Operation was canceled because there wasn't enough memory

[Macc-PA]

Hi,

We have just started to use PBI Embedded capacity are getting this error when refreshing the report.

The report refreshes fine in the normal PBI Service however fails in an embedded capacity which has an A1 SKU which has 3GB max and 1GB per query.

The report according to DAX studio occupied 42MB.

The error message simply says 'Either reduce the memory footprint of your dataset by doing things such as limiting the amount of imported data, or if using Power BI Premium, increase the memory of the Premium capacity where this dataset is hosted. '

I need help with whats going on and what can be done about it to reduce the memory and what  type of refresh operations are consuming so much memory above the limits.

Thanks

[Macc-PA]

Thanks for the response, are  there any tools or tips we can use to target the strategies that you have provided to the tables/queries that are utilising the most memory during the refresh?

[Anonymous]

Hi @Macc-PA ,

When the memory consumed during a refresh operation exceeds the available capacity, you will receive an error message like this. Although the report occupies only 42MB according to DAX Studio, the refresh operation may consume much more memory than the size of the dataset itself.

Here are a few steps to reduce memory footprint and manage refresh operations more efficiently:

1. Implementing an incremental refresh strategy can significantly reduce the amount of data processed during each refresh operation, thereby reducing memory consumption. This method refreshes only the most recent data that has changed. For more details on this setting, read the following documentation:
   Incremental refresh for semantic models and real-time data in Power BI - Power BI | Microsoft Learn
2. See if there are any unnecessary columns or tables in the data model that can be deleted. If the report does not require detailed transaction level data, consider aggregating data at a higher level. The following article provides several strategies for reducing the size of the data model:
   Data reduction techniques for Import modeling - Power BI | Microsoft Learn
3. If the dataset does not need to be updated in real time, consider adjusting the refresh frequency to less busy times or less frequent intervals. This helps manage the load on the capacity.

Best Regards,

Ada Wang

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