When developing applications in Swift, slow scrolling lists can negatively impact user experience. To profile and fix these performance issues, follow these steps:
The first step in identifying performance issues is using Instruments, a profiling tool included in Xcode. Here’s how to use it:
Ensure that your data source methods are efficient. For instance, avoid complex computations in methods like tableView(_:cellForRowAt:)
. Instead, preprocess data where possible.
Cell configuration can be a bottleneck. Use lightweight cells and reuse them effectively:
class MyCustomCell: UITableViewCell {
@IBOutlet weak var titleLabel: UILabel!
@IBOutlet weak var detailLabel: UILabel!
func configure(with title: String, detail: String) {
titleLabel.text = title
detailLabel.text = detail
}
}
func tableView(_ tableView: UITableView, cellForRowAt indexPath: IndexPath) -> UITableViewCell {
let cell = tableView.dequeueReusableCell(withIdentifier: "MyCustomCell", for: indexPath) as! MyCustomCell
let data = dataArray[indexPath.row]
cell.configure(with: data.title, detail: data.detail)
return cell
}
If you’re loading images in your cells, use lazy loading to prevent blocking the main thread:
func loadImage(url: URL, for cell: MyCustomCell) {
DispatchQueue.global().async {
if let data = try? Data(contentsOf: url), let image = UIImage(data: data) {
DispatchQueue.main.async {
cell.imageView?.image = image
cell.setNeedsLayout()
}
}
}
}
prefetches
Implementing prefetching can help smooth out scrolling behavior. Consider implementing tableView(_:prefetchRowsAt:)
to load data ahead of what the user is likely to scroll to.
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?