Database

PostgreSQL performance tuning requires understanding configuration and queries. Here’s a guide. Configuration Tuning postgresql.conf # Memory settings shared_buffers = 256MB effective_cache_size = 1GB work_mem = 16MB # Connection settings max_connections = 100 # Query planner random_page_cost = 1.1 Query Optimization Use EXPLAIN ANALYZE EXPLAIN ANALYZE SELECT * FROM users WHERE email = '[email protected]'; Indexing -- B-tree index CREATE INDEX idx_user_email ON users(email); -- Partial index CREATE INDEX idx_active_users ON users(email) WHERE status = 'active'; -- Composite index CREATE INDEX idx_user_status_created ON users(status, created_at); Best Practices Analyze query plans Create appropriate indexes Use connection pooling Monitor slow queries Vacuum regularly Conclusion Tune PostgreSQL for optimal performance! 🐘

Using SQLAlchemy with async Python requires understanding async patterns. Here’s how. Setup from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession from sqlalchemy.orm import sessionmaker engine = create_async_engine("postgresql+asyncpg://user:pass@localhost/db") AsyncSessionLocal = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) Async Models from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, Integer, String Base = declarative_base() class User(Base): __tablename__ = "users" id = Column(Integer, primary_key=True) name = Column(String) Async Operations async def create_user(name: str): async with AsyncSessionLocal() as session: user = User(name=name) session.add(user) await session.commit() return user async def get_user(user_id: int): async with AsyncSessionLocal() as session: result = await session.get(User, user_id) return result Best Practices Use async context managers Commit transactions explicitly Handle exceptions properly Use connection pooling Close sessions correctly Conclusion Build efficient async database applications with SQLAlchemy! 🐍