Best Practices for Large PowerApps Solutions
Introduction
Building large-scale PowerApps solutions requires careful planning, efficient data management, and performance optimization. Without proper strategies, large apps can become slow, unresponsive, and difficult to maintain. This guide covers step-by-step best practices to ensure your PowerApps solution is fast, scalable, and easy to manage.
1. Planning and Architecture
1.1 Understand Business Requirements
🔹 Define the app’s purpose, key features, and expected user interactions.
🔹 Identify required data sources (SharePoint, SQL, Dataverse, APIs, etc.).
🔹 Determine the expected number of users and data volume to plan for scalability.
1.2 Choose the Right Data Source
✅ Dataverse – Best for enterprise solutions, relational data, and security.
✅ SQL Server – Best for structured data with complex queries.
✅ SharePoint – Suitable for small to medium datasets, but has delegation limits.
✅ Excel (Avoid for Large Apps) – Not scalable, slow for large data.
🔹 Tip: Use Delegable Data Sources like SQL or Dataverse to handle large datasets efficiently.
2. Data Management and Performance Optimization
2.1 Use Delegable Queries
🔹 Delegation allows PowerApps to push filtering and sorting operations to the data source.
🔹 Always use delegable functions (e.g., Filter, Sort, LookUp, Search) to prevent PowerApps from pulling unnecessary data.
🚨 Avoid Non-Delegable Queries:
Filter(Orders, Year(OrderDate) = 2023) // ❌ Non-delegable
✅ Use Delegable Alternative:
Filter(Orders, OrderDate >= Date(2023,1,1) && OrderDate <= Date(2023,12,31))
2.2 Optimize Data Calls with Collections
🔹 Instead of fetching data multiple times, store frequently used data in collections.
✅ Before (Inefficient Repeated Data Calls):
Filter(Orders, Status = "Pending")
✅ After (Optimized with Collection in OnVisible):
ClearCollect(PendingOrders, Filter(Orders, Status = "Pending"))
🔹 Use collections wisely – Do not store massive data in memory unless necessary.
2.3 Reduce API Calls Using Concurrent()
🔹 Fetch multiple datasets at the same time instead of sequentially.
✅ Example:
Concurrent(
ClearCollect(Orders, OrdersTable),
ClearCollect(Customers, CustomersTable),
ClearCollect(Products, ProductsTable)
)
🔹 This approach significantly reduces load time.
2.4 Use Indexed Columns in SharePoint
🔹 If using SharePoint, index columns to improve lookup and filtering speed.
🔹 Avoid filtering by non-indexed columns.
3. UI and User Experience Optimization
3.1 Use Lazy Loading in Galleries
🔹 Load only necessary data first, then fetch more on demand.
✅ Before (Loads Full Data Set at Once):
Gallery.Items = Orders
✅ After (Optimized Lazy Loading with Pagination):
Gallery.Items = FirstN(Orders, 50)
🔹 Improves performance by displaying only a subset of data at a time.
3.2 Minimize Controls on Each Screen
🔹 Too many controls increase rendering time.
🔹 Use Tab Containers or Multiple Screens instead of loading everything at once.
🚨 Avoid:
- Placing more than 500 controls on a single screen.
- Using complex nested controls inside galleries.
✅ Tip: Use screens with navigation instead of showing too much data at once.
3.3 Optimize Form Controls
🔹 Prefill dropdowns and choice fields using collections to avoid repeated data calls.
🔹 Use Edit Forms instead of multiple individual input fields to reduce overhead.
4. App Load Time Optimization
4.1 Reduce OnStart Load Time
🔹 Avoid loading unnecessary data in App.OnStart.
✅ Before (Slow App Load with OnStart Calls):
ClearCollect(Users, UsersTable);
ClearCollect(Orders, OrdersTable);
ClearCollect(Products, ProductsTable);
✅ After (Faster App Load Using OnVisible):
Screen.OnVisible = ClearCollect(Orders, OrdersTable);
🔹 Tip: Move data fetching to screens instead of OnStart for faster load times.
4.2 Use Variables for Static Values
🔹 Instead of recalculating values, store them in global or local variables.
✅ Example:
Set(CurrentUser, User().Email)
🔹 Now, use CurrentUser instead of calling User().Email repeatedly.
5. Security and Governance
5.1 Implement Role-Based Access Control
🔹 Use Dataverse Security Roles or SharePoint Permissions to restrict access.
🔹 If using SQL, filter data at the database level instead of in PowerApps.
✅ Example: Show records only for the logged-in user:
Filter(Orders, CreatedBy = User().Email)
5.2 Avoid Storing Sensitive Data in Collections
🔹 Collections store data in memory and can be accessed by users.
🔹 For security-sensitive data, fetch it dynamically instead of caching it.
🚨 Avoid:
ClearCollect(Admins, Filter(Users, Role = "Admin"))
🔹 Instead, use real-time LookUp() for sensitive data.
6. Testing and Debugging
6.1 Use Monitor to Analyze Performance
🔹 The Monitor Tool in Power Apps helps identify slow queries and errors.
🔹 Test app performance before deployment to optimize slow areas.
✅ How to Open Monitor:
- Go to PowerApps Studio
- Click on Advanced Tools → Monitor
- Run the app and analyze performance logs.
6.2 Avoid Unnecessary Recalculation
🔹 If an expression is used in multiple places, store it in a variable instead.
🔹 Use With() function for temporary calculations instead of recalculating them multiple times.
✅ Example Using With() to Avoid Repeated LookUp Calls:
With(
{ OrderStatus: LookUp(Orders, ID = ThisItem.OrderID).Status },
If(OrderStatus = "Completed", "Delivered", "Pending")
)
🔹 OrderStatus is stored only once instead of recalculating every time.
7. Deployment and Maintenance
7.1 Use Solution Packages for Deployment
🔹 For large apps, use Solutions in PowerApps to manage deployments.
🔹 Keeps track of version history, environments, and managed/unmanaged solutions.
7.2 Maintain Version Control
🔹 Save versions regularly to avoid losing progress.
🔹 Use Power Platform ALM (Application Lifecycle Management) for controlled releases.
Conclusion
Building a large PowerApps solution requires careful planning, optimized data handling, and performance tuning. By following these best practices, you can ensure your app is fast, scalable, secure, and easy to maintain.
Would you like a specific implementation for SQL, SharePoint, or Dataverse?