNumberOfWeek] = 1))
in
    #"Filtered Rows"

image

You can confirm that query folding is taking place and see the SQL that is being generated for the Filtered Rows step by right-clicking on it in the Applied Steps pane and choosing View Native Query:

image

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If you now add two lines of code to the query you can get the SQL query for the #”Filtered Rows” step as a text value using the Value.ResourceExpression() function, like so:

let
    Source = Sql.Database("localhost", "adventure works dw"),
    dbo_DimDate = Source{[Schema="dbo",Item="DimDate"]}[Data],
    #"Removed Other Columns" = 
	Table.SelectColumns(
		dbo_DimDate,
		{"DateKey", "FullDateAlternateKey", 
		"DayNumberOfWeek"}),
    #"Filtered Rows" = 
	Table.SelectRows(
		#"Removed Other Columns", 
		each ([DayNumberOfWeek] = 1)),
    FindQueryRecord = 
        Value.ResourceExpression(#"Filtered Rows"),
    Output = 
        FindQueryRecord[Arguments]{1}[Value]
in
    Output

image

As far as I can see, the Value.ResourceExpression() function can be used with any expression where query folding is taking place and returns a record which, if you dig deep enough into it contains the SQL query. The record itself seems to contain all the information needed to invoke the new Value.NativeQuery() function which I’ve also been looking at recently – but I won’t be blogging about just yet because I’m told it’s not quite finished.

Why is it useful to get the SQL query as text, rather than just seeing it? Well… I have some ideas, and hopefully some other upcoming blog posts will make things clearer!

The Ellipsis/Not Implemented Error In M

Recently I was creating a parameter in Power BI Desktop and had it configured something like this:

image

I didn’t bother to choose anything in the Default Value dropdown box, and when I looked at the code for the parameter in the Advanced Editor I saw this:

"a" 
meta 
[IsParameterQuery=true, 
 List={"a", "b", "c"}, 
 DefaultValue=..., 
 Type="Text", 
 IsParameterQueryRequired=true]

I was interested to know what the ellipsis symbol (three dots …) in the DefaultValue field in the record meant, and looking in the Language Reference I found that in M it can be used for two purposes: as an open record marker (maybe something for a future blog post) and, as in this case, as a quick way of returning an error. The language reference says that it is directly equivalent to the following expression, which returns a “Not Implemented” error:

error Error.Record("Expression.Error", "Not Implemented")

But from what I can see, it actually returns a “Value was not specified” error instead. Here’s another example of how it can be used in a function definition, and what it returns:

let
    MyFunction = (x,y) => if x>y then true else ...,
    Output = MyFunction(0,1)
in
    Output

image

It’s not something I think I’ll be using in my own code, but it’s good to know what it means!

Creating Sequences Of Integers And Characters In Power BI/Power Query Lists

When you’re writing your own M code you often find yourself needing to create a list containing a sequence of numbers or characters. Luckily the M language allows you to do this very easily when you are defining lists by using expressions in the format

{lowest_integer..highest_integer}

For example, imagine you want to create a list with all of the integers between 1 and 5. Instead of writing

{1, 2, 3, 4, 5}

You can write the following:

{1..5}

and it will return the same list:

image

You can also use this format in more complex list definitions, for example

{1..3, 5, 7..9}

Returns the list

{1, 2, 3, 5, 7, 8, 9}

image

When you’re using this technique you must always put the lowest integer first and the highest integer last; if you don’t do this you get an empty list. So, for example, the expression

{5..1}

Returns an empty list:

image

It’s also possible to use this technique to create lists of characters. For example, the expression:

{"a".."z"}

Returns a list containing all of the lowercase letters of the alphabet:

image

The first character in the expression has to have the lowest Unicode value and the second character has to have the highest Unicode value, and the sequence of characters returned is the list of all characters with Unicode values in that range. As a result, the expression

{"#".."%"}

Returns the list

{"#", "$", "%"}

image

And the expression

{"a".."Z"}

Returns an empty list because the Unicode value of “a” is greater than the Unicode value of “Z”.

This technique doesn’t work for decimal numbers, dates or other data types. If you want a sequence of values of these types you need to use functions list List.Dates() and List.Numbers().

Lists are, of course, used all over the place in M. Building on my recent post on using #table() to create tables with no data source, here’s one last example of using lists containing sequences to create a simple table with three columns and three rows:

#table({"A".."C"}, {{1..3}, {7..9}, {11..13}})

image

Creating Tables In Power BI/Power Query M Code Using #table()

After my post earlier this week on creating current day/week/month/year reports in Power BI a few people asked me for a more detailed explanation of the way I was creating tables without using a data source in my M code. This is something I find myself doing quite a lot when I’m loading data with Power BI and Power Query, and while there are several ways of doing this I find that using the #table() intrinsic function is the most elegant option.

Let’s look at some examples. The following query returns a table with two columns (called “First Column” and “Second Column”) and two rows containing the values from 1 to 4:

#table({"First Column", "Second Column"}, {{1,2},{3,4}})

image

No data source is needed – this is a way of defining a table value in pure M code. The first parameter of the function takes a list of column names as text values; the second parameter is a list of lists, where each list in the list contains the values on each row in the table.

In the last example the columns in the table were of the data type Any (the ABC123 icon in each column header tells you this), which means that they can contain values of any data type including numbers, text, dates or even other tables. Here’s an example of this:

#table(
 {"First Column", "Second Column"}, 
 {
  {1,"Hello"},
  {#date(2016,1,1),3}
 }
)

image

While this is flexible it’s not exactly practical: in almost all cases the Any data type is a bad choice for loading data, and you need to explicitly set the data type for each column. You can set data types for columns quite easily as a separate step, but it is also possible to set column data types using #table():

#table(
 type table
    [
        #"Number Column"=number, 
        #"Text Column"=text,
        #"Date Column"=date
    ], 
 {
  {1,"Hello",#date(2016,1,1)},
  {2,"World",#date(2017,12,12)}  
 }
)

image

In this example the first parameter is no longer a list of column names but a declaration of a table type that not only has column names in but also column types. You can see from the icons in the column headers in the screenshot above that the column called “Number Column” has a data type of number, “Text Column” has a data type of text, and “Date Column” has a data type of date.

Of course if you need a fixed table value in Power BI you could use the “Enter Data” button or, if you’re using Excel and Power Query you could create an Excel table and then use the Excel.CurrentWorkbook() function to load the contents of it; if you or your end users need to edit the values in your table easily then you should use one of these two options. On the other hand if you don’t want users to be able to edit the values in the table or, more likely, you are generating the contents of your table using functions that return lists (as in my previous post) then #table() is the way to go.

Understanding Let Expressions In M For Power BI And Power Query

When you start writing M code for loading data in Power Query or Power BI, one of the first things you’ll do is open up the Advanced Editor for a query you’ve already built using the UI. When you do that you’ll see a very scary chunk of code (and at the time of writing there’s no intellisense or colour coding in the Advanced Editor, making it even more scary) and you’ll wonder how to make sense of it. The first step to doing so is to understand how let expressions work in M.

Each query that you create in Power BI Desktop or Power Query is a single expression that, when evaluated, returns a single value – and that single value is usually, but not always, a table that then gets loaded into the data model. To illustrate this, open up Power BI Desktop (the workflow is almost the same in Power Query), click the Edit Queries button to open the Query Editor window and then click New Source/Blank Query to create a new query.

image

Next, go to the View tab and click on the Advanced Editor button to open the Advanced Editor dialog:

image

You’ll notice that this doesn’t actually create a blank query at all, because there is some code visible in the Advanced Editor when you open it. Delete everything there and replace it with the following M expression:

"Hello " & "World"

image

Hit the Done button and the expression will be evaluated, and you’ll see that the query returns the text value “Hello World”:

image

Notice how the ABC icon next to the name of the Query – Query1 – indicates that the query returns a text value. Congratulations, you have written the infamous “Hello World” program in M!

You might now be wondering how the scary chunk of code you see in the Advanced Editor window for your real-world query could possibly be a single expression – but in fact it is. This is where let expressions come in: they allow you to break a single expression down into multiple parts. Open up the Advanced Editor again and enter the following expression:

let
    step1 = 3,
    step2 = 7,
    step3 = step1 * step2
in
    step3

image

Without knowing anything about M it’s not hard to guess that this bit of code returns the numeric value 21 (notice again that the 123 icon next to the name of the query indicates the data type of the value the query returns):

image

In the M language a let expression consists of two sections. After the let comes a list of variables, each of which has a name and an expression associated with it. In the previous example there are three variables: step1, step2 and step3. Variables can refer to other variables; here, step3 refers to both step1 and step2. Variables can be used to store values of any type: numbers, text, dates, or even more complex types like records, lists or tables; here, all three variables return numbers. The Query Editor is usually clever enough to display these variables as steps in your query and so displays then in the Applied Steps pane on the right-hand side of the screen:

image

The value that the let expression returns is given in the in clause. In this example the in clause returns the value of the variable step3, which is 21.

It’s important to understand that the in clause can reference any or none of the variables in the variable list. It’s also important to understand that, while the variable list might look like procedural code it isn’t, it’s just a list of variables that can be in any order. The UI will always generate code where each variable/step builds on the value returned by the previous variable/step but when you’re writing your own code the variables can be in whatever order that suits you. For example, the following query also returns the value 21:

let
    step3 = step1 * step2,
    step2 = 7,
    step1 = 3
in
    step3

image

The in clause returns the value of the variable step3, which in order to be evaluated needs the variables step2 and step1 to be evaluated; the order of the variables in the list is irrelevant (although it does mean the Applied Steps no longer displays each variable name). What is important is the chain of dependencies that can be followed back from the in clause.

To give another example, the following query returns the numeric value 7:

let
    step3 = step1 * step2,
    step2 = 7,
    step1 = 3
in
    step2

image

In this case, step2 is the only variable that needs to be evaluated for the entire let expression to return its value. Similarly, the query

let
    step3 = step1 * step2,
    step2 = 7,
    step1 = 3
in
    "Hello" & " World"

image

…returns the text value “Hello World” and doesn’t need to evaluate any of the variables step1, step2 or step3 to do this.

The last thing to point out is that if the names of the variables contain spaces, then those names need to be enclosed in double quotes and have a hash # symbol in front. For example here’s a query that returns the value 21 where all the variables have names that contain spaces:

let
    #"this is step 1" = 3,
    #"this is step 2" = 7,
    #"this is step 3" = #"this is step 1" * #"this is step 2"
in
    #"this is step 3"

image

How does all this translate to queries generated by the UI? Here’s the M code for a query generated by the UI that connects to SQL Server and gets filtered data from the DimDate table in the Adventure Works DW database:

let
    Source = Sql.Database("localhost", "adventure works dw"),
    dbo_DimDate = Source{[Schema="dbo",Item="DimDate"]}[Data],
    #"Filtered Rows" = Table.SelectRows(dbo_DimDate, 
                                    each ([DayNumberOfWeek] = 1))
in
    #"Filtered Rows"

Regardless of what the query actually does, you can now see that there are three variables declared here, #”Filtered Rows”, dbo_DimDate and Source, and the query returns the value of the #”Filtered Rows” variable. You can also see that in order to evaluate the #”Filtered Rows” variable the dbo_DimDate variable must be evaluated, and in order to evaluate the dbo_DimDate variable the Source variable must be evaluated. The Source variable connects to the Adventure Works DW database in SQL Server; dbo_DimDate gets the data from the DimDate table in that database, and #”Filtered Rows” takes the table returned by dbo_DimDate and filters it so that you only get the rows here the DayNumberOfWeek column contains the value 1.

image

That’s really all there is to know about let expressions. It explains why you can do the kind of conditional branching that Avi Singh describes here; and also why, when I first tried to come up with a way to time how long a query takes to execute, I had to bend over backwards to ensure that all the variables in my let expression were executed in the correct order (though it turns out there’s an easier way of doing this). I hope you find this useful when writing your own M code.

The M Code Behind Power BI Parameters

For me the most exciting new feature in Power BI in a long while is the appearance of Query Parameters for data loading. We have been promised an official blog post explaining how they work (although they are very easy to use) and in fact Soheil Bakhshi has already two very good, detailed posts on them here and here. What I want to do in this post, however, is look at the M code that is generated for them and see how it works.

Consider the following parameter built in Power BI Desktop that has, as its possible values, the names of all of the days of the week:

image

The first thing to notice is that parameters are shown as a special type of query in the Queries Pane, but they are still a query:

image

This means that you can open up the Advanced Editor and look at the M code for the query. Here’s the code for the query shown above:

"Monday" 
meta 
[
IsParameterQuery=true, 
List={"Monday", "Tuesday", "Wednesday", 
"Thursday", "Friday", "Saturday", "Sunday"}, 
DefaultValue="Monday", 
Type="Text", 
IsParameterQueryRequired=true
]

From this you can see that the value returned by the parameter query is just a single piece of text – it’s the value “Monday” that is set as the Current Value, that’s to say the value returned by the parameter itself. The interesting stuff is all in the metadata record associated with the value. I blogged about metadata here, so you may want to read that post before going any further; it’s pretty clear that the fields in the metadata record correspond to the values set in the UI. All of the fields in the metadata record can be edited in the Advanced Editor if you want.

When the parameter is used in another query it is referenced like any other query value. For example, if you load the DimDate table from the Adventure Works DW sample database and use the parameter above to filter the EnglishDayNameOfWeek column then the code generated in the UI looks like this:

let
    Source = 
          Sql.Databases("localhost"),
    #"Adventure Works DW" = 
          Source{[Name="Adventure Works DW"]}[Data],
    dbo_DimDate = 
          #"Adventure Works DW"{[Schema="dbo",Item="DimDate"]}[Data],
    #"Filtered Rows" = 
         Table.SelectRows(dbo_DimDate, 
           each [EnglishDayNameOfWeek] = Day)
in
    #"Filtered Rows"

The filtering takes place in the #”Filtered Rows” step and you can see where the name of the parameter – Day – is used in the Table.SelectRows() function to filter the EnglishDayNameOfWeek column. This is nothing new in terms of the language itself because you have always been able to return values of any data type from a query, not just tables, and you have always been able to reference queries in other queries like this – in fact you can see me write the same kind of code manually in this video. What is new is that there is now a UI to do this and there’s no need to write any code.

Personally, I think the Power BI team have done a great job here in terms of usability and clearly a lot of thought has gone into this feature. It doesn’t do everything I would want yet though: the ability to bind the list of available values to the output of another query and the ability to select multiple parameter values at the same time are obvious missing features (and ones that would be needed to match the parameter functionality in SSRS). However I would not be surprised to see them appear in a future version of Power BI.

After seeing the code, I wondered whether I could edit the code in the parameter query to make it do more interesting things. For example, even if the UI doesn’t support data-driven lists of available values for a parameter, it looks as though it should be possible to replace the hard-coded list with a list of values returned by another query. Unfortunately this does not work: any changes I tried to the parameter query code were either ignored or removed completely. A bit of a disappointment but again, hopefully this will be possible in a future version.

Timing M Query Execution In Power Query And Power BI (Revisited)

Some time ago I wrote the following post on how to work out how long your M query takes to execute in Power Query:

https://blog.crossjoin.co.uk/2014/11/17/timing-power-query-queries/

While it’s still relevant for Power Query and now of course Power BI (and it also illustrates some interesting facts about query execution), recently I had an interesting discussion with Colin Banfield in the comments section of that post that led to us finding an easier way of measuring query execution times.

In M, the DateTime.LocalNow() function returns the current system date and time at the time the function is called – which means that you could call it multiple times in the same query and get different results. There’s also the DateTime.FixedLocalNow() function which also returns the system date and time; the difference between the two is, as the documentation says:

This value is fixed and will not change with successive calls, unlike DateTime.LocalNow, which may return different values over the course of execution of an expression.

The question here is, though, what time does DateTimeFixedLocalNow() actually return? I asked on the Power Query forum here and Ehren from the Power Query dev team revealed that it returns the system date and time at the point when the query begins.

This means that it can be used to simply the original method I used to find query execution. Here’s a query that uses Function.InvokeAfter() to create a delay of 5 seconds during query execution and returns the difference between the values returned by DateTime.LocalNow() and DateTime.FixedLocalNow():

let
   SomeFunction = ()=> DateTime.LocalNow(),
   InvokeAfter5Secs = 
      Function.InvokeAfter(SomeFunction, #duration(0,0,0,5)),
   Output = 
      Duration.ToText(InvokeAfter5Secs - DateTime.FixedLocalNow())
in
   Output

image

If you’re using this on a real query I strongly recommend you read my original post carefully and make sure that all of the steps in your query are executed, but does make things a little bit easier.