Differences in performance between Python visuals as used in Power BI Desktop and Power BI Service can be raised due to various underlying reasons. Here are certain important causes and optimizations to improve
Performance in Power BI Service:
Reasons why Python Visuals will be Slower in Power BI Service:
Resource Allocation: The main reason is that while Power BI Desktop runs Python scripts in the local environment, which is dedicated to it for execution purposes, Power BI Service runs its scripts in a resource-strapped shared environment for running scripts, CPU, and memory.
Execution Time Limitations: Python scripts running on Power BI Service time limit. Scripts usually time out if they take longer than 5 minutes (5) minutes to run.
Data Size: Power BI Service may behave in a way that handles big data sets or more sophisticated queries than the local environment. Bigger in terms of data models Power BI Service would work with, that might take more time to process.
Network Latency: Power BI Service servers use remote servers for processing data and visualizations, which causes network latency, which isn't applicable in Power BI Desktop.
Optimizations and Best Practices:
Optimize Python Code:
Vectorized Operations: Use vector operation-capable libraries such as that of Pandas for wider access to running functionalities rather than iterating the data using Python loops.
Optimal Data Processing: Use filtering or aggregation before passing data to Python visuals to reduce the amount of data going through the Python script. The reduced data size could greatly improve overall performance.
Use of Preprocessed Data:
Preprocess the data in Power BI (e.g., aggregation, filtering) before giving it to Python. This would reduce the volume of data Python has to deal with.
Simplify Visuals:
Heavy computational requirements for some complex visuals or scripts may slow down. Break down the part into small pieces, and make use of simple Python visuals.
Alt Visuals:
If performance becomes an issue, one can consider using native built-in Power BI visuals for data display. These built-in visuals optimized for performance and scalability become highly recommended for Power BI users.
Monitor and Test in Service:
Use the Performance Analyzer in Power BI Service to monitor and specify which aspects of the report take the longest to load. This helps identify opportunities for optimization specific to the Power BI Service environment.
