Performance tip for List.Generate (1): Buffer your tables in Power BI and Power Query

Lately I was working on a fairly advanced allocation algorithm on large data which forced me to search for different tricks to improve performance than those that you can find on my site here already.


I was using List.Generate to check for every month in my table, if there was enough free capacity on a platform to start new wells. As every well had a certain production scheme (producing different amounts for a certain length of time), I first had to check the total production amount of active wells before I could determine the spare capacity for a new month. So I had to look into every active well, grab the capacity of the new month and add it up.

Therefore I’ve stored the active production schemes in one table in my List.Generate-record. That lead to an exponentially decreasing performance unfortunately.

Solution to improve performance of List.Generate

Buffering my tables in the “next”-function reduced the query duration by almost 70% !

Although a Table.Buffer or List.Buffer is always high on my list when it comes to performance issues, I was fairly surprised to see that behaviour here: As List.Generate returns the last element of its list as an argument for the next step, I was always assuming that this would be cached (and that was the reason because List.Generate performs recursive operations faster than the native recursion in M). Also, I had just referenced that table once ane in such a case, a buffer would normally not have come into my mind. (But desperation sometimes leads to unexpected actions …)

I also buffered a table that had just been referenced within the current record (and not recursively) and this improved performance as well. (Although in that case, the tables has been referenced multiple times within the current record). But this buffer didn’t have such a big impact on performance than the one on the table that was referenced by the recursive action.


Here is some pseudo-code illustrating the general principle:

Solution with buffers:

How to improve performance of List.Generate: Use Table.Buffer


Is that new to you or have you made the same experience? Which grades of performance improvements did you achieve with this method? Please let me know in the comments!

Enjoy & stay queryious 😉

How to do a real VLOOKUP (false) in Power Query or Power BI

When you merge tables with distinct keys in Power Query you will get the same result than the VLOOKUP-function in Excel returns (if this is new to you, check out this article for example: .

But how to retrieve only the result of the first row, if the lookup-table has multiple rows with the same key?



Say you have a dimension table for products:

Product table with one row per Product





and a transaction table with multiple entries per product:

Transactions table with multiple rows per Product






The task is to create 2 additional columns in your dimension table. One to show the first price at which the product has been sold and the other one the corresponding first date:

Select only first rows per Product

If you merge the transactions to the dimension table and expand it, you will end up with as much rows in the dimension table as there are in transaction table.


So how to retrieve only the elements of the first row of the matching tables? I’ll show you 2 different methods:

Solution 1 – Tweak the aggregation code

This is very quick to implement if you just want to return one or a few columns from the lookup-table: In the dialogue where you usually expand the columns, check “Aggregate” instead and click on one of the suggested aggregations for each column that I’m interested in (I simply ignore for a moment that these are not the aggregations that I actually need):

Choose one (false) aggregate per column







Now I tweak the code in the formula bar like so:

Tweaking Code for real VLOOKUP

Replacing the default aggregations by what I need (in red: List.First) and adjusting the column names directly in that command (in green: just to save one manual step later).

To avoid long query durations on large tables, you can transform the key column of the dimension table to a real key column, like Chris Webb has described here:

Solution 2 – Add a column that selects the whole desired row

If you want to retrieve many more columns from your lookup table, the method above can become a bit tedious. Then it might be easier to add a column, that grabs the whole first row instead: Table.First would do that job:

Add a column to retrieve the full first (or last) row

Then simply expand out all fields that you need.


You can use many different selection operations with this technique: So List.Last or Table.Last would give you the latest prices for example. This would actually be a more realistic use case here … and is the reason why I didn’t solve the original problem with just removing duplicates 😉 .

Enjoy and stay queryious 😉

Improve import of Excel sheets with empty rows and columns in Power Query and Power BI

When you import Excel sheets who have empty leading or trailing columns and rows (showing null-values), you can substantially improve the complexity and speed of your import process with a simple trick:

Remove the reasons for the empty trailing rows and columns 😉


Usually, when you import data from an Excel sheet, Power Query will automatically detect the used range in a sheet and will just return those rows and columns who have content in it. So how can it come that in some cases, additional rows or columns are returned who have nothing but empty values in them?


The reason for it can be cell formatting of empty cells. They often occur in old workbooks where cells have been deleted. These cells will be returned with a null-value during the import process with Power Query. See this blogpost for more details of potential pitfalls that come with it.


The “Inquire” Excel Add-On lets you clean any excess cell formatting. After you’ve executed this command, Power Query will not import any of those leading or trailing empty rows or columns any more. Often this will reduce the file size of the Excel files dramatically as well.


You will benefit from:

  • simpler query logic
  • potentially huge improved import speed, due to the reduced file size

Enjoy and stay queryious 😉

Fast and easy way to reference previous or next rows in Power Query or Power BI

When you search the web for solutions to reference previous or next rows in the query editor of Power BI or Power Query, you will find many solutions that base on an added index-column. But the problem with these solutions on large tables is that performance will range between slow and extremely slow. In this post I’ll show a faster method with function Table.ReferenceDifferentRow .

Basic mechanism

This new mechanism doesn’t use an index that is either used to merge the table with itself or to be referenced as a new row index. Instead, I “simply” add one table next to the other. To retrieve the previous row from a table, I reference the original table, delete its last row and add a blank row on top. That will “shift” the first row to the second row. Then I “put” this table just right to the original table, without referencing a key or applying any sort of logic. This will speed up the process considerably:

Shift table to reference different rows

The key of this method is the Table.FromColumns-function: It creates a table from a list of columns in the order of the columns in the list. So I just have to find a way to turn 2 tables into 1 list of columns:

Table.ToColumns(OriginalTable) & Table.ToColumns(ShiftedTable)

will do this job. Table.ToColumns will turn a table into a list of columns and the ampersand (“&”) will concatenate the lists from both tables.

The function

I’ve included this basic mechanism into a handy function with some bells and whistles: “Table.ReferenceDifferentRow”


How it works

  1. The only mandatory parameter is your table and then it will return a table with the previous rows values of all columns. So Table.ReferenceDifferentRow(MyTable) will return the result from above.
  2. The default-value for this parameter is set to -1 to return the previous row if you omit it. If you want the values from the next row instead, fill in 1. 2 will return the overnext and -2 the pre-previous row. This is what Table.ReferenceDifferentRow(MyTable, -2) returns:

    -2 will return the pre-previous row


  3. You probably just need one or a few columns/fields from the previous row: In the 3rd parameter you can enter a list of column names to be returned:   Table.ReferenceDifferentRow(MyTable, null, {"Value"}):

    Select specific columns


  4. Quite often the previous values shall only be returned within a group of rows. (That’s when you use [MyColumn] = EARLIER([MyColumn]) in DAX). You can enter a list of group-columns in the 4th parameter of this function: Table.ReferenceDifferentRow(MyTable, null, null, {"Product"})

    Group by columns

  5. By default, the suffix “.Prev” will be added to the new column names. Use the 5th parameter to change if needed. In this example, I reference the row below using “1” for the 2nd parameter: Table.ReferenceDifferentRow(MyTable, 1, null, null, "Next")

    Changing suffix and referencing next row

  6. If performance is still too bad, you can try to buffer the input table. Any value in the 6th parameter will do that for you (although I haven’t seen a performance improvement for my test cases).


Why not use DAX?

Referencing a previous row in DAX is still faster than my method above. So if you can do it in DAX, go ahead (create an index in the query editor and use LOOKUPVALUE like shown here: ) My function is for cases where you have to stay in M.

Enjoy & stay queryious 😉

Efficiently rename columns with a function in Power BI and Power Query

Edit 2018-July-05: As it turns out I’ve missed the native function that exists with exact the same functionality: Table.TransformColumnNames Thanks to Tristan Malherbe for pointing this out 🙂

But anyway: If you want to understand what it does and how you could have written it by yourself – read along:

The standard function to rename columns in the query editor of Power BI or Power Query is Table.RenameColumns. It takes a nested list of old and new column names as its second argument. But I often come across scenarios where I would like the new column names to be created dynamically. Therefore I’ve created a function (Table.RenameColumnsWithFunction) that takes a text transformation function as its argument instead of a hardcoded list of values:


Say you’re importing tables where the table name is part of each column name like so:

Old column names for Table.RenameColumnsWithFunction



But you only want to maintain everything after the TABLENAME.:

New column names for Table.RenameColumnsWithFunction




One way would be to replace “TABLENAME.” by nothing (“”):


This function allows you to pass the function as a second argument instead of a hardcoded list of new names like so:







As you can use any appropriate function, an alternative could also be to use is like this for example:

Table.RenameColumnsWithFunction( MyTable, each Text.BeforeDelimiter(_, ".")

So the underscore (_) stands for each column name of the table that you’re passing in as the 1st parameter of the function.

Function Code

If you don’t know how to handle custom functions, check out this video:

How it works

Details to row numbers:

  • 4: The old column names are retrieved and returned as a list
  • 5: Each item from that list will be transformed with the transformation function that has been passed in as the 2nd parameter
  • 6: Both lists are zipped to be in the required shape for the native function “Table.RenameColumns”
  • 7: The native function “Table.RenameColumns” is applied to perform the desired renaming operation
  • 10 ff: This is just function documentation that will appear when you address this function in the program

Hope this will help you one day, so

enjoy & stay queryious 😉

Automatically expand all fields from a JSON document in Power BI and Power Query

If you work with JSON documents that are new to you, it can be very helpful to fully expand JSON to see at a glance what’s in there. The following function has you covered for this task. It returns a table with all values in column “Value” and additional columns describing where that value came from in a hierarchical form, no matter how deep the nesting goes:


See below what this function does for the following JSON :

Automatically expand JSON: Sample JSON

{"A":"A Record", "B":["ListItem1", {"C":"A nested Record", "D":"Another nested Record"}], "E":{"F":["NestedListItem1","NestedListItem2","NestedListItem3"]}}

Automatically expand JSON: Result


  • Value: The respective values
  • Level: Main levels.
  • Sort: Sort column with hierarch to display in report
  • SortBy: Sort your “Sort”-column in the data model by this column: It will be filled up with “0” according to the maximum value within the respective position. This effectively allows sorting by number, although it is a text-field.
  • Name-columns: They display the hierarchical location of the value with regards to JSONs record field names.

The Function


How the code works

  • The main elements in JSON are records and lists and they can appear in many different combinations. So to handle them equally, I often convert them into a common type so that they can be further processed equally (row 6, 27 and 32).
  • I use List.Generate (row 10-42) to repetitively check whether the returned values are further expandable or not. If they are not expandable, they go into “Finished” (row 25) and if they need further expanding, they land in “Unfinished” (row 26) and will be expanded further.
  • Row 48-56 contain a dynamic padding that will convert the Sort-entry “2.1” into “02.1” in column “SortBy”, if there is also a “10.x” in the column. This will make sure that the (alphabetical) sort returns “2.1” before “10.1”. (And if there is a 100.x it will convert to “002.1” and so on…)

I’m pretty sure that this code can be further improved, as it has been evolved for quite some time and I didn’t re-engineer it. So if you’re up for it, please don’t hesitate to post an improved version!

For large JSONs, the table can get very long and it could be beneficial to view it in a more compact form (actually a form that would provide tables for a relational model). I will show this in an upcoming article, so if you’re interested in it, make sure to subscribe to my blog not to miss it.

Enjoy & stay queryious 😉