Performance Optimization: Profiling, Caching, and Latency Reduction

-- Bad: SELECT *
SELECT * FROM orders WHERE user_id = 123;

-- Good: SELECT specific columns
SELECT id, total, status, created_at 
FROM orders 
WHERE user_id = 123 
AND status = 'completed'
LIMIT 10;

# O(n²) - Bad for large inputs
for i in data:
    for j in data:
        process(i, j)

# O(n log n) - Better
sorted_data = sorted(data)
for i in sorted_data:
    process(i)

# Bad: N+1 query problem
users = db.query("SELECT * FROM users LIMIT 100")
for user in users:
    posts = db.query(f"SELECT * FROM posts WHERE user_id = {user.id}")
    user.posts = posts

# Better: Single join
users = db.query("""
    SELECT u.*, p.* FROM users u
    LEFT JOIN posts p ON u.id = p.user_id
    WHERE u.id IN (SELECT id FROM users LIMIT 100)
""")

# Blocking: Wait for each request
results = []
for url in urls:
    results.append(requests.get(url))  # Sequential

# Non-blocking: All requests in parallel
import asyncio
import aiohttp

async def fetch_all(urls):
    async with aiohttp.ClientSession() as session:
        tasks = [session.get(url) for url in urls]
        return await asyncio.gather(*tasks)