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gebberryOffice's avatar
gebberryOffice
Advocate I
7 years ago
Solved

Dataflows don't support Query Folding?

Working with a DataFlow on premium... because there is too much data to manipulate (100MM+rows) locally.   Issue is it seems on service dataflows don't support query folding?  hunh?     I created...
  • v-xicai's avatar
    7 years ago

    Hi gebberryOffice ,

     

    1. For your first question:>>Issue is it seems on service dataflows don't support query folding?

     

    Checking if query folding takes place is simply done by right-clicking on a step in the query editor and by verifying if the "view native query" option isn't greyed out.

    If "View Native Query" is greyed out, it means query folding isn't taking place for that step.

     

     

     

     

     

     

     

     

     

     

     

     

     

     

    You can refer to the links: https://www.mssqltips.com/sqlservertip/3635/query-folding-in-power-query-to-improve-performance/

                                                   http://blog.pragmaticworks.com/power-bi-checking-query-folding-with-view-native-query

     

    Please understand that this link is provided “AS IS” with no warranties or guarantees of content changes, and confers no rights.

     

    1. For your second question:>>How can I edit my Dataset/Model?  Without having to wait so long.

     

    You can optimize your model from respects below:

    • Tables or columns that are unused should be removed if possible. 
    • Avoid distinct counts on fields with high cardinality – that is, millions of distinct values.  
    • Take steps to avoid fields with unnecessary precision and high cardinality. For example, you could split highly unique datetime values into separate columns – for example, month, year, date, etc. Or, where possible, use rounding on high-precision fields to decrease cardinality – (for example, 13.29889 -> 13.3).
    • Use integers instead of strings, where possible.
    • Be wary of DAX functions, which need to test every row in a table – for example, RANKX – in the worst case, these functions can exponentially increase run-time and memory requirements given linear increases in table size.
    • When connecting to data sources via DirectQuery, consider indexing columns that are commonly filtered or sliced again – this will greatly improve report responsiveness.  

    For more guidance on optimizing data sources for DirectQuery, see DirectQuery in SQL Server 2016 Analysis Services.

     

    You can refer to the links to learn more: https://docs.microsoft.com/en-us/power-bi/power-bi-reports-performance,

     

    Best Regards,

     

    Amy

     

    If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.