![]() No extra work is needed to fulfill the dependency for screen 2, and therefore there's no performance impact. Then screen 2 will have already been loaded in memory when screen 1 is to be loaded. There's an exception: In the previous example, imagine that the only way to display screen 1 is by navigating from screen 2. In some cases, you can use a global variable or collection to share information between screens. This dependency chain can cause many screens to be loaded.įor this reason, avoid formula dependencies between screens. Imagine screen 2 has a dependency on screen 3, which has another dependency on screen 4, and so on. Now screen 2 must be loaded to fulfill the dependency before screen 1 can be displayed. This optimization can be hampered if, for example, screen 1 is loaded and one of its formulas uses the property of a control from screen 2. To improve performance, the screens of an app are loaded into memory only as they're needed. You can also use this technique with the Defaults and User functions. This method is more useful for data such as contact information, default values, or user information that doesn't change frequently. Set(CustomerAddress, CustomerOrder.Address) As shown in the following example, the data is retrieved from the source once, and then referenced locally until the user closes the app. This technique optimizes performance if the data probably won't change during a session. Use the Set function to cache data from lookup tables locally to avoid repeatedly retrieving data from the source. We recommend using App.StartScreen property since it simplifies app launch and boosts the app's performance. With this change, the app fetches the tables in parallel, as shown in the following image. You can enclose the same formula in the Concurrent function to reduce the overall time that the operation needs: Concurrent(ĬlearCollect( Product, '.' ),ĬlearCollect( Customer, '.' ),ĬlearCollect( SalesOrderDetail, '.' ),ĬlearCollect( SalesOrderHeader, '.' )) You can confirm this behavior in the Developer Tools for your browser, as shown in the following image. Without the Concurrent function, the following formula loads each of four tables one at a time: ClearCollect( Product, '.' ) ĬlearCollect( Customer, '.' ) ĬlearCollect( SalesOrderDetail, '.' ) ĬlearCollect( SalesOrderHeader, '.' ) More information: Concurrent function in Power Apps Also, use the Concurrent function to load data sources simultaneously this can cut the amount of time an app needs to load data in half. Use the ClearCollect function to cache data locally if it doesn't change during the user session. Some controls (such as PDF viewer, data table, andĬombo box) pull in large execution scripts and take longer to render. InĪddition, you might want to reduce the number of control types on the same screen. ![]() You can, in some cases, achieve the same result and have the app start faster if you use a gallery instead of individual controls. The more controls you add, the more generation time Power Apps needs. Power Apps generates an HTML document object model to render each control. Limit the number of controlsĭon't add more than 500 controls in one app. Each added connection is counted individually in this limit, irrespective of the connected data source type-such as Microsoft Dataverse or SQL Server tables, or lists created using Microsoft Lists. Your app is more likely to take longer than 15 seconds to return data if it frequently requests data from more than 30 connections. You can quickly measure your app's performance by turning on Developer Tools in Microsoft Edge or Google Chrome while running the app. ![]() As an app runs, each connector requires CPU resources, memory, and network bandwidth when the app requests data from that source. Apps prompt new users to sign in to each connector, so every extra connector increases the amount of time that the app needs to start. Limit data connectionsĭon't add more than 30 connections in one app. You can also benefit by following the tips and best practices in this article to boost the performance of apps that you create. In the previous articles, you learned about the execution phases and data call flow, common sources of slow performance, and common performance issues and resolutions in canvas apps.
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