sqlalchemyseed 2.6.0

SQLAlchemy Seeder

sqlalchemyseed

Sqlalchemy seeder that supports nested relationships.

Supported file types

• json
• yaml
• csv

Installation

Default installation

pip install sqlalchemyseed

Quickstart

main.py

from sqlalchemyseed import load_entities_from_json
from sqlalchemyseed import Seeder
from db import session

# load entities
entities = load_entities_from_json('data.json')

# Initializing Seeder
seeder = Seeder(session)

# Seeding
seeder.seed(entities)

# Committing
session.commit()  # or seeder.session.commit()

data.json

{
    "model": "models.Person",
    "data": [
        {
            "name": "John March",
            "age": 23
        },
        {
            "name": "Juan Dela Cruz",
            "age": 21
        }
    ]
}

Works with SQLModel & FastAPI

A SQLModel table=True class is a SQLAlchemy model, so sqlalchemyseed works with SQLModel and FastAPI out of the box — same seed files, same seeders, verified in CI:

# app/models.py
from typing import Optional

from sqlmodel import Field, SQLModel


class Hero(SQLModel, table=True):
    id: Optional[int] = Field(default=None, primary_key=True)
    name: str

# seed.py
from sqlmodel import Session, SQLModel, create_engine

from app.models import Hero  # registers the table on SQLModel.metadata
from sqlalchemyseed import Seeder

engine = create_engine("sqlite:///database.db")
SQLModel.metadata.create_all(engine)

with Session(engine) as session:
    seeder = Seeder(session)
    seeder.seed({"model": "app.models.Hero", "data": [{"name": "Deadpond"}]})
    session.commit()

Seed at app startup, in tests via the bundled pytest plugin, or from the CLI — see the FastAPI & SQLModel docs.

Editor validation (JSON Schema)

A JSON Schema for seed files ships with the package and lives in the repo at src/sqlalchemyseed/res/schema.json. Point your editor at it to get autocomplete and inline validation as you write fixtures.

In the URLs below, replace v2.6.0 with the version of sqlalchemyseed you have installed, so the editor validates against the same rules as your runtime.

For YAML files, add a modeline as the first line:

# yaml-language-server: $schema=https://raw.githubusercontent.com/jedymatt/sqlalchemyseed/v2.6.0/src/sqlalchemyseed/res/schema.json
- model: models.Person
  data:
    name: John March
    age: 23

For JSON files (which can't carry a modeline), associate the schema by glob in your editor settings, e.g. VS Code .vscode/settings.json:

{
    "yaml.schemas": {
        "https://raw.githubusercontent.com/jedymatt/sqlalchemyseed/v2.6.0/src/sqlalchemyseed/res/schema.json": "seeds/**/*.yaml"
    },
    "json.schemas": [
        {
            "fileMatch": ["seeds/**/*.json"],
            "url": "https://raw.githubusercontent.com/jedymatt/sqlalchemyseed/v2.6.0/src/sqlalchemyseed/res/schema.json"
        }
    ]
}

The schema covers the full format including the ! relationship prefix; the filter key it allows is only honored by HybridSeeder.

Command-line usage

Seed a database directly from data files without writing Python:

sqlalchemyseed data.json --url sqlite:///app.db

The command accepts one or more files and/or directories (a directory seeds every .json/.yaml/.yml file inside it, in sorted order):

sqlalchemyseed seeds/ --url "$DATABASE_URL"
sqlalchemyseed a.json b.yaml --url sqlite:///app.db

The database URL may be passed with --url or the DATABASE_URL environment variable. Model paths in the data files (e.g. models.Person) are resolved against the current working directory, so run the command from your project root.

Options:

• --dry-run — seed inside a transaction, then roll back (validate without writing)
• --seeder hybrid — use HybridSeeder instead of the default Seeder
• --model models.Person — required for CSV inputs, which are not self-describing
• --ref-prefix — override the relationship reference prefix (default !)

The same command is available as a module:

python -m sqlalchemyseed data.json --url sqlite:///app.db

Testing with pytest

Installing sqlalchemyseed alongside pytest registers a plugin that loads fixture files into a transactionally-isolated session. Provide one engine fixture in your conftest.py; the plugin supplies sqlalchemyseed_session (rolled back after every test) and a seed factory.

# conftest.py
import pytest
from sqlalchemy import create_engine, event
from sqlalchemy.pool import StaticPool

from myapp.models import Base


@pytest.fixture(scope="session")
def engine():
    # StaticPool keeps a single in-memory connection alive so the schema you
    # create is visible to the test session. A file-based or server database
    # needs no such tweak — just return your usual engine.
    engine = create_engine(
        "sqlite://",
        connect_args={"check_same_thread": False},
        poolclass=StaticPool,
    )

    # SQLite only: hand transaction control to SQLAlchemy so an explicit
    # commit() inside a test lands on a savepoint and is rolled back with the
    # outer transaction. Left to itself the pysqlite driver commits straight to
    # the database and the per-test rollback cannot undo it. Other databases
    # (PostgreSQL, MySQL) need neither listener.
    @event.listens_for(engine, "connect")
    def _sqlite_no_driver_begin(dbapi_connection, connection_record):
        dbapi_connection.isolation_level = None

    @event.listens_for(engine, "begin")
    def _sqlite_emit_begin(connection):
        connection.exec_driver_sql("BEGIN")

    Base.metadata.create_all(engine)
    return engine

# test_people.py
from sqlalchemy import select

from myapp.models import Person


def test_people_are_seeded(seed, sqlalchemyseed_session):
    seeder = seed("tests/data/people.yaml")
    people = sqlalchemyseed_session.scalars(select(Person)).all()
    assert len(people) == 2
    assert seeder.instances[0].name == "Alice"

seed() accepts the same inputs as the library: .json, .yaml/.yml, and .csv files. CSV is not self-describing, so pass the model: seed("people.csv", model="myapp.models.Person"). Use seeder="hybrid" for the HybridSeeder, and ref_prefix=... to override the relationship reference prefix. Every test runs inside a transaction that is rolled back afterward, so tests never see each other's rows.

Note: the plugin registers fixtures named engine, sqlalchemyseed_session, and seed. Defining your own engine fixture is how you plug in your database; if you already use those names for something else, your definitions take precedence (pytest resolves conftest fixtures over plugin fixtures).

Async usage

If your application only has an AsyncSession, use AsyncSeeder and AsyncHybridSeeder. They accept the same entities as their sync counterparts and run the seeding through AsyncSession.run_sync, so filter-key queries execute against your async driver.

Install with the async extra (pulls in greenlet); you also need an async driver such as aiosqlite or asyncpg:

pip install "sqlalchemyseed[async]" aiosqlite

from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
from sqlalchemyseed import AsyncSeeder

engine = create_async_engine("sqlite+aiosqlite:///app.db")

async with AsyncSession(engine) as session:
    seeder = AsyncSeeder(session)
    await seeder.seed(entities)
    await session.commit()

Use AsyncHybridSeeder when the entities contain a filter key, exactly as you would reach for HybridSeeder in synchronous code.

Documentation

https://sqlalchemyseed.readthedocs.io/

Found Bug?

Report here in this link: https://github.com/jedymatt/sqlalchemyseed/issues

Want to contribute?

First, Clone this repository.

This project uses uv for dependency management and running tasks.

Install dev dependencies

Inside the folder, sync the environment (uv creates the virtualenv and installs the project plus dev dependencies):

uv sync

Run tests

uv run pytest

Run the tests against a specific Python version (uv downloads it if needed):

uv run --python 3.14 pytest

Run the tests against the lowest supported dependencies (e.g. SQLAlchemy 2.0):

uv run --resolution lowest-direct pytest

Run tests with coverage:

uv run coverage run -m pytest

Autobuild documentation

sphinx-autobuild docs docs/_build/html