Question

How do you deal with report performance degradation after adding additional visuals or pages?

After adding a few more visuals and pages to my Power BI report, the overall performance has noticeably degraded. The report is taking longer to load, and some visuals are slow to update. What are the best practices for managing performance as reports grow more complex in Power BI, and how can I address performance issues caused by additional visuals or pages?

Answer 1

As Power BI reports become more detailed over time, performance problems arise, and certain best practices for performance management must be adopted.

Constrain the Number of Visuals: Each visual in Power BI is rendered as a separate entity, which means that the addition of visuals increases the processing resources required, which may result in the lagging of a report. Instead of this, one visual which incorporates all the insights can be used. For instance, one can utilize a multi-row card to show different KPIs together in a smaller space, thereby minimizing the use of visuals. Another option is to employ summarized visuals such as tables and matrices with conditional formatting in order to display more information with fewer visuals.

Optimizing DAX Calculations and Query Performance: Over time, as more reports are made, complicated DAX queries can become a pain to performance. Investigate ways to improve DAX function performance by applying measures only where they are warranted and eschewing overly difficult mathematics. Furthermore, in order to spare DAX calculations for improvements in loading times, examine either fitting values on certain metrics already in Power Query or Aggregating data.

Use the Appropriate Kind of Visual: Choosing appropriate visuals affects the report's performance. Donut charts, for example, are preferred to pie charts since, when well designed, they portray size without intrusive borders, as pies often do. Scatter graphs are ideal for plotting the relationship between two variables, where multiplying data points comes into play. However, if many data points are added, scatter charts may cause performance degradation.

Answer 2

At first, the optimization of the data model should be done with regard to the performance degradation experienced with Power BI after adding some visuals or pages. Always look to the limitation as to how many visuals can fairly be placed on a page since all the visual objects add to the loading time; use simple visuals, like tables or matrices, instead of complex charts where possible; avoid too many slicers or filters; and minimize the DAX calculations on visuals, especially those recalculating frequently. Use aggregate tables or pre-summarized data to reduce the dataset size. In the case of very large reports, enable query folding for the Power Query and reduce other unnecessary relationships. Finally, performance analysis tools such as the Performance Analyzer can be used to discover bottlenecks.

Answer 3

There are various reasons why performance issues may arise within Power BI once more visuals or pages are incorporated into a report. However, several solutions can be employed to enhance performance without compromising on usability. Here are some of the recommended solutions:

1. Use Fewer, More Meaningful Graphics, and Do Not Overcrowd Pages

One of the sweet perspectives to enhance the performance of a report is reducing the number of visuals contained in a single page. Every visual contains a query on data, and having many such visuals increases the processing burden. Where possible, try to combine and edit visuals rather than placing many of them – it can positively shorten and fasten the overall report. Also, take into account the type of visual used; simpler visuals tend to be less expensive, whereas layered visuals such as scatter charts and maps are costly.

2. Improve the Performance of DAX Measures and Eliminate Unneeded Calculated Columns

Another time-intensive part of the report can be complex DAX measures, which are often updated on many visuals in real-time. When possible, try to perform an aggregation or computation closer to the source; otherwise, measures may be preferred rather than calculated columns. That lightens the burden on Processing in Power BI. Also, do not implement the same DAX in different visuals, but implement it once and reference it as required.

3. Implementing Data Shrinkage Strategies

Power BI contains a number of data shrinkage strategies, which allow for reducing the amount of data that is loaded into the data visualizations. Employ such features as the Top N filter that restricts the amount of information represented in the visualizations or apply a data filter at the report data model level. It may also be useful to turn visuals to “import” mode instead of “DirectQuery” for very large datasets since DirectQuery connects directly to the source database and retrieves data in real-time, which may result in the sluggishness of the report if the data source is not well optimized.

4. Use Performance Analyzer to Identify Bottlenecks.

One of the most well-known resources of Power BI is the Performance Analyzer, which is used to identify performance bottlenecks that may be experienced within the application. This tool allows you to see how much time it takes to visualize each report element and process its data, thus allowing you to find report elements that make the report slow. After these report elements have been identified, it is then possible to either optimize them or change them, if possible, change the queries, or, if the case allows, consider reducing the size of the data.

Using these approaches, you will be able to solve problems related to performance as you handle larger amounts of data in your Power BI report, allowing users to enjoy the speed of the report without sacrificing its complexity.