Flutter Performance Monitor #

Real-time performance monitoring with FPS tracking and memory usage for Flutter applications.

Features #

• 🎯 Real-time FPS tracking - Monitor frame rates in real-time
• 💾 Memory usage monitoring - Track memory consumption patterns
• 📊 Performance metrics - Comprehensive performance analytics
• 🔄 Live updates - Real-time performance data updates
• 📱 Cross-platform - Works on iOS, Android, Web, and Desktop
• ⚡ Lightweight - Minimal performance impact on your app
• 🚀 Native implementations - Real CPU and memory metrics on Android/iOS

Getting Started #

Installation #

Add this to your package's pubspec.yaml file:

dependencies:
  flutter_perf_monitor: ^0.1.0

Usage #

import 'package:flutter_perf_monitor/flutter_perf_monitor.dart';

void main() {
  // Initialize the performance monitor
  FlutterPerfMonitor.initialize();
  
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      home: Scaffold(
        body: Column(
          children: [
            // Performance monitor widget
            PerfMonitorWidget(),
            
            // Your app content
            Expanded(
              child: YourAppContent(),
            ),
          ],
        ),
      ),
    );
  }
}

API Reference #

FlutterPerfMonitor #

The main class for performance monitoring.

Methods

• initialize() - Initialize the performance monitor
• startMonitoring() - Start performance monitoring
• stopMonitoring() - Stop performance monitoring
• getFPS() - Get current FPS value
• getMemoryUsage() - Get current memory usage
• getPerCoreCpuUsage() - Get per-core CPU usage percentages

PerfMonitorWidget #

A widget that displays performance metrics.

Example #

See the example directory for a complete working example.

Native Implementations #

This package includes native implementations for Android and iOS to provide accurate system-level metrics:

Android #

• Total & Available Memory: Via ActivityManager.MemoryInfo
• Per-Core CPU Usage: Via /proc/stat
• Total CPU Usage: Average of all cores

iOS #

• Total & Available Memory: Via ProcessInfo and mach_task_basic_info
• Per-Core CPU Usage: Via task_threads
• Total CPU Usage: Sum of all thread usage

Fallback Behavior #

On platforms without native support (Web, Desktop), the package falls back to:

• Memory: Process-level memory via ProcessInfo.currentRss
• CPU: FPS-based estimation

For detailed implementation information, see SETUP.md.

Contributing #

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License #

This project is licensed under the MIT License - see the LICENSE file for details.

Support #

If you encounter any problems or have suggestions, please file an issue at the GitHub repository.

Changelog #

See CHANGELOG.md for a list of changes and version history.