# ⚡ Performance Tuning Guide

This guide covers performance optimization techniques and best practices for Sonora v1.2.7.

# Performance Architecture

Sonora v1.2.7 features a high-performance architecture designed for enterprise workloads:

Lock-free async queues for concurrent operations
Zero-copy payload routing to minimize memory overhead
Adaptive backpressure to prevent system overload
CPU-aware load balancing across Lavalink nodes

# Memory Optimization

# Garbage Collection Tuning

import gc
import psutil

# Monitor memory usage
process = psutil.Process()
memory_usage = process.memory_info().rss / 1024 / 1024  # MB

# Force garbage collection during idle periods
if memory_usage > 500:  # 500MB threshold
    gc.collect()

# Connection Pooling

# Configure connection pooling
client = SonoraClient(
    lavalink_nodes=[{
        "host": "127.0.0.1",
        "port": 2333,
        "password": "pass",
        "pool_size": 10,  # Connection pool size
        "max_keepalive": 300  # Keep-alive duration
    }],
    connection_pooling=True
)

# CPU Optimization

# Async Task Management

import asyncio
from sonora import performance_monitor

# Monitor CPU usage
cpu_percent = performance_monitor.get_system_stats()['cpu_percent']

# Adjust concurrency based on CPU load
max_concurrent = 10 if cpu_percent < 70 else 5

semaphore = asyncio.Semaphore(max_concurrent)

async def process_with_backpressure(task):
    async with semaphore:
        return await task

# Load Balancing

# CPU-aware node selection
async def select_optimal_node(client, guild_id):
    nodes = client.nodes.values()

    # Score nodes by CPU usage and latency
    scored_nodes = []
    for node in nodes:
        if node.connected:
            cpu_score = 1.0 / (node.cpu_usage + 1)  # Lower CPU is better
            latency_score = 1.0 / (node.latency + 1)  # Lower latency is better
            total_score = cpu_score + latency_score
            scored_nodes.append((node, total_score))

    # Select highest scoring node
    return max(scored_nodes, key=lambda x: x[1])[0] if scored_nodes else None

# Network Optimization

# Connection Tuning

# Optimize Lavalink connections
node_config = {
    "host": "127.0.0.1",
    "port": 2333,
    "password": "pass",
    "timeout": 30,  # Connection timeout
    "reconnect_delay": 5,  # Initial reconnect delay
    "max_reconnect_attempts": 10,
    "heartbeat_interval": 30,  # Heartbeat frequency
    "buffer_size": 8192,  # Socket buffer size
}

# Payload Compression

# Enable payload compression for large data
client = SonoraClient(
    lavalink_nodes=[node_config],
    compression=True,  # Enable gzip compression
    compression_level=6  # Compression level (1-9)
)

# Queue Optimization

# Smart Batching

from sonora import SmartQueue

class OptimizedQueue(SmartQueue):
    def __init__(self, guild_id):
        super().__init__(guild_id)
        self.batch_size = 10
        self.batch_timeout = 0.1  # 100ms

    async def add_batch(self, tracks):
        """Add tracks in optimized batches"""
        for i in range(0, len(tracks), self.batch_size):
            batch = tracks[i:i + self.batch_size]
            await self.add_multiple(batch)

            # Small delay to prevent overwhelming
            if i + self.batch_size < len(tracks):
                await asyncio.sleep(self.batch_timeout)

# Memory-Efficient Queues

# Configure queue memory limits
queue = SmartQueue(guild_id=123)
queue.max_history_size = 100  # Limit history size
queue.max_upcoming_size = 500  # Limit queue size

# Automatic cleanup
async def cleanup_queue():
    while True:
        await asyncio.sleep(300)  # Every 5 minutes

        # Remove old history items
        while len(queue.history) > queue.max_history_size:
            queue.history.pop(0)

        # Remove low-priority upcoming tracks if queue too long
        if len(queue.upcoming) > queue.max_upcoming_size:
            # Keep only high-priority tracks
            queue.upcoming = queue.upcoming[:queue.max_upcoming_size]

# Autoplay Optimization

# Intelligent Caching

from sonora import AutoplayEngine

class CachedAutoplayEngine(AutoplayEngine):
    def __init__(self, guild_id, cache_size=1000):
        super().__init__(guild_id)
        self.recommendation_cache = {}
        self.cache_size = cache_size

    async def fetch_next_track(self, context):
        # Check cache first
        cache_key = self._generate_cache_key(context)

        if cache_key in self.recommendation_cache:
            cached_result = self.recommendation_cache[cache_key]
            if time.time() - cached_result['timestamp'] < 3600:  # 1 hour
                return cached_result['track']

        # Fetch new recommendation
        track = await super().fetch_next_track(context)

        # Cache result
        if track:
            self.recommendation_cache[cache_key] = {
                'track': track,
                'timestamp': time.time()
            }

            # Maintain cache size
            if len(self.recommendation_cache) > self.cache_size:
                # Remove oldest entries
                oldest_key = min(
                    self.recommendation_cache.keys(),
                    key=lambda k: self.recommendation_cache[k]['timestamp']
                )
                del self.recommendation_cache[oldest_key]

        return track

    def _generate_cache_key(self, context):
        """Generate cache key from context"""
        history_titles = [t.title for t in context.get('history', [])[-5:]]
        current_title = context.get('current', {}).get('title', '')
        return hashlib.md5(f"{current_title}:{','.join(history_titles)}".encode()).hexdigest()

# Rate Limiting

# Configure autoplay rate limits
autoplay = AutoplayEngine(guild_id)
autoplay.max_requests_per_minute = 30  # Limit external API calls
autoplay.cache_ttl = 3600  # Cache recommendations for 1 hour

# Profiling and Monitoring

# Built-in Profiler

from sonora import performance_profiler

# Start profiling
performance_profiler.start_profiling()

# Run your bot operations...

# Stop and analyze
results = performance_profiler.stop_profiling()

print(f"Total execution time: {results['execution_time']:.2f}s")
print(f"Peak memory usage: {results['memory_peak_mb']:.1f} MB")

# Analyze slowest functions
for line in results['profile_stats'].split('\n')[:10]:
    if line.strip():
        print(line)

# Performance Metrics

from sonora import performance_monitor

# Track custom metrics
performance_monitor.record_timing("track_load", 0.234)
performance_monitor.increment_counter("tracks_played")
performance_monitor.set_gauge("active_guilds", 150)

# Get comprehensive stats
stats = performance_monitor.get_stats()
system_stats = performance_monitor.get_system_stats()

print(f"CPU Usage: {system_stats['cpu_percent']:.1f}%")
print(f"Memory: {system_stats['memory_mb']:.1f} MB")
print(f"Active connections: {stats.get('counters', {}).get('active_connections', 0)}")

# Scaling Strategies

# Horizontal Scaling

# Multi-node configuration
nodes = [
    {"host": "node1.example.com", "port": 2333, "region": "us-east"},
    {"host": "node2.example.com", "port": 2333, "region": "us-west"},
    {"host": "node3.example.com", "port": 2333, "region": "eu-central"},
]

client = SonoraClient(
    lavalink_nodes=nodes,
    node_pooling=True,
    load_balancing="latency"  # or "cpu", "random"
)

# Vertical Scaling

# Optimize for high concurrency
client = SonoraClient(
    lavalink_nodes=[node_config],
    max_concurrent_requests=100,
    connection_pool_size=20,
    enable_compression=True
)

# Configure queue for high throughput
queue = SmartQueue(guild_id)
queue.enable_adaptive_reorder = False  # Disable for performance
queue.max_concurrent_operations = 50

# Benchmarking

# Performance Benchmarks

# Run comprehensive benchmarks
sonoractl benchmark

# Profile specific operations
sonoractl profile

# Test with different loads
# - 100 concurrent tracks
# - 1000 queue size
# - Network latency simulation

# Custom Benchmarks

import time
import asyncio
from sonora import performance_profiler

async def benchmark_track_loading(client, num_tracks=100):
    """Benchmark track loading performance"""
    performance_profiler.start_profiling()

    start_time = time.time()

    # Load tracks concurrently
    tasks = []
    for i in range(num_tracks):
        query = f"test track {i}"
        task = client.load_track(f"ytsearch:{query}")
        tasks.append(task)

    results = await asyncio.gather(*tasks, return_exceptions=True)

    end_time = time.time()
    profile_results = performance_profiler.stop_profiling()

    successful_loads = len([r for r in results if not isinstance(r, Exception)])

    return {
        "total_time": end_time - start_time,
        "successful_loads": successful_loads,
        "failure_rate": (num_tracks - successful_loads) / num_tracks,
        "avg_time_per_track": (end_time - start_time) / num_tracks,
        "profile_data": profile_results
    }

# Troubleshooting Performance Issues

# Common Performance Problems

High CPU Usage:

Check for infinite loops in plugins
Monitor asyncio task count
Profile with performance_profiler

High Memory Usage:

Check for memory leaks in plugins
Monitor queue sizes
Use tracemalloc for detailed analysis

Slow Track Loading:

Check network latency to Lavalink
Monitor Lavalink node performance
Enable caching and connection pooling

Queue Performance Issues:

Check queue size limits
Monitor concurrent operations
Enable adaptive reordering only when needed

# Performance Monitoring Dashboard

# Create performance dashboard
async def performance_dashboard():
    while True:
        stats = performance_monitor.get_stats()
        system = performance_monitor.get_system_stats()

        print("=== Performance Dashboard ===")
        print(f"CPU: {system['cpu_percent']:.1f}%")
        print(f"Memory: {system['memory_mb']:.1f} MB")
        print(f"Uptime: {stats['uptime']:.1f}s")
        print(f"Active connections: {stats.get('counters', {}).get('active_connections', 0)}")
        print(f"Tracks played: {stats.get('counters', {}).get('tracks_played', 0)}")

        await asyncio.sleep(60)  # Update every minute

# Best Practices

# 1. Connection Management

Use connection pooling
Configure appropriate timeouts
Enable compression for large payloads

# 2. Memory Management

Set reasonable queue size limits
Implement automatic cleanup
Monitor memory usage patterns

# 3. CPU Optimization

Use async operations throughout
Implement backpressure
Profile regularly to identify bottlenecks

# 4. Network Efficiency

Enable compression
Use appropriate buffer sizes
Implement request batching

# 5. Monitoring

Set up comprehensive monitoring
Use performance profiling regularly
Monitor system resources continuously

# Enterprise Deployment

# Production Configuration

# Production-optimized configuration
client = SonoraClient(
    lavalink_nodes=[
        {
            "host": "production-node-1",
            "port": 2333,
            "password": os.getenv("LAVALINK_PASSWORD"),
            "timeout": 30,
            "reconnect_delay": 5,
            "max_reconnect_attempts": 10,
            "heartbeat_interval": 30,
            "buffer_size": 16384,
        }
    ],
    connection_pooling=True,
    compression=True,
    max_concurrent_requests=200,
    enable_metrics=True,
    security_enabled=True
)

This comprehensive performance tuning guide will help you optimize Sonora v1.2.7 for maximum efficiency and scalability in production environments.