How Can I Fix this DAX and Improve it for Performance?

[bvy]

Very simple dataset here: 

- Data - Each record represents a data point with (X, Y) coordinates; ID is unique to each point. 

ID	Level	X	Y
1	Level A	50	35

- Neightbors - A record exists in this table for each pair of ID's that are in mutually close proximity. For example the following might exist in the table, indicating that points 2 and 5 are arbitrarily close. A second row with 5 then 2 would NOT appear however: 

ID1	ID2
2	5

I want to add a DAX measure to the Data table indicating, for each ID, the the number of nearby points (i.e. the number of records in the Neighbors table). Here is my DAX: 

ClusterMeasure =

VAR _this_id = MAX(Data[ID])

RETURN COUNTROWS(FILTER(ALLSELECTED(Neighbors), [ID1] = _this_id || [ID2] = _this_id))

There are a few issues.

1. I want the DAX to respond to filter selections. I have a slicer called Level, so when Level A or Level B is selected, the DAX should recalculate to count only the points in the filter context. 

2. Isolated points should ideally show a ClusterMeasure of 0. 

3. The DAX performs somewhat slow in our scaled up production application. Is there a better way to write it? 

Thank you. 

(PS: I had a PBIX ready to upload but sadly these forums don't allow it.) 

EDIT/PPS: I NEED A PURE DAX SOLUTION BY THE WAY. This exists in a tabular model, so altering relationships, for example, may not work. 

[bvy]

Company policy doesn't allow it. I think there's enough info in the original post to make suggestions though. 

Can anyone help? 

[OwenAuger]

Hi @bvy 

I am assuming that there are no relationships between Data & Neighbors, and that you prefer not to (or cannot) add any.

You could try a measure along these lines. This performs reasonably well in a test model at my end:

ClusterMeasure = 
VAR This_ID = SELECTEDVALUE ( Data[ID] )
VAR AllSelected_ID = CALCULATETABLE ( VALUES ( Data[ID] ), ALLSELECTED() )
VAR Neighbors1 =
    CALCULATE ( 
        COUNTROWS ( Neighbors ),
        Neighbors[ID1] = This_ID,
        TREATAS ( AllSelected_ID, Neighbors[ID2] )
    )
VAR Neighbors2 = 
    CALCULATE ( 
        COUNTROWS ( Neighbors ),
        Neighbors[ID2] = This_ID,
        TREATAS ( AllSelected_ID, Neighbors[ID1] )
    )
RETURN
    Neighbors1 + Neighbors2

This measure ensures that both ID1 & ID2 are in Allselected_ID.

Neighbors1 has This_ID as ID1 and Neighbors2 = has This_ID as ID2.

Does this give expected results for you, and is performance ok?

Regards,

Owen

[bvy]

Hi @OwenAuger. This actually works pretty well. Can you see a way to adjust it to return 0 for data points that have no nearby neightbors? That's the only thing that's missing. Thanks so much. 

[OwenAuger · Accepted solution]

That's good to hear.

In this case, I think you can get away with essentially adding zero to the measure.

In more complicated situations this can cause unwanted zeros to appear, and may require a more complex solution (see here).

Maybe to be on the safe side we could include a condition that SELECTEDVALUE ( Data[ID] ) is not blank to the overall measure as well (which ensures we have filtered on a single ID):

ClusterMeasure =
VAR This_ID =
    SELECTEDVALUE ( Data[ID] )
RETURN
    IF (
        NOT ISBLANK ( This_ID ),
        VAR AllSelected_ID =
            CALCULATETABLE ( VALUES ( Data[ID] ), ALLSELECTED () )
        VAR Neighbors1 =
            CALCULATE (
                COUNTROWS ( Neighbors ),
                Neighbors[ID1] = This_ID,
                TREATAS ( AllSelected_ID, Neighbors[ID2] )
            )
        VAR Neighbors2 =
            CALCULATE (
                COUNTROWS ( Neighbors ),
                Neighbors[ID2] = This_ID,
                TREATAS ( AllSelected_ID, Neighbors[ID1] )
            )
        RETURN
            Neighbors1 + Neighbors2 + 0
    )

Regards,

Owen

[bvy]

Working well. Thanks so much for taking the time. 

[Fowmy]

@bvy 

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