Wes Matlock

Tuning Lazy Stacks and Grids in SwiftUI: A Performance Guide

When building SwiftUI applications, performance tuning becomes essential, especially when working with large datasets or complex layouts…

Tuning Lazy Stacks and Grids in SwiftUI: A Performance Guide

When building SwiftUI applications, performance tuning becomes essential, especially when working with large datasets or complex layouts. SwiftUI’s LazyVStack and LazyHStack provide powerful tools for managing large lists, while LazyVGrid and LazyHGrid extend this to grid-based layouts. In this blog post, we’ll dive into the details of how and when to use these lazy containers, the common pitfalls you might encounter, and strategies to optimize their performance.

Understanding Lazy Stacks

Regular stacks like VStack and HStack eagerly load all of their child views when the view is created. For small datasets, this works fine, but for large datasets, loading all items at once can cause significant memory usage and slow down the initial view rendering.

Enter Lazy Stacks. SwiftUI’s LazyVStack and LazyHStack defer view creation until the view is about to appear on the screen. This lazy loading mechanism allows for large datasets to be handled efficiently without overwhelming memory and CPU resources.

Example: LazyVStack

struct LazyViewSample: View {  
  let items = Array(1...10000)  
  
  var body: some View {  
    ScrollView {  
      LazyVStack {  
        ForEach(items, id: \.self) { item in  
          Text("Item \(item)")  
            .padding()  
            .background(Color.blue)  
            .cornerRadius(8)  
        }  
      }  
    }  
    .padding()  
  }  
}

In this example, LazyVStack only loads the visible views on the screen, making it memory-efficient even with 10,000 items.

Performance Pitfalls and Considerations

While lazy stacks improve performance significantly, there are pitfalls you need to be aware of:

1. Memory Usage & View Retention

Lazy stacks defer view creation but retain offscreen views in memory once they have been created. This can lead to increased memory usage when the user scrolls through large datasets. For memory-heavy views (e.g., views containing images or video), retaining all offscreen views can cause memory bloat.

Mitigation:

  • Use List instead of LazyVStack when working with very large datasets. List provides view recycling, similar to UITableView, which ensures offscreen views are discarded.
  • Break down complex views into smaller, simpler components to reduce their memory footprint. Lazy-loading large views should still be mindful of complexity.

2. Excessive Re-renders

SwiftUI’s declarative nature can lead to frequent re-renders of views, even when using lazy stacks. If not managed carefully, expensive computations in the view hierarchy can negatively impact performance, particularly in long lists.

Mitigation:

  • Use @State, @Binding, and @ObservedObject to minimize unnecessary view updates.
  • Cache the results of expensive computations or data-fetching operations to prevent recalculating them during each render cycle.

3. Complex Subviews

Using complex views or animations inside lazy stacks can degrade performance, especially during scrolling. For example, continuously running animations inside a lazily loaded list can lead to significant CPU usage.

Mitigation:

  • Start animations only when the view appears using onAppear and stop them when the view disappears with onDisappear.
struct AnimatedView: View {  
  @State private var rotate = false  
  
  var body: some View {  
    Text("Animating...")  
      .rotationEffect(Angle(degrees: rotate ? 360 : 0))  
      .onAppear {  
        withAnimation(.linear(duration: 2).repeatForever(autoreverses: false)) {  
          rotate = true  
        }  
      }  
      .onDisappear {  
        rotate = false  
      }  
  }  
}

This approach ensures animations are only active when they are visible, helping to conserve CPU resources.

Lazy Grids: Flexibility with Performance

LazyVGrid and LazyHGrid extend the concept of lazy loading to grid layouts, making them ideal for displaying large, structured data such as product listings, photo galleries, or similar content.

Example: LazyVGrid

struct GridView: View {  
  let items = Array(1...1000)  
  let columns = [GridItem(.adaptive(minimum: 50))]  
  
  var body: some View {  
    ScrollView {  
      LazyVGrid(columns: columns, spacing: 10) {  
        ForEach(items, id: \.self) { item in  
          Text("Item \(item)")  
            .frame(minWidth: 50, minHeight: 50)  
            .background(Color.orange)  
            .cornerRadius(8)  
        }  
      }  
      .padding()  
    }  
  }  
}

Performance Considerations:

  • Similar to lazy stacks, avoid complex subviews within grids. Use caching for image-heavy or media-rich content to minimize memory and CPU usage.
  • You can optimize grid performance further by reducing the number of items rendered at once using paginated data loading.

Lazy Loading Data & Images

Lazy loading is not just about views. It also applies to how you manage your data and assets like images. Loading all data upfront, even with a lazy stack, defeats the purpose of deferred view creation.

Data Loading Example:

class DataLoader: ObservableObject {  
  @Published var items: [String] = []  
  private var currentPage = 0  
  private let pageSize = 20  
  
  func loadMore() {  
    let newItems = (1...pageSize).map { "Item \($0 + currentPage * pageSize)" }  
    items.append(contentsOf: newItems)  
    currentPage += 1  
  }  
}  
  
struct ContentView: View {  
  @StateObject private var dataLoader = DataLoader()  
  
  var body: some View {  
    List {  
      ForEach(dataLoader.items, id: \.self) { item in  
        Text(item)  
      }  
      .onAppear {  
        if dataLoader.items.count % dataLoader.pageSize == 0 {  
          dataLoader.loadMore()  
        }  
      }  
    }  
    .onAppear {  
      dataLoader.loadMore()  
    }  
  }  
}

Lazy loading data in pages or batches helps to reduce memory consumption and speeds up the rendering of large datasets.

Profiling and Testing

Profiling your app is crucial to ensure lazy stacks and grids perform optimally. Use Xcode’s Instruments — particularly the Time Profiler and Memory Allocations tools — to track memory usage, identify view retention issues, and detect over-rendering.

Profiling Tips:

  • Time Profiler: Identify which views are causing performance bottlenecks. This tool helps detect expensive render operations or slow scrolling performance.
  • Memory Allocations: Track memory usage growth over time, especially when handling large datasets. Monitor for signs of memory leaks or excessive retention of views.

Conclusion

SwiftUI’s LazyVStack, LazyHStack, and lazy grids are powerful tools for handling large or complex datasets with minimal impact on performance. However, achieving smooth, efficient performance requires understanding when and how to use these lazy components, as well as avoiding common pitfalls like memory overuse and unnecessary re-renders. Profiling and testing your app will help catch these issues early and ensure that your app remains responsive and efficient.

By following the best practices outlined in this post, you can tune your SwiftUI applications to efficiently handle large data sets, optimize rendering times, and provide a smooth, performant user experience.

If you want to learn more about native mobile development, you can check out the other articles I have written here: https://medium.com/@wesleymatlock

🚀 Happy coding! 🚀

By Wesley Matlock on October 31, 2024.

Canonical link

Exported from Medium on May 10, 2025.

Written on October 31, 2024