Forum Discussion
Defragment not working
If defragmenting your table doesn't reduce its size, then there probably isn't anything else you can do. Are you trying to reduce the size of your table? If so then you will need to look at things like splitting datetime columns into separate date and time columns or rounding numbers in decimal columns, both of which can reduce the cardinality of your columns. See articles like this for more details https://www.sqlbi.com/articles/optimizing-high-cardinality-columns-in-vertipaq/
Thanks a lot cpwebb
Yes, I'm currently trying to reduce the size of tables. Despite removing all unused columns, optimizing calculated columns, and even transferring some columns to PQ and SQL Server, I continue to face the following error, although DAX studio VertiPaq shows only 800MB used memory and we have 5GB RAM with our embedded capacity:
"Resource Governing: This operation was canceled because there wasn't enough memory to finish running it."
In the Power BI service, I've scheduled a refresh every 30 minutes with incremental refresh enabled. Interestingly, I sometimes don't see this error for up to 10 consecutive hours (which means 20 refreshes). But, intermittently, the refreshes begin to fail, sometimes even failing twice in a row.
These failed refreshes consistently occur between 3 minutes and 3 minutes 30 seconds. So, if a refresh exceeds 4 minutes, it usually completes successfully.
I'm struggling to identify the root cause of this issue. If it is indeed related to calculated columns, I will transfer more columns to SQL and Power Query. But, I'm really not sure where to look.
Thanks for your time!
You're running into something called the Command Memory Limit, which is documented here: https://learn.microsoft.com/en-gb/power-bi/enterprise/troubleshoot-xmla-endpoint#resource-governing-command-memory-limit-in-premium It's also something I will address in an upcoming blog post in a series I started here https://blog.crossjoin.co.uk/2024/04/28/power-bi-semantic-model-memory-errors-part-1-model-size/ (reading this post will provide some useful background).
If you're using an A2 with 5GB of RAM and your existing model is 800MB, then you have 5GB - (800MB plus some extra memory used by queries and sessions) left for your refresh, so say 4GB. The next thing you should do is run a Profiler trace against your model (see https://blog.crossjoin.co.uk/2020/03/02/connecting-sql-server-profiler-to-power-bi-premium/ for how to do this) and capture the Command End events when you run a refresh. From this you'll be able to see the peak memory used during the refresh as a whole and for just Power Query (see https://blog.crossjoin.co.uk/2023/04/30/measuring-memory-and-cpu-usage-in-power-bi-during-dataset-refresh/) and for just Power Query for individual table partitions (see https://blog.crossjoin.co.uk/2023/04/02/identifying-cpu-and-memory-intensive-power-query-queries-during-refresh-in-the-power-bi-service/). My guess is that the peak memory for the refresh as a whole is going over 4GB. If so, then you need to look at the Power Query memory usage number for individual partitions and see if there are any memory hungry Power Query queries - if so, they need to be tuned. If not then it's likely to be your calculated columns that are the problem still. Reducing the amount of parallelism during refresh may also help reduce the peak memory usage (see https://blog.crossjoin.co.uk/2022/10/31/speed-up-power-bi-dataset-refresh-performance-in-premium-or-ppu-by-changing-the-parallel-loading-of-tables-setting/).
If peak memory for the refresh is a lot less than 4GB then it's possible that the problem isn't the refresh but that users are running memory-hungry queries during the refresh. Running a Profiler trace during a refresh and looking for Query Begin/End events will tell you if queries are being run; there is a new Profiler event coming very soon which will tell you more about query memory usage but before that happens it's impossible to know how much memory a query consumes in the Service but you should see it happen easily in Power BI Desktop by looking at what happens in Task Manager when you view or interact with a report. If this is the problem then you'll need to tune your model and measures to reduce memory usage. This antipattern is a very common example of DAX that can cause memory spikes: https://xxlbi.com/blog/power-bi-antipatterns-9/
If you are able to post some screenshots of your Profiler traces and the memory usage numbers please let me know - I'm curious to see what you find!
HTH,
Chris
Thank you very much cpwebb . Very thorough and great information. I really appreciate it!
I used Profiler for two different datasets, and the following are the results:
1) The refresh failed for this dataset:
PeakMemory: 4.5GB , MashupPeakMemory: 1.49 GB
I checked the 'progress report end' for all the tables and observed that the MashupPeakMemory for most tables falls between 120MB and 250MB, except for one table, which was 1.4 GB!
2) This is another dataset with an incremental refresh, and the refresh failed:
PeakMemory: 3.95 GB , MashupPeakMemory: 2.12 GB
And same story with this dataset, as MashupPeakMemory for one of the tables was 1.65 GB!
I refreshed the same dataset again, and it refreshed successfully with a PeakMemory of 3.82GB, as follows:
As you mentioned, I think I need to tune the queries in Power Query for this table that is eating lots of memory, correct?
This is the query for that table:
Thanks again! much appreciated!
Thanks for the detailed information. I'm still learning a lot about this topic and it helps to have a real life example to work though!
It does sound like there's some tuning needed in your Power Query queries. Bear in mind that for the Command End for the refresh you're seeing peak values for all operations, so if you're refreshing 6 tables in parallel then that peak value could be 6 * (a reasonable amount of memory for a single table). From my limited experience I would say that 200-400MB is a reasonable value for memory used by Power Query for a single table.
That said, a MashupPeakMemory of 1.4GB or 1.6GB is definitely too high. For the Power Query query you posted, how much of that folds? Do the Merge and the Group operations fold? They would be the obvious memory-hungry operations; if they do, the next thing to investigate would be the steps where you're changing data types; moving them to the end of the query, or at least after the Merge, could help.