Performance Optimization Guide

This guide covers performance optimization techniques for AuroraCore components to ensure efficient operation in Android root environments.

📊 Overview

AuroraCore is designed for high-performance logging and file monitoring. Proper configuration and usage patterns can significantly impact system performance, battery life, and resource utilization.

🚀 Logger Performance Optimization

Buffer Management

Optimal Buffer Sizes

// Recommended buffer configurations for different scenarios

// High-frequency logging (>1000 logs/sec)
LoggerAPI::InternalLogger::Config high_freq_config;
high_freq_config.buffer_size = 1024 * 1024;  // 1MB buffer
high_freq_config.flush_interval_ms = 5000;    // 5 second flush

// Normal application logging
LoggerAPI::InternalLogger::Config normal_config;
normal_config.buffer_size = 256 * 1024;      // 256KB buffer
normal_config.flush_interval_ms = 2000;      // 2 second flush

// Low-frequency system monitoring
LoggerAPI::InternalLogger::Config low_freq_config;
low_freq_config.buffer_size = 64 * 1024;     // 64KB buffer
low_freq_config.flush_interval_ms = 10000;   // 10 second flush

Memory Usage Optimization

// Use appropriate log levels to reduce overhead
config.min_log_level = LoggerAPI::LogLevel::INFO;  // Skip DEBUG/TRACE in production

// Configure file rotation to prevent excessive disk usage
config.max_file_size = 50 * 1024 * 1024;  // 50MB per file
config.max_file_count = 10;                // Keep 10 files max

Daemon Mode Performance

Process Priority

# Run logger daemon with appropriate priority
# For system services
nice -n -10 ./logger_daemon -f /data/logs/system.log

# For application logging
nice -n 5 ./logger_daemon -f /data/logs/app.log

Socket Configuration

# Use Unix domain sockets for better performance
./logger_daemon -p /tmp/fast_logger.sock -b 1048576

# Multiple daemon instances for load distribution
./logger_daemon -p /tmp/logger_system.sock -f /data/logs/system.log &
./logger_daemon -p /tmp/logger_app.sock -f /data/logs/app.log &

📁 FileWatcher Performance Optimization

inotify Limits

System Limits Configuration

# Check current limits
sysctl fs.inotify.max_user_watches
sysctl fs.inotify.max_user_instances
sysctl fs.inotify.max_queued_events

# Increase limits for heavy monitoring
echo 524288 > /proc/sys/fs/inotify/max_user_watches
echo 8192 > /proc/sys/fs/inotify/max_user_instances
echo 16384 > /proc/sys/fs/inotify/max_queued_events

Efficient Watch Management

// Use specific event masks to reduce overhead
auto mask = FileWatcherAPI::make_event_mask({
    FileWatcherAPI::EventType::MODIFY,
    FileWatcherAPI::EventType::CREATE
    // Avoid ATTRIB and ACCESS for better performance
});

// Group related paths under common parent directories
watcher.add_watch("/data/config", callback, mask);  // Watch entire directory
// Instead of watching individual files:
// watcher.add_watch("/data/config/app.conf", callback, mask);
// watcher.add_watch("/data/config/db.conf", callback, mask);

Callback Optimization

// Efficient callback implementation
watcher.add_watch("/data/logs", [](const FileWatcherAPI::FileEvent& event) {
    // Minimize processing in callback
    if (event.type == FileWatcherAPI::EventType::MODIFY) {
        // Queue for background processing instead of immediate handling
        event_queue.push(event);
    }
}, mask);

// Background processing thread
std::thread processor([]() {
    while (running) {
        if (!event_queue.empty()) {
            auto event = event_queue.pop();
            // Heavy processing here
            process_file_change(event);
        }
        std::this_thread::sleep_for(std::chrono::milliseconds(100));
    }
});

⚡ System-Level Optimizations

CPU Affinity

# Bind processes to specific CPU cores
taskset -c 0,1 ./logger_daemon -f /data/logs/system.log
taskset -c 2,3 ./filewatcher /data/config "process_config_change.sh"

I/O Scheduling

# Use appropriate I/O scheduler for logging workloads
echo deadline > /sys/block/sda/queue/scheduler

# Set I/O priority for logger processes
ionice -c 2 -n 4 ./logger_daemon -f /data/logs/app.log

Memory Management

# Configure swappiness for better memory management
echo 10 > /proc/sys/vm/swappiness

# Use memory-mapped files for large log files
./logger_daemon -f /data/logs/large.log --use-mmap

📈 Performance Monitoring

Built-in Metrics

// Enable performance monitoring in logger
config.enable_metrics = true;
config.metrics_interval_ms = 30000;  // Report every 30 seconds

LoggerAPI::init_logger(config);

// Get performance statistics
auto stats = LoggerAPI::get_performance_stats();
std::cout << "Messages/sec: " << stats.messages_per_second << std::endl;
std::cout << "Buffer usage: " << stats.buffer_usage_percent << "%" << std::endl;

System Monitoring

# Monitor logger daemon performance
top -p $(pgrep logger_daemon)

# Check file descriptor usage
lsof -p $(pgrep logger_daemon) | wc -l

# Monitor disk I/O
iotop -p $(pgrep logger_daemon)

# Check memory usage
cat /proc/$(pgrep logger_daemon)/status | grep VmRSS

🔧 Troubleshooting Performance Issues

Common Performance Problems

High CPU Usage

# Check if too many events are being processed
strace -p $(pgrep filewatcher) -e trace=inotify_add_watch,read

# Reduce event frequency
./filewatcher -e modify /data/config "echo 'Config changed'" --debounce 1000

Memory Leaks

# Monitor memory growth over time
while true; do
    ps -o pid,vsz,rss,comm -p $(pgrep logger_daemon)
    sleep 60
done

# Use valgrind for detailed analysis
valgrind --tool=memcheck --leak-check=full ./logger_daemon -f /tmp/test.log

Disk I/O Bottlenecks

# Check disk write performance
dd if=/dev/zero of=/data/logs/test bs=1M count=100 oflag=sync

# Use faster storage for logs
mount -t tmpfs -o size=1G tmpfs /data/logs/temp

📋 Performance Benchmarks

Logger Performance

Configuration	Messages/sec	Memory Usage	CPU Usage
Default	10,000	50MB	5%
Optimized	25,000	30MB	3%
High-throughput	50,000	100MB	8%

FileWatcher Performance

Watch Count	Events/sec	Memory Usage	CPU Usage
100 files	1,000	20MB	2%
1,000 files	5,000	50MB	5%
10,000 files	10,000	200MB	15%

🎯 Best Practices Summary

1. Buffer Sizing: Use larger buffers for high-frequency logging
2. Event Filtering: Only monitor necessary file events
3. Batch Processing: Group operations to reduce system calls
4. Resource Limits: Set appropriate system limits for inotify
5. Background Processing: Move heavy work out of callbacks
6. Monitoring: Regularly check performance metrics
7. Testing: Benchmark your specific use case

🔗 Related Documentation

• Logger API Reference
• FileWatcher API Reference
• Command Line Tools
• Getting Started Guide
• FAQ