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I have a table with users, each user occurs multiple times as I project values into the future and each row represents a user/year.
UserID | Year | Other Values |
1 | 2020 | Complex projected values. |
1 | 2021 | Complex projected values. |
I have a dropdown slicer so one year is always selected and I can treat my model as distinct in all of the generated reports.
I have another table that describes User Siblings, of which each user may have several.
UserID | SiblingID | Other Values |
1 | 2 | Basic values. |
1 | 3 | Basic values. |
They have a many:many relationship with filtering flowing from the first.
If I try to represent these linked values in a table report, then I get x by x rows for each row, as the filter on year is ignored.
UserID | Year | SiblingID |
1 | 2020 | 2 |
1 | 2021 | 2 |
1 | 2020 | 3 |
1 | 2021 | 3 |
I would have expected simply
UserID | Year | SiblingID |
1 | 2020 | 2 |
1 | 2020 | 3 |
If I choose to sumerise the data, such as count value, then the rows are consolidated. The problem is I wanted to us a measure that combines various elements from the linked table and I have no option to sumerise the measure. Oddly I get a number of blank values and one acurate measure.
Is this in anyway expected behavoir of a many to many relationship? I know the model would ideally not have any many to many relationships but I moved to projecting values into a common table as the complexity of trying to achieve the same with measures was horendous I was constantly fighting the lack of row context.
Some posts suggest having an intermediary table with say unique user ids in and having two many to one relationships focusing on this. But would I again have a problem focusing the filter context down to rows of the linked table?
Here is a link to a very simple example, it actually works fine so I know that this is not expected behaviour. If I find a solution I will post it.
https://drive.google.com/file/d/1-5AHnXQB8qGaSYz87vrU4nMdOTzzM8Bv/view?usp=sharing
Solved! Go to Solution.
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