| Project | Status | |
|---|---|---|
| CI/CD |    |
|
| Quality | ||
| Community |  |
|
| Meta |       |
Check out the project documentation 📚 for more information.
A carefully crafted, thoroughly tested, optimized companion library for SQLAlchemy, offering features such as:
-
Sync and async repositories, featuring common CRUD and highly optimized bulk operations
-
Integration with major web frameworks including Litestar, Starlette, FastAPI, Sanic.
-
Custom-built alembic configuration and CLI with optional framework integration
-
Utility base classes with audit columns, primary keys and utility functions
-
Optimized JSON types including a custom JSON type for Oracle.
-
Pre-configured base classes with audit columns UUID or Big Integer primary keys and a sentinel column.
-
Synchronous and asynchronous repositories featuring:
- Common CRUD operations for SQLAlchemy models
- Bulk inserts, updates, upserts, and deletes with dialect-specific enhancements
- lambda_stmt when possible for improved query building performance
- Integrated counts, pagination, sorting, filtering with
LIKE,IN, and dates before and/or after.
-
Tested support for multiple database backends including:
- SQLite via aiosqlite or sqlite
- Postgres via asyncpg or psycopg3 (async or sync)
- MySQL via asyncmy
- Oracle via oracledb (async or sync) (tested on 18c and 23c)
- Google Spanner via spanner-sqlalchemy
- DuckDB via duckdb_engine
- Microsoft SQL Server via pyodbc or aioodbc
- CockroachDB via sqlalchemy-cockroachdb (async or sync)
pip install advanced-alchemyImportant
Check out the installation guide in our official documentation!
Advanced Alchemy includes a set of asynchronous and synchronous repository classes for easy CRUD operations on your SQLAlchemy models.
from advanced_alchemy.base import UUIDBase
from advanced_alchemy.filters import LimitOffset
from advanced_alchemy.repository import SQLAlchemySyncRepository
from sqlalchemy import create_engine
from sqlalchemy.orm import Mapped, sessionmaker
class User(UUIDBase):
# you can optionally override the generated table name by manually setting it.
__tablename__ = "user_account" # type: ignore[assignment]
email: Mapped[str]
name: Mapped[str]
class UserRepository(SQLAlchemySyncRepository[User]):
"""User repository."""
model_type = User
# use any compatible sqlalchemy engine.
engine = create_engine("duckdb:///:memory:")
session_factory = sessionmaker(engine, expire_on_commit=False)
# Initializes the database.
with engine.begin() as conn:
User.metadata.create_all(conn)
with session_factory() as db_session:
repo = UserRepository(session=db_session)
# 1) Create multiple users with `add_many`
bulk_users = [
{"email": 'cody@advanced-alchemy.dev', 'name': 'Cody'},
{"email": 'janek@advanced-alchemy.dev', 'name': 'Janek'},
{"email": 'peter@advanced-alchemy.dev', 'name': 'Peter'},
{"email": 'jacob@advanced-alchemy.dev', 'name': 'Jacob'}
]
objs = repo.add_many([User(**raw_user) for raw_user in bulk_users])
db_session.commit()
print(f"Created {len(objs)} new objects.")
# 2) Select paginated data and total row count. Pass additional filters as kwargs
created_objs, total_objs = repo.list_and_count(LimitOffset(limit=10, offset=0), name="Cody")
print(f"Selected {len(created_objs)} records out of a total of {total_objs}.")
# 3) Let's remove the batch of records selected.
deleted_objs = repo.delete_many([new_obj.id for new_obj in created_objs])
print(f"Removed {len(deleted_objs)} records out of a total of {total_objs}.")
# 4) Let's count the remaining rows
remaining_count = repo.count()
print(f"Found {remaining_count} remaining records after delete.")For a full standalone example, see the sample here
Advanced Alchemy includes an additional service class to make working with a repository easier. This class is designed to accept data as a dictionary or SQLAlchemy model and it will handle the type conversions for you.
Here's the same example from above but using a service to create the data:
from advanced_alchemy.base import UUIDBase
from advanced_alchemy.filters import LimitOffset
from advanced_alchemy import SQLAlchemySyncRepository, SQLAlchemySyncRepositoryService
from sqlalchemy import create_engine
from sqlalchemy.orm import Mapped, sessionmaker
class User(UUIDBase):
# you can optionally override the generated table name by manually setting it.
__tablename__ = "user_account" # type: ignore[assignment]
email: Mapped[str]
name: Mapped[str]
class UserRepository(SQLAlchemySyncRepository[User]):
"""User repository."""
model_type = User
class UserService(SQLAlchemySyncRepositoryService[User]):
"""User repository."""
repository_type = UserRepository
# use any compatible sqlalchemy engine.
engine = create_engine("duckdb:///:memory:")
session_factory = sessionmaker(engine, expire_on_commit=False)
# Initializes the database.
with engine.begin() as conn:
User.metadata.create_all(conn)
with session_factory() as db_session:
service = UserService(session=db_session)
# 1) Create multiple users with `add_many`
objs = service.create_many([
{"email": 'cody@advanced-alchemy.dev', 'name': 'Cody'},
{"email": 'janek@advanced-alchemy.dev', 'name': 'Janek'},
{"email": 'peter@advanced-alchemy.dev', 'name': 'Peter'},
{"email": 'jacob@advanced-alchemy.dev', 'name': 'Jacob'}
])
print(objs)
print(f"Created {len(objs)} new objects.")
# 2) Select paginated data and total row count. Pass additional filters as kwargs
created_objs, total_objs = service.list_and_count(LimitOffset(limit=10, offset=0), name="Cody")
print(f"Selected {len(created_objs)} records out of a total of {total_objs}.")
# 3) Let's remove the batch of records selected.
deleted_objs = service.delete_many([new_obj.id for new_obj in created_objs])
print(f"Removed {len(deleted_objs)} records out of a total of {total_objs}.")
# 4) Let's count the remaining rows
remaining_count = service.count()
print(f"Found {remaining_count} remaining records after delete.")Advanced Alchemy works with nearly all Python web frameworks. Several helpers for popular libraries are included, and additional PRs to support others are welcomed.
Advanced Alchemy is the official SQLAlchemy integration for Litestar.
In addition to installed with pip install advanced-alchemy, it can also be installed installed as a Litestar extra with pip install litestar[sqlalchemy].
from litestar import Litestar
from litestar.plugins.sqlalchemy import SQLAlchemyPlugin, SQLAlchemyAsyncConfig
# alternately...
# from advanced_alchemy.extensions.litestar.plugins import SQLAlchemyPlugin
# from advanced_alchemy.extensions.litestar.plugins.init.config import SQLAlchemyAsyncConfig
alchemy = SQLAlchemyPlugin(
config=SQLAlchemyAsyncConfig(connection_string="sqlite+aiosqlite:///test.sqlite"),
)
app = Litestar(plugins=[alchemy])For a full Litestar example, check here
from fastapi import FastAPI
from advanced_alchemy.config import SQLAlchemyAsyncConfig
from advanced_alchemy.extensions.starlette import StarletteAdvancedAlchemy
app = FastAPI()
alchemy = StarletteAdvancedAlchemy(
config=SQLAlchemyAsyncConfig(connection_string="sqlite+aiosqlite:///test.sqlite"), app=app,
)For a full CRUD example, see here
from starlette.applications import Starlette
from advanced_alchemy.config import SQLAlchemyAsyncConfig
from advanced_alchemy.extensions.starlette import StarletteAdvancedAlchemy
app = Starlette()
alchemy = StarletteAdvancedAlchemy(
config=SQLAlchemyAsyncConfig(connection_string="sqlite+aiosqlite:///test.sqlite"), app=app,
)from sanic import Sanic
from sanic_ext import Extend
from advanced_alchemy.config import SQLAlchemyAsyncConfig
from advanced_alchemy.extensions.sanic import SanicAdvancedAlchemy
app = Sanic("AlchemySanicApp")
alchemy = SanicAdvancedAlchemy(
sqlalchemy_config=SQLAlchemyAsyncConfig(connection_string="sqlite+aiosqlite:///test.sqlite"),
)
Extend.register(alchemy)All Jolt projects will always be a community-centered, available for contributions of any size.
Before contributing, please review the contribution guide.
If you have any questions, reach out to us on Discord, our org-wide GitHub discussions page, or the project-specific GitHub discussions page.
A Jolt Organization Project