python-database-patterns

SQLAlchemy 2.0 async operations use async engines and sessions to handle non-blocking database calls. Define an async engine with `create_async_engine()`, create async sessions, and use `async with` syntax in your application. This pattern works with FastAPI and other async frameworks for concurrent database access.

Use Declarative Base to define ORM models with clear relationships, foreign keys, and constraints. Structure models around your domain entities, leverage SQLAlchemy's relationship() to define one-to-many and many-to-many associations, and validate through type hints. This approach ensures testable, maintainable data access code.

Alembic scaffolds migration files that track schema changes incrementally. Initialize Alembic in your project, define models using Declarative Base, auto-generate migrations, review the generated SQL, and apply upgrades or downgrades to your database. This ensures reproducible schema versioning across environments.

Transactional patterns wrap database operations in explicit transactions, committing or rolling back atomically. The Unit of Work pattern batches multiple operations and flushes them together. Use SQLAlchemy sessions with context managers to ensure proper transaction boundaries and handle rollback on errors.

Yes, SQLAlchemy includes built-in pool configuration to manage database connections efficiently. Configure pool size, overflow, and recycling behavior when creating your engine. Connection pooling reduces overhead and improves concurrency for high-traffic production applications.

Synchronous SQLAlchemy blocks on I/O, limiting concurrency under heavy load. Each request consumes a thread or process, reducing throughput. Async patterns avoid this by allowing many concurrent operations per thread, making async SQLAlchemy sessions the preferred choice for FastAPI and modern concurrent frameworks.