Can you please explain why I run into this alert message of circular dependency when I try to create relationship between dimension #product (or #region) and a #bridge table which is a Cartesian of product x region?
I have connected #bridge with Sales and Budget by single column P#G witch is concatenation of product and region.
Download file here: PBIX
The solution is simple. Do not use CALCULATE function in the DAX bridge tables. Instead add all that columns to the same table later as calculated columns.
I changed the original code of the bridge table which was:
ADDCOLUMNS (
CROSSJOIN ( '#product', '#region' ),
"P#R", COMBINEVALUES("#",'#product'[product], '#region'[region]),
"sales", CALCULATE ( SUM ( Budget[target] ) ),
"IsSale", IF ( CALCULATE ( SUM ( Budget[target] ) ) > 0, "Yes", "No" )
)
To something simpler:
ADDCOLUMNS (
CROSSJOIN ( '#prodact', '#region' ),
"P#R", COMBINEVALUES("#",'#prodact'[product], '#region'[region])
)
I modified the DAX code of bridge table so as to leaving only the columns necessary for joins. The columns that I needed to be calculated I added as calculated columns. And that's it. It was by pure chance I found that out while experimenting with it.
For playing with bridge tables I recommend this Alberto Ferrari's article: https://www.sqlbi.com/articles/avoiding-circular-dependency-errors-in-dax/. It inspired me to solve the problem. What I get from the Alberto's text is that the functions VALUES and ALL are no good for bridge tables. He mentions issue of using CALCULATE function inside the bridge DAX tables. The function somehow is translated to mixture of ALL and FILTER functions. Instead of VALUE and ALL, use functions as DINSTINCT and ALLNOBLANKROW.
Working PBIX file. Hurray!
A quick and dirty solution is to creat to new versions of #product and #region by using VALUES. This is probably not the best way of doing it...
NewProduct = VALUES('#product'[product])
This new tables can be linked to #bridge with a 1:* relationship and thus can be used as a slicer on the dashboard.
Alberto has written about this on the sqlbi blog: Circular dependency sqlbi blog
Related
A co-worker has asked me to help her with an issue she is having. Her data model looks as follows:
She is using SUMMARIZE to build the Jobs table. In part, the DAX looks like this:
Jobs = SUMMARIZE(
'MainSI Table',
'MainSI Table'[Market],
'MainSI Table'[Region],
'MainSI Table'[EmployeeId],
'EmployeeTable'[Business],
)
This works but she is not able to add any columns from WODetail (the error is that the column selected from WODetail cannot be found) and I do not know why when there is a 1:M relationship between MainSI and WODetail.
You need to use ADDCOLUMNS, RELATEDTABLE and then summarise the result from the detail table according to requirements. e.g.
ADDCOLUMNS (
SUMMARIZE(MainSI Table, MainSI Table'[Market]),
"Max",MAXX(RELATEDTABLE(WODetail),WODetail[Job Finish Date])
)
I am trying to understand the below DAX code:
CALCULATE(
SUM(Revenue[Net])
,FILTER('Center', NOT 'Center'[Acc] IN {"RSM", BLANK() })
,ALLSELECTED()
,VALUES('Customer'[Customer Number])
)
I have the below questions:
What's the use of ALLSELECTED?? By definition ALLSELECTED returns all rows in a table, ignoring any filters that might have been applied inside the query, but keeping filters that come from outside. https://dax.guide/allselected/
So, what's the point of writing FILTER() if its going to be forced to be ignored by the next line (ALLSELECTED)?!?
Also by definition:
CALCULATE is just a expresion followed by filters...
What's the use of VALUES() ? It doesn't appear to be a filter, so how is it even allowed to appear there? (Per definition VALUES(): returns a one-column table that contains the distinct values from the specified column.)
I do not understand what is this returning? is it the SUM() or the VALUES()?
(I come from a SQL background, so any sql-friendly answer is appreciated).
In Dax every filter is a table of values its look similar to INNER JOIN;
ALLSELECTED is useful when you need to keep a row context (this is also a filter in DAX). You can use ALLSELECTED inside FILTER function.
For better understand what engine does you can use a DaxStudio with ServerTiming;
As you see this product one simple statement:
SELECT
SUM ( 'Table'[Cost Centre] )
FROM 'Table'
WHERE
'Table'[Project] NIN ( 'AB' ) ;
You can find useful article by Alberto Ferrari and Marco Russo:
https://www.sqlbi.com/tv/auto-exist-on-clusters-or-numbers-unplugged-22/
If it is only converting DAX queries if your connecting your sql analysis services to in DAX studio. because it is not working for PBI file data
I have three different tables. I want to select different columns from each of the tables and create one table based on some filters. Have a look at the following dax expression:
FILTER(DISTINCT(SELECTCOLUMNS(Test_Table,"site_key",[site_key],"is_active",[is_active])),[is_active]=TRUE&&[dbsource]=="DB2")
As you can see, I've selected olumns from Test_Table.
Firstly, How can I add columns from the other two tables?
Secondly, please note that these tables are related so I've created a relationship between them based on relevant IDs.
Will I have to create a natural join in DAX as well?
As I mentioned in the comment, SUMMARIZECOLUMNS can probably get you what you're looking for. It might look something like this in your case:
SUMMARIZECOLUMNS (
Test_Table[site_key],
Test_Table_2[industry_group],
Test_Table_2[country],
Test_Table_2[state],
FILTER ( Test_Table, [is_active] = TRUE && [dbsource] = "DB2" )
)
I have a table that looks like this. Table Screenshot
"FY 20-21 (Budgeted)", "FY21 Approved Budget" and "Revised Budget" are columns coming from three different data sources that I appended into one table. There isn't always data in all three of these columns, so I created a new column to consolidate the data with the following formula:
FY 20-21 Budget =
if(
and(
isblank('Comprehensive Budget'[FY 20-21 (Budgeted)]),
isblank('Comprehensive Budget'[FY21 Approved Budget])
),
'Comprehensive Budget'[Revised Budget],
if(
and(
isblank('Comprehensive Budget'[FY21 Approved Budget]),
isblank('Comprehensive Budget'[Revised Budget])
),
'Comprehensive Budget'[FY 20-21 (Budgeted)],
'Comprehensive Budget'[Revised Budget]
)
)
If both Budgeted and Approved are blank, use Revised.
If not, if both Revised and Approved are blank, use Budgeted.
If not, use Revised.
But if you look on the screenshot, if NONE of the columns are blank, it gives me Revised plus Budgeted. Where is the problem in my formula?
I have added your data here and found your Measure is perfectly returning your expected data as shown in the below image-
I Guess, there are some Aggregation issue in your case. You can right click on all column in the table visual properties and select Don't Summarize from the options. This should solve your issue I hope.
I have the following tables & relationships in our pbix report:
For some obvious reasons, I need to have a relationship (non-active) between Dates[date] and Table2[T2Date]. However, doing so causes data fluctuation to measure 'Total Amount' in Table1.
Here are some screenshots:
Before Relationship (Dates[date] - Table2[T2Date]):
After Relationship (Dates[date] - Table2[T2Date]):
I need to understand why this difference is coming up and how the relationship is causing it since the measure uses a different relationship.
For reference, I am attaching the pbix report.
https://drive.google.com/open?id=1XknisXvElS6uQN224bEcZ_biX7m-4el4
Any help would be appreciated :)
The link that #MikeHoney gives has really useful information on the subtleties of relationships and does relate to this problem (do watch it!), but this issue is not ultimately related to bidirectional filtering in particular. In fact, I can reproduce it with this simplified relationship structure:
The key thing to note here is that when you attach Table2 to Dates, since Table2 contains T2Date values that don't match to any Date[date], this creates an extra row in Dates with a blank date which you can notice in your filter on 6. Year when that relationship exists (active or inactive). Filtering out that that blank in the 6. Year filter would work, except that in your measure, you use ALL(Dates) to strip all filtering done on that table.
There are multiple ways to resolve this discrepancy, the easiest being replacing ALL with ALLNOBLANKROW. If you used ALLSELECTED that would also work in conjunction with filtering out blanks on your report-level filter on 6. Year.
Cleaning up some items not relevant in this context and changing ALL to ALLNOBLANKROW, your total measure can be more simply written as:
ALLNOBLANKROW =
VAR EndServiceDate =
MAX ( Dates[Date] )
RETURN
CALCULATE (
SUM ( Table1[Net Amount] ),
FILTER (
ALLNOBLANKROW ( Dates ),
Dates[Date] <= EndServiceDate
),
Table1[Flag2] = 1,
Table1[Flag] = TRUE ()
)
Results with no 6. Year filter and with two measures, one using ALL and one using ALLNOBLANKROW:
Notice that every row in the ALL column has been reduced by -7,872.01. This is the sum of all the Net Amount values that don't match to any dates in the Dates table. If you remove the relationship from Dates[date] to Table2[T2Date] then the blank row no longer exists and both of these will match the ALLNOBLANKROW version.
Setting the Cross Filter Direction to Both on any relationship is a bit risky - you essentially hand over control of the runtime query designs to the Power BI robots. There's then a risk that they will come up with a "creative" query design that is unexpected.
There's some insight into how this happens in a recent talk by Alberto Ferrari:
https://www.sqlbi.com/tv/understanding-relationships-in-power-bi/
I'm sure you'll agree it's quite terrifying.
Looking at your info, I expect you can avoid those traps by changing the Cross Filter Direction to Single, for the relationship from MonthYear to Dates.