Question

How do you troubleshoot slow data model refreshes, especially when custom code (M or DAX) is involved?

I'm working on a project involving Power BI with a data model that has become increasingly slow to refresh. The model includes custom code written in M and DAX for data transformations and calculations. These customizations are essential to the analysis but appear to be affecting refresh times significantly.

Are there effective troubleshooting methods to pinpoint and resolve slow data refreshes in Power BI, especially when custom M or DAX code is involved? Any guidance on best practices or techniques to improve refresh performance would be highly appreciated!

Answer 1

If the data model refresh in Power BI takes too long, follow the methods below, which include using custom M or DAX codes.

1. Use Power Query to investigate the performance of Data Models

• Load Data in Parts: Load a smaller portion of data first and see whether the issue is attributed to volume or complexity.
• Equipment Review: Disable each transformation step in Power Query to Net one by one. This can help identify which processes take longer. Where applicable, enhance the efficiency of queries through the use of Tables. Buffer and cut the steps performed where they are not necessary.

2. Improving DAX calculations

• Check on Dependencies: Use DAX Studio to look for measure dependencies and eliminate the ones that do not need to be calculated again.
• Make Use Of Variables: Utilize variables whenever possible in DAX measures, which improves the speed of storing intermediary results.
• Improve Relationships: Be sure to populate a model with correct relationships, avoiding many-to-many types of relationships.

3. Manage Resource Consumption

• Performance Analyzer: Turn on the performance Analyst tool within Power BI to find any slow visuals.
• Of Review Resources: Observe CPU and Memory activity when refreshes are in progress, especially in Power BI Premium. This includes resource checking.

4. Best Practices for a Refresh

• Avoid Full Refresh Strategies: In large datasets, it is possible to set incremental refresh to avoid excessive processing times.
• Power BI dataflows: Implement ETL within Power BI dataflows to lessen the report's load and make the refresh processes much faster.

Applying these strategies will help one diagnose the reasons behind the slowness of the data model refresh in Power BI and improve performance.

Answer 2

To start troubleshooting the slow data-model refresh in Power BI with custom M or DAX codes, one must first identify the points of bottleneck in the refresh process flow. Here are some key steps:

Analyze Query Performance (M Code): Power Query has its "Diagnostics," and with these tools, you can check the execution times and identify slow steps within the transformations. Some slow steps may include inefficient steps like joins or data type conversion. Consider "Table.Buffer" to optimize large datasets' data processing.

Optimize DAX Calculations: Analyze DAX measures for efficiency. They must not recalculate unnecessarily and avoid using complex expressions in visuals. To avoid such recalculating, use variables (VAR) to hold intermediate results to reuse in DAX measures.

Optimize Data Model Design: The size of the model should be reduced by removing all unnecessary columns and tables. Use the correct datatypes (e.g., an integer instead of text for keys). Such as implementing incremental refresh if your dataset is large so that new ones alone would be processed for every refresh.

Check Data Source Performance: The performance of this source can sometimes be an issue with the source itself, not even with the code. Performance-check data source sources such as SQL Server or API. Ensure all can process large queries.

Monitor Refresh Logs: Power BI uses refresh history logs to provide detailed information about each refresh step. Delve into the refresh history logs for those bits and pieces, and check where the delays occur.

By systematically analyzing and optimizing these areas in development, one can vastly improve refresh performance in Power BI, even with M and DAX code complexities.