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blairtoews's avatar
blairtoews
New Member
4 years ago
Solved

Ignoring Incremental Refresh Warning - Query Folding

Hello,


I know many have asked questions surrounding the "Unable to confirm if the M query can be folded..." warning when setting up incremental refresh.  Hopefully this question is not redundant:

 

I've written a native query, using temporary tables, and have have set up the RangeStart and RangeEnd parameters and have implemented incremental refresh successfully (based on tests I've run, uploading the report to my company's appspace / gateway), while ignorning the query folding warning message. 

My question is: "If I'm getting the results that I'm expecting, is it a big deal that the query cannot be folded?"

I've heard others say that this is just a prompt / warning and its not going to prevent incremental refresh from happening, so why not ignore it?  If my tests prove successful and the incremental refresh process is working, are there any other inintended consequences / pitfalls that I might experience as a result of my native query not being foldable?

Thanks!

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  • lbendlin's avatar
    lbendlin
    4 years ago

    You're good.  Not having query folding may slightly increase the processing overhead when partitions are rearranged at the date interval boundaries (new year, new quarter etc) to move data from hot to warm to cold partitions. 

     

    In Datasets you can apply the RangeStart/RangeEnd filter as soon as it makes logical sense in your query. If you already do it in the native query then that is optimal.  Unfortunately this cannot be applied the same way in dataflows.

2 Replies

  • lbendlin's avatar
    lbendlin
    Super User
    4 years ago

    You're good.  Not having query folding may slightly increase the processing overhead when partitions are rearranged at the date interval boundaries (new year, new quarter etc) to move data from hot to warm to cold partitions. 

     

    In Datasets you can apply the RangeStart/RangeEnd filter as soon as it makes logical sense in your query. If you already do it in the native query then that is optimal.  Unfortunately this cannot be applied the same way in dataflows.