A Swift library for displaying real-time FPS, CPU, and memory usage overlays on iOS apps.
GDPerformanceView-Swift is an iOS library that provides real-time performance monitoring by displaying an overlay with FPS, CPU usage, memory usage, and system information above the status bar. It helps developers identify performance issues during app development and testing without needing external profiling tools.
iOS developers and QA engineers who need to monitor app performance in real-time during development, debugging, or testing phases.
It offers a simple, integrated way to visualize performance metrics directly on the device with customizable displays and delegate-based reporting, eliminating the need for complex external tooling.
Shows FPS, CPU and memory usage, device model, app and iOS versions above the status bar and report FPS, CPU and memory usage via delegate.
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Provides immediate, on-device display of FPS, CPU, and memory usage above the status bar, enabling quick performance checks without external tools like Xcode Instruments.
Allows developers to select specific metrics (e.g., performance, memory, application) and customize appearance with dark, light, or custom styles, as detailed in the README's appearance section.
Offers programmatic access to performance data via a delegate, enabling logging or integration with other systems without relying solely on the visual overlay.
Easy to install via CocoaPods or Carthage with straightforward API calls for starting, pausing, and configuring monitoring, reducing setup complexity.
Only covers basic indicators like FPS, CPU, and memory usage, lacking advanced profiling features such as network activity, battery usage, or GPU performance found in tools like Xcode Instruments.
The overlay is positioned above the status bar, which can interfere with app status bar styling and may not be suitable for all design or testing scenarios, as noted in the status bar control section.
While data is accessible via delegate, there's no out-of-the-box solution for saving, exporting, or analyzing historical performance logs, requiring additional development effort.