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Hi everyone,
For the past couple weeks we are running into a timeout error for a specific dataset in our premium workspace. It does not happen everyday, but the frequency of the error is increasing.
It's a datset with incremental refresh that loads a lot of tables (most of them in dataflows, a couple by querying athena), it stores data for the past 8 quarters and refreshes the last 2.
I think we may have a bit of performance gain if we avoid loading some unused columns, but are there any other things we can do to avoid reaching the timeout error (usually token expired)?
Thanks!
Removing unneeded columns is always a good step. You could also consider moving to monthly partitions instead of quarter. The other thing would be to try to optimize your M code, if you are doing any complex transformations, merges, etc. If so, you can share your M code here and get suggested optimizations from the community.
Pat
It can be reallly challenging to understand what portion of a Dataset refresh is causing slowness. I've relied heavliy on the method described in this blog post to analyze my refresh and pinpoint bottlenecks.
https://dax.tips/2021/02/15/visualise-your-power-bi-refresh/
Also, using the "Best Practice Analyzer" in Tabular Editor is incredibly helpful at guiding you (and helping you fix) inefficiencies in your datasets.
https://www.sqlbi.com/tools/tabular-editor/
I've been able to reduce model sizes by half (!) and reduce refresh times by more than half (!) just by using the two tools above.
Thanks a lot! I will review this and see what I find 🙂