Production Python database engineering

Build SQLAlchemy data layers that hold up in production.

Practical guidance for teams moving to SQLAlchemy 2.0, async execution, tuned connection pools, and query patterns that stay predictable under real traffic.

Explore async workflows Improve query performance

engine = create_async_engine(
    DATABASE_URL,
    pool_size=20,
    max_overflow=10,
    pool_pre_ping=True,
)

async with AsyncSession(engine) as session:
    result = await session.execute(
        select(User).where(User.active)
    )

SQLAlchemy-first

2.0

Event-loop aware

async

Operational focus

prod

Core reading paths

Choose the work you need to unblock.

Master SQLAlchemy 2.0 ArchitectureModern Core and ORM patterns, typed mappings, session scope, and migration decisions for production Python systems.Core vs ORMMapped[] typingSession lifecycle Build Reliable Async EnginesAsync drivers, engine configuration, pool sizing, and failure modes for FastAPI, Starlette, and worker workloads.asyncpg vs psycopgPool exhaustionFastAPI integration Optimize Queries and Bulk DataRelationship loading, analytical SQL, recursive CTEs, and high-throughput writes without accidental N+1 behavior.selectinloadWindow functionsBulk inserts