Hi,
My Power BI runs very slowly when creating the visualizations. Just clicking to open a data model to select a field takes ages.
Are there please any suggestions to fix this issue?
Thank you
Rosemary
Hello Rosemary,
I suggest you check the minimum requirements in this page. The more data you are working with the more RAM is beneficial.
If your computer has adequate specifications then perhaps there are some other background services running that makes your computer sluggish. If you can, a good test would be to open your .pbix file in another computer and see if it behaves the same or not.
Br,
Anders Sehlstedt
In addition to Anders suggestions, you should install the 64-bit version of Power BI Desktop if you can. This will allow Power BI to use more of your PC's memory.
Thank you both.
Hello, I have done all of the above but my report is growing out of control. It is at 26 megabytes now, what actions are good to keep size down? It grew after I added a bunch (10) of new datasources and created measures on top of them to my original file that was appx 4 megabytes. Are there measures that are more efficient than others? I could run a file where I reverse my new measures and datasources to see which ones are dragging it down, but if anyone has more insight that would be great to know!
Kind regards,
Vladi
Hello Vladimir,
There are some tips for you at this site to check and see if it helps you.
Good luck.
Br,
Anders
Hi Vladimir,
I'm assuming you're working with Power BI Desktop, in which case the link Anders sent won't apply as it's for Excel.
If your Power BI/Power Pivot measures are in the form of 'Calculated Columns' then I would move these to calculate in the query. That said measures themselves won't typically add MBs, it's the data, but moving calculated columns to the query will improve recalculation time and responsiveness.
Power BI and Power Pivot are good at compressing data in columns where there are a lot of duplicates, but when you have a lot of unique values, as is typical in a column containing numbers like sales amounts, then the compression can't work as well.
If you have columns containing a lot of decimal places, you could try rounding them to 2 decimal places to see if that helps. The bottom line is, only bring in the data you absolutely need. If you have columns or rows in your data tables that you don't need, then filter them out at the query stage.
Mynda
