Reading Rob Collie’s latest cool blogpost on how to retrieve slicer selections in Power BI, I couldn’t stop thinking of how awesome it would be, if we could use this technique to pass slicer selections as query parameters to the M-queries in the query editor. Not only would we have a very convenient user interface, but – what’s actually more important at the moment – we could pass multiple values as parameters to our queries, as this is not possible at all currently:
But how to fetch them? Rob’s post simply uses cross-filtering to show the values in a separate visual. In Excel we have cubefunctions where we can pass the slicer(-selection) as a parameter. Igor Cotruta, who is describing beautiful PBI-hacks on his blog here, kindly helped me out on this: “Via DMVs. Check $system.discover_sessions for the field sessions_last_command”. This worked perfectly into the following function, in which you just have to pass the name of the measure as a parameter:
Code beautified using Lars Schreiber’s Notepad++ Script: http://ssbi-blog.de/technical-topics-english/power-query-editor-using-notepad/
Make sure that you have used that measure on one of your visuals, as otherwise the function cannot harvest it. Also you have to first save the file and then push the refresh-button in order to trigger the correct refresh. The above function sort of “reads the current PBI file from outside”, so it will only see the saved version.
When you do the first refresh, a dialogue will pop up, where you just have to accept the default values like this:
The example in the file below fetches temperature data where every selected year will create a unique URL and the results of all those calls is consolidated into one table. But of course, this technique can also be used to pass multiple parameter values to SQL-commands or others.
A final note: The query to extract the slicer parameters from the DAX-statement is not particularly robust and you might have to adjust it, if your slicer-selection-strings contain special characters.
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Enjoy & stay queryious 🙂
In finance & accounting, you very rarely report the figures with the signs of their source systems, but switch (certain) signs according to different needs. Instead of using unary operators for it, I’ll present an easy and dynamic way for it in Power BI and Power Pivot using DAX. It will cover the following 3 main scenarios:
- 1_SwitchAll: All signs are switched (red)
- 2_SwitchExpLiab: Expenses and liabilities are switched back to their original values (green)
- 3_BWT_Indiv: Only the main figure for expenses (or liabilities) carries a minus, all following positions specifying the expenses are (principally) reported as positives (blue)
Switching signs in Power BI and Power Pivot without unary operators
I’m using the sample data from this article but changed the source-data to a double-bookkeeping structure. There signs are used and the transaction entries in your ledger table always add up to zero. This is a method that prevents errors when posting and can also be used to prevent errors in reporting. If you keep the signs in your reporting system, all you have to do is add up the relevant figures and the returned (absolute) figures will always be correct. If you have read my previous articles on Easy P&L, you have seen this method in action: No minus-operation there, just a simple stupid adding of all accounts who fall into several (sub-) total categories via the bridge-table.
The Account-table also contains of (sub-) totals and the column “AccountType” shows if the positions are regarded as Turnover (Revenue) or Expenses:
My values on “1_SwitchAll” corresponds to “FinalValue” in the article above. The revenues come from consultancy and coursed provided. But the revenue for courses don’t just consist of attendee rates, but the costs for catering and paid instructors shall be deducted (highlighted in yellow). So the “good” numbers that contribute to cash in your pocket shall be reported without a sign and the “bad” numbers that result in an outflow of cash shall be reported with a minus. Within the expenses category, the costs carry a minus and the travel refunds (highlighted in orange), which are cash positive, are reported as positives.
Another requirement that is often used for balance-sheet-reporting or reports that only report on cost-situations, require that the costs or liabilities are reported without signs. … Principally, because the reimbursements/cost deductions shall be reported with an opposite sign (to show the adverse effect to the cashflow). This is what “2_SwitchExpLiab” shows (not covered in the article).
Last but not least comes a typical “BossWantsThat”-requirement: Basically some strange stuff that you just have to deliver. Here the main categories “Revenues” and “Expenses” shall be shown with the signs that reflect the cash-direction, but all specifications that follow below shall be reported without signs (again: Principally, because positions with opposite cash-effects than the main category shall carry inverted signs).
Reporting techniques covered with this approach
Excel-reports on SSAS cubes (multidimensional and tabular) can have some flaws that now can be overcome by using Power Query for sourcing your cubedata:
- filter your cube by complete Excel-tables without loading them to the model/cube
- Apply nice number and date formats to non-measure number and date fields in your row- or column section
- create fast detailed reports (multiple attributes in your row sections, overcome the slow MDX that the pivots on cubes produce)
As with the recent Power Query update (26) you can now create your own MDX and DAX-statements for retrieving data from a cube, it is also possible to pass individual parameters from your Excel-sheet to the queries. This is a prerequisite for dynamically reducing the number of returned fields to the query, thereby allowing a decent performance of these reports.
So how about filtering the query by a table that sits in your local Excel file? Can we do an inner-join just like on the SQL-server-source? Read more
Today I read Andrew Todd’s brilliant guest post on Powerpivotpro.com about some really cool tricks on cubefunctions. The second one shows a very elegant solution to what I’ve formerly done with my “hidden-Pivot”-technique: Avoid the cubeset-functions by referencing a hidden pivot table instead. So if the set of “cubeslices” you want to use in your reports is static, this is the most elegant way to go in my eyes.
But if it is dynamic, like in a Top-10 ranking or similar, this technique still has it’s reason of being: Say you want to show a section in your dashboards that shows top-x elements, a calculation of their share on the total, then an aggregation on all others and a sum on all like this:
Very happy to see that my Power Query Management Studio isn’t just perceived nerdy but useful as well 🙂 Thanks to Dusty for his nice review.
So let’s push it a bit further and add some MDX functions to it that cannot be done by DMVs:
- get a list of all unique fields used in a specific MDX query
- translate your code to a different cube using a simple field-translation table
How to use this for MDX:
During my evaluation of Power Query as a reporting engine I wondered why we should keep detail fields in our cube at all if the preferred output is a flat table anyway. Cubes are meant for aggregation, aren’t they?
Especially in the Finance- & Accounting area you will come across many cubes with detail fields because sometimes you simply need to perform analysis on ledger entry level. But this seems like a loose/loose scenario in my eyes: Not only do these detail reports often perform badly, their biggest negative impact might lie in the fact that they cause the fact tables to be x-times bigger than the next aggregation level, thereby decreasing the overall performance of the cube.
So how about this approach then: Use Power Query for your reports on detail level: Directly connect to your fact table in the DWH and merge to your SSAS-data in order to retrieve the attributes/filters only. Or keep your fact tables in a dedicated DB if your DWH serves other purposes as well and you fear the performance impacts of those queries.
So this would leave the cubes’ fact tables with much less data -> improving performance.
I tried some scenarios that worked fine. But putting the fact tables into a separate tabular model instead of a relational DB performed quite badly.
Does anyone have experience with this approach? If you know someone who might, please forward.
What do you think about this approach, any other obstacles I’ve missed?
Cascading Time Granularities
When the client is not happy with the pivot report layout options on cubes in Excel, my usual reaction is “OK no problem, then we’re going to use cubefunctions instead”. This went well until recently: My client told me that he wanted a report like above: Years totals first, followed by quarters & months totals, but with growing number of years! All nicely close together, no gaps. Normally I don’t mind cubereports with dynamic table length (will blog about this later), but this is basically 3 dynamic reports under each other (yes they are still alive and will probably stay 😉 ):
So it was time to test what Chris Webb has talked about in this blogpost: Using Power Query as a report authoring tool in Excel. The idea was to use Power Query’s append-function to attaching 3 different report: Year, Quarter, Month. They could then have different lengths, the append-operation would seamlessly stitch them together. The connection to the cubedata was easy enough, thanks to his brilliant step-by-step guide. But then there were some challenges to solve: