# Performance Tuning

# Enable performance mode
client = SonoraClient(..., performance_mode="overdrive")

# Configure thread pools
import concurrent.futures
executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)

# Use CPU affinity
import os
os.sched_setaffinity(0, {0, 1, 2, 3})  # Use cores 0-3

# Object pooling
from sonora.utils import ObjectPool

track_pool = ObjectPool(Track, max_size=1000)

# Memory monitoring
import psutil
process = psutil.Process()

def check_memory():
    mem = process.memory_info()
    if mem.rss > 512 * 1024 * 1024:  # 512MB
        gc.collect()

# Connection pooling
connector = aiohttp.TCPConnector(
    limit=100,
    limit_per_host=10,
    ttl_dns_cache=300,
    use_dns_cache=True
)

# File I/O optimization
import aiofiles

async def read_large_file(path):
    async with aiofiles.open(path, 'rb') as f:
        return await f.read()

# application.yml
server:
  port: 2333

lavalink:
  server:
    password: "password"
    sources:
      youtube: true
      bandcamp: true
    bufferDurationMs: 400
    frameBufferDurationMs: 1000
    opusEncodingQuality: 9
    resamplingQuality: HIGH
    trackStuckThresholdMs: 10000

  audio:
    opus:
      quality: 10

# Intelligent load balancer
class SmartLoadBalancer:
    def __init__(self, nodes):
        self.nodes = nodes
        self.metrics = {}

    def select_node(self, track):
        # Score nodes based on load
        scores = {}
        for node in self.nodes:
            cpu = node.stats.get('cpu', {}).get('cores', 1)
            load = node.stats.get('cpu', {}).get('systemLoad', 0)
            score = cpu / (load + 1)  # Higher is better
            scores[node] = score

        return max(scores, key=scores.get)

# Task management
async def run_with_timeout(coro, timeout=5.0):
    try:
        return await asyncio.wait_for(coro, timeout=timeout)
    except asyncio.TimeoutError:
        logger.warning("Operation timed out")
        return None

# Concurrent operations
async def batch_process(items, batch_size=10):
    semaphore = asyncio.Semaphore(batch_size)

    async def process_item(item):
        async with semaphore:
            return await process_single_item(item)

    tasks = [process_item(item) for item in items]
    return await asyncio.gather(*tasks)

# Multi-level caching
from cachetools import TTLCache, LRUCache

# Fast L1 cache
l1_cache = LRUCache(maxsize=1000)

# L2 cache with TTL
l2_cache = TTLCache(maxsize=10000, ttl=300)

async def get_with_cache(key):
    # Check L1
    if key in l1_cache:
        return l1_cache[key]

    # Check L2
    if key in l2_cache:
        value = l2_cache[key]
        l1_cache[key] = value
        return value

    # Fetch and cache
    value = await fetch_data(key)
    l1_cache[key] = value
    l2_cache[key] = value
    return value

# Connection pooling
db_pool = await asyncpg.create_pool(
    min_size=5,
    max_size=20,
    command_timeout=60
)

# Query optimization
async def get_tracks_batch(track_ids):
    # Single query instead of multiple
    query = "SELECT * FROM tracks WHERE id = ANY($1)"
    return await db_pool.fetch(query, track_ids)

# Index optimization
# CREATE INDEX CONCURRENTLY idx_tracks_artist ON tracks (artist);
# CREATE INDEX CONCURRENTLY idx_tracks_title ON tracks (title);

import time
from contextlib import asynccontextmanager

@asynccontextmanager
async def measure_time(operation_name):
    start = time.perf_counter()
    try:
        yield
    finally:
        duration = time.perf_counter() - start
        metrics.record_latency(operation_name, duration)

# Usage
async with measure_time("track_load"):
    track = await player.play(query)

import cProfile
import pstats

def profile_function(func):
    def wrapper(*args, **kwargs):
        profiler = cProfile.Profile()
        try:
            return profiler.runcall(func, *args, **kwargs)
        finally:
            stats = pstats.Stats(profiler)
            stats.sort_stats('cumulative')
            stats.print_stats(20)  # Top 20 functions
    return wrapper

# Profile async functions
@profile_function
async def profiled_operation():
    await player.play("query")

class ResourceMonitor:
    def __init__(self):
        self.metrics = {}

    async def monitor(self):
        while True:
            self.metrics.update({
                'cpu': psutil.cpu_percent(),
                'memory': psutil.virtual_memory().percent,
                'disk': psutil.disk_usage('/').percent,
                'network': psutil.net_io_counters()
            })

            # Alert on thresholds
            if self.metrics['cpu'] > 90:
                await send_alert("High CPU usage")
            if self.metrics['memory'] > 85:
                await send_alert("High memory usage")

            await asyncio.sleep(60)  # Check every minute

# Multi-node setup
nodes = [
    {"host": "node1.example.com", "port": 2333},
    {"host": "node2.example.com", "port": 2333},
    {"host": "node3.example.com", "port": 2333}
]

client = SonoraClient(
    lavalink_nodes=nodes,
    node_pooling=True
)

# Optimize for high load
config = {
    "max_players": 1000,
    "buffer_size": 1024 * 64,  # 64KB
    "thread_pool_size": 16,
    "connection_pool_size": 100
}

class LoadShedder:
    def __init__(self, threshold=0.8):
        self.threshold = threshold

    async def should_shed(self):
        cpu = psutil.cpu_percent() / 100
        memory = psutil.virtual_memory().percent / 100
        return cpu > self.threshold or memory > self.threshold

    async def shed_load(self, request):
        if await self.should_shed():
            # Return cached response or error
            return {"error": "Service overloaded", "retry_after": 30}
        else:
            return await process_request(request)

# Run built-in benchmarks
sonoractl benchmark --all

# Custom benchmark
python -c "
import asyncio
import time

async def benchmark():
    start = time.time()
    tasks = [player.play(f'test{i}') for i in range(100)]
    await asyncio.gather(*tasks)
    duration = time.time() - start
    print(f'100 tracks in {duration:.2f}s')

asyncio.run(benchmark())
"

# Compare configurations
configs = [
    {"buffer_size": 1024},
    {"buffer_size": 2048},
    {"buffer_size": 4096}
]

for config in configs:
    # Apply config
    player.buffer_size = config["buffer_size"]

    # Benchmark
    duration = await benchmark_operation()
    print(f"Config {config}: {duration:.3f}s")