AS

async-sqlalchemy-patterns

Provides production-ready patterns for async database operations, ORM models, and migration workflows in FastAPI.

Install

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Activation

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Async SQLAlchemy 2.0+ database patterns for FastAPI including session management, connection pooling, Alembic migrations, relationship loading strategies, and query optimization. Use when implementing database models, configuring async sessions, setting up migrations, optimizing queries, managing relationships, or when user mentions SQLAlchemy, async database, ORM, Alembic, database performance, or connection pooling.
421 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Advanced

Key capabilities

  • Set up async database engines and session factories
  • Manage database sessions using FastAPI dependency injection
  • Implement base models with UUID primary keys and timestamps
  • Configure Alembic for database migrations
  • Optimize relationship loading with selectinload and joinedload
  • Perform basic CRUD operations asynchronously

How it works

This skill configures SQLAlchemy 2.0+ for asynchronous operations within FastAPI, providing patterns for session management, model definition, and query optimization.

Inputs & outputs

You give it
Database URL, SQLAlchemy models, migration scripts
You get back
Configured async database connection, migrated schema, query results

When to use async-sqlalchemy-patterns

  • Configure async SQLAlchemy sessions
  • Set up Alembic migrations
  • Optimize database model performance

About this skill

Async SQLAlchemy Patterns

Purpose: Implement production-ready async SQLAlchemy 2.0+ patterns in FastAPI with proper session management, migrations, and performance optimization.

Activation Triggers:

  • Database model implementation
  • Async session configuration
  • Alembic migration setup
  • Query performance optimization
  • Relationship loading issues
  • Connection pool configuration
  • Transaction management
  • Database schema migrations

Key Resources:

  • scripts/setup-alembic.sh - Initialize Alembic with async support
  • scripts/generate-migration.sh - Create migrations from model changes
  • templates/base_model.py - Base model with common patterns
  • templates/session_manager.py - Async session factory and dependency
  • templates/alembic.ini - Alembic configuration for async
  • examples/user_model.py - Complete model with relationships
  • examples/async_context_examples.py - Session usage patterns

Core Patterns

1. Async Engine and Session Setup

Database Configuration:

# app/core/database.py
from sqlalchemy.ext.asyncio import (
    AsyncSession,
    create_async_engine,
    async_sessionmaker
)
from sqlalchemy.orm import declarative_base
from app.core.config import settings

# Create async engine
engine = create_async_engine(
    settings.DATABASE_URL,
    echo=settings.DEBUG,
    pool_pre_ping=True,  # Verify connections before using
    pool_size=5,         # Base number of connections
    max_overflow=10,     # Additional connections when pool is full
    pool_recycle=3600,   # Recycle connections after 1 hour
    pool_timeout=30,     # Wait 30s for available connection
)

# Create async session factory
AsyncSessionLocal = async_sessionmaker(
    engine,
    class_=AsyncSession,
    expire_on_commit=False,  # Keep objects usable after commit
    autoflush=False,         # Manual flush control
    autocommit=False,        # Explicit commits only
)

Base = declarative_base()

Dependency Injection Pattern:

# app/core/deps.py
from typing import AsyncGenerator
from sqlalchemy.ext.asyncio import AsyncSession
from app.core.database import AsyncSessionLocal

async def get_db() -> AsyncGenerator[AsyncSession, None]:
    """
    FastAPI dependency for database sessions.
    Automatically handles cleanup.
    """
    async with AsyncSessionLocal() as session:
        try:
            yield session
            await session.commit()
        except Exception:
            await session.rollback()
            raise
        finally:
            await session.close()

2. Base Model Pattern

Use template from templates/base_model.py with:

  • UUID primary keys by default
  • Automatic created_at/updated_at timestamps
  • Soft delete support
  • Common query methods

Essential Mixins:

from datetime import datetime
from sqlalchemy import DateTime, Boolean, func
from sqlalchemy.orm import Mapped, mapped_column

class TimestampMixin:
    """Auto-managed timestamps"""
    created_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True),
        server_default=func.now(),
        nullable=False
    )
    updated_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True),
        server_default=func.now(),
        onupdate=func.now(),
        nullable=False
    )

class SoftDeleteMixin:
    """Soft delete support"""
    is_deleted: Mapped[bool] = mapped_column(
        Boolean,
        default=False,
        nullable=False
    )
    deleted_at: Mapped[datetime | None] = mapped_column(
        DateTime(timezone=True),
        nullable=True
    )

3. Relationship Loading Strategies

Lazy Loading (Default - N+1 Problem Risk):

# Bad: Causes N+1 queries
users = await session.execute(select(User))
for user in users.scalars():
    print(user.posts)  # Separate query per user!

Eager Loading (Recommended):

from sqlalchemy.orm import selectinload, joinedload

# selectinload: Separate query, good for one-to-many
stmt = select(User).options(selectinload(User.posts))
result = await session.execute(stmt)
users = result.scalars().all()

# joinedload: SQL JOIN, good for many-to-one
stmt = select(Post).options(joinedload(Post.author))
result = await session.execute(stmt)
posts = result.scalars().unique().all()  # unique() required with joins!

# subqueryload: Subquery loading
stmt = select(User).options(subqueryload(User.posts))

Relationship Configuration:

from sqlalchemy.orm import relationship

class User(Base):
    __tablename__ = "users"

    # One-to-many with cascade
    posts: Mapped[list["Post"]] = relationship(
        back_populates="author",
        cascade="all, delete-orphan",
        lazy="selectin"  # Default eager loading
    )

    # Many-to-many
    roles: Mapped[list["Role"]] = relationship(
        secondary="user_roles",
        back_populates="users",
        lazy="selectin"
    )

4. Query Patterns

Basic CRUD:

from sqlalchemy import select, update, delete
from sqlalchemy.ext.asyncio import AsyncSession

# Create
async def create_user(session: AsyncSession, user_data: dict):
    user = User(**user_data)
    session.add(user)
    await session.commit()
    await session.refresh(user)
    return user

# Read with filters
async def get_users(session: AsyncSession, skip: int = 0, limit: int = 100):
    stmt = (
        select(User)
        .where(User.is_deleted == False)
        .offset(skip)
        .limit(limit)
        .order_by(User.created_at.desc())
    )
    result = await session.execute(stmt)
    return result.scalars().all()

# Update
async def update_user(session: AsyncSession, user_id: int, updates: dict):
    stmt = (
        update(User)
        .where(User.id == user_id)
        .values(**updates)
        .returning(User)
    )
    result = await session.execute(stmt)
    await session.commit()
    return result.scalar_one()

# Delete
async def delete_user(session: AsyncSession, user_id: int):
    stmt = delete(User).where(User.id == user_id)
    await session.execute(stmt)
    await session.commit()

Complex Queries:

from sqlalchemy import func, and_, or_

# Aggregations
stmt = (
    select(User.id, func.count(Post.id))
    .join(Post)
    .group_by(User.id)
    .having(func.count(Post.id) > 5)
)

# Subqueries
subq = (
    select(func.avg(Post.views))
    .where(Post.user_id == User.id)
    .scalar_subquery()
)
stmt = select(User).where(User.id.in_(
    select(Post.user_id).where(Post.views > subq)
))

# Window functions
from sqlalchemy import over
stmt = select(
    Post,
    func.row_number().over(
        partition_by=Post.user_id,
        order_by=Post.created_at.desc()
    ).label('row_num')
).where(over.row_num <= 10)

5. Transaction Management

Nested Transactions:

async def transfer_funds(
    session: AsyncSession,
    from_account: int,
    to_account: int,
    amount: float
):
    async with session.begin_nested():  # Savepoint
        # Debit
        stmt = (
            update(Account)
            .where(Account.id == from_account)
            .where(Account.balance >= amount)
            .values(balance=Account.balance - amount)
        )
        result = await session.execute(stmt)
        if result.rowcount == 0:
            raise ValueError("Insufficient funds")

        # Credit
        stmt = (
            update(Account)
            .where(Account.id == to_account)
            .values(balance=Account.balance + amount)
        )
        await session.execute(stmt)

    await session.commit()

Manual Transaction Control:

async def batch_operation(session: AsyncSession, items: list):
    try:
        for item in items:
            session.add(item)
            if len(session.new) >= 100:
                await session.flush()  # Flush but don't commit

        await session.commit()
    except Exception as e:
        await session.rollback()
        raise

6. Alembic Migration Setup

# Initialize Alembic with async template
./scripts/setup-alembic.sh

# Creates:
# - alembic/ directory
# - alembic.ini configured for async
# - env.py with async support

Generate Migrations:

# Auto-generate from model changes
./scripts/generate-migration.sh "add user table"

# Review generated migration in alembic/versions/
# Always review auto-generated migrations!

Migration Best Practices:

  1. Always review auto-generated migrations
  2. Use batch operations for large tables
  3. Add indexes in separate migrations
  4. Include both upgrade and downgrade
  5. Test migrations on staging first

Manual Migration Template:

# alembic/versions/xxx_add_index.py
from alembic import op
import sqlalchemy as sa

def upgrade() -> None:
    # Use batch for SQLite compatibility
    with op.batch_alter_table('users') as batch_op:
        batch_op.create_index(
            'ix_users_email',
            ['email'],
            unique=True
        )

def downgrade() -> None:
    with op.batch_alter_table('users') as batch_op:
        batch_op.drop_index('ix_users_email')

7. Connection Pool Configuration

Production Settings:

# For web applications
engine = create_async_engine(
    DATABASE_URL,
    pool_size=20,           # Concurrent requests
    max_overflow=40,        # Burst capacity
    pool_recycle=3600,      # 1 hour
    pool_pre_ping=True,     # Verify connections
    pool_timeout=30,        # Wait time
    echo_pool=False,        # Pool debug logging
)

# For background tasks
engine = create_async_engine(
    DATABASE_URL,
    pool_size=5,            # Fewer connections
    max_overflow=10,
    pool_recycle=7200,      # 2 hours
)

Connection Health Check:

from sqlalchemy import text

async def check_database_health(session: AsyncSession) -> bool:
    try:
        await session.execute(text("SELECT 1"))
        return True
    except Exception:
        return False

Content truncated.

When not to use it

  • When working with synchronous database operations
  • When the project does not use SQLAlchemy 2.0+ or FastAPI
  • When a different ORM or database access layer is preferred

Limitations

  • Requires SQLAlchemy 2.0+ for async features
  • Optimizations like eager loading must be explicitly configured
  • Troubleshooting N+1 problems requires SQL logging and specific loading strategies

How it compares

This approach provides structured patterns for async SQLAlchemy in FastAPI, including dependency injection and optimized relationship loading, which differs from manual setup that may lack these best practices.

Compared to similar skills

async-sqlalchemy-patterns side by side with the closest alternatives in the catalog.

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async-sqlalchemy-patterns (this skill)03moReviewAdvanced
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DB Schema Surgeon06moNo flagsAdvanced

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