Memory Alocation Error for Power BI desktop

[PowerBI_DevUser]

Hi Team,

I created a report which was working fine when I open the report and refreshed the report in the local , but after updated the latest power bi desktop version(Still I got October Version only) couple of days back , I got the below error

Memory error: Memory Allocation failure . Try simplifying or reducing the number of queries.

For another report which started to create newly, I got the similar type of issue which was not happened previously

Failed to save modifications to the server. Error returned: 'OLE DB or ODBC error: [DataSource.Error] ERROR [HY000] [Microsoft][Snowflake] (25) Result download worker error: Worker error: [Microsoft][Snowflake] (25) Result download worker error: Arrow chunk download failed max retries done: Unformatted ErrorException: {Message="LocalizableDiagnostic(MessageKey=\"SFRestRequestFailed\" ComponentID=102 Params=[***, CURLerror (curl_easy_perform() failed) - code=28 msg='Timeout was reached'])" ComponentID=102' RowNumber=UNKNOWN ColumnNumber=UNKNOWN DiagState=63}

[rajendraongole1 · Accepted solution]

Hi @PowerBI_DevUser - You're encountering—both the memory allocation issue and the Snowflake download failure—are commonly associated with Power BI's handling of large datasets and recent updates or changes to the Power BI Desktop version.

Limit Data Pulls: Check if you can reduce the data volume imported in your queries by applying filters to limit rows or columns, especially if you’re working with high-cardinality or wide tables.
Disable Unnecessary Columns: If your queries pull unnecessary columns, remove them to reduce the memory load.
Optimize Query Steps: Simplify any complex transformations in Power Query and try to reduce the number of transformation steps.

Power BI Desktop has internal limits for memory, but you can increase the working memory allowance.
Enable Large Dataset Storage Format: If you’re using Premium capacity, switch to a large dataset storage format in the Power BI service, which helps with memory management.

Use DirectQuery Mode (if possible): Instead of loading all data into memory, consider using DirectQuery for Snowflake. This method reduces memory load in Power BI but does increase dependence on Snowflake performance.
Switch to Import Mode for Aggregated or Smaller Datasets: For large datasets, create aggregated tables in Snowflake that you can pull in Import mode. This setup reduces Power BI's memory demands and enables faster interactions.
Troubleshoot OLE DB/ODBC Connection and Timeout Settings
Extend Connection Timeouts: In Power BI, go to File > Options and Settings > Data Load, then increase the timeout settings to prevent connection timeouts with Snowflake.
Update Snowflake ODBC Driver: Ensure that your Snowflake ODBC driver is the latest version. Compatibility between Power BI Desktop and the driver can impact stability.
Snowflake Query Execution Timeout: Check if there are timeout or resource governor settings in Snowflake that might limit query execution time. Consult with your database administrator if this persists.

These actions should help mitigate the memory and connectivity issues, but if they persist, updating your Snowflake driver settings and exploring DirectQuery for large data sources may provide more stability