paint-brush
How to set up FastAPI, Ormar, and Alembicby@amalshaji
4,505 reads
4,505 reads

How to set up FastAPI, Ormar, and Alembic

by Amal ShajiSeptember 11th, 2021
Read on Terminal Reader
Read this story w/o Javascript
tldt arrow

Too Long; Didn't Read

[FastAPI] is the 3rd most popular python web framework. [Ormar] is a mini-async ORM for python. [Pydantic] is built on top of [Alembic] for migrations. Pydantic is very useful for your FastAPI application. You can create an Ormar model with three basic fields, and use the same as the database. You're modifying a single line to create a single database table and create a database. Database and models are easy to use in the project.

Company Mentioned

Mention Thumbnail
featured image - How to set up FastAPI, Ormar, and Alembic
Amal Shaji HackerNoon profile picture


If you're reading this, chances are you’re already familiar with FastAPI and SQLAlchemy.


Still, I'll give a little introduction to both these libraries.

FastAPI

FastAPI is the 3rd most popular python web framework. The factors like asynchronous views, easy to learn, and fast setup have contributed to its quick adoption.

SQLAlchemy


“SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL.

It provides a full suite of well-known enterprise-level persistence patterns, designed for efficient and high-performing database access, adapted into a simple and Pythonic domain language.” - Source.


It is the most popular ORM for python, primarily seen in use with Flask.


Ormar is a mini async ORM for python. It uses SQLAlchemy for building queries, databases for asynchronous execution of queries, and Pydantic for data validation. You can create an Ormar model and generate Pydantic models from it.


If you have read my post, Pydantic for FastAPI, you will understand how Pydantic is very useful for your FastAPI application.


SQLAlchemy uses Alembic for migrations. Since Ormar is built on top of SQLAlchemy, we can use the same for migrations.

Setup the Project

mkdir fastapi-ormar-alembic && cd $_
mkdir .venv
pipenv install fastapi uvicorn ormar alembic aiosqlite

Setup the Database and Models


Pinned dependencies are available in the Piplock file in the source code repository.


Create a new file db.pyat the root of the project. This file will contain the database setup and an example table.


import databases
import ormar
import sqlalchemy


database = databases.Database("sqlite:///db.sqlite")
metadata = sqlalchemy.MetaData()


class BaseMeta(ormar.ModelMeta):
    database = database
    metadata = metadata


class Users(ormar.Model):
    class Meta(BaseMeta):
        tablename = "users"

    id: int = ormar.Integer(primary_key=True)
    email: str = ormar.String(max_length=64, unique=True)
    password: str = ormar.String(max_length=128)


Creating a BaseMeta lets you add the database and metadata variables to all your models.

We created a simple model with three basic fields, now let's set up migrations with Alembic.

The Migrations


(fastapi-ormar-alembic) $ alembic init migrations


Your project structure should look like this:


├── Pipfile
├── Pipfile.lock
├── alembic.ini
├── db.py
├── db.sqlite
├── main.py
└── migrations


Add the database URL to the alembic.ini file. You're modifying a single line:


sqlalchemy.url = sqlite:///db.sqlite


Now, tell the migration environment where our metadata is stored.


Add(modify) the following in migrations/env.py:


...
from db import BaseMeta
...


target_metadata = BaseMeta.metadata


Finally, create the migration script. You can use the --autogenerate option to generate migrations based on the metadata automatically:


(fastapi-ormar-alembic) $ alembic revision --autogenerate -m "Added users table"
INFO  [alembic.runtime.migration] Context impl SQLiteImpl.
INFO  [alembic.runtime.migration] Will assume non-transactional DDL.
INFO  [alembic.autogenerate.compare] Detected added table 'users'
  Generating /home/amalshaji/Workspace/Python/blog-code-repository/fastapi-ormar-
  alembic/migrations/versions/c07fe5d55962_added_users_table.py ...  done


Now, run migrations:


(fastapi-ormar-alembic) $ alembic upgrade head


This produced a new file: migrations/versions/c07fe5d55962_added_users_table.py


File name inferred from the migration output.


"""Added users table

Revision ID: c07fe5d55962
Revises:
Create Date: 2021-08-14 11:55:46.845709

"""
from alembic import op
import sqlalchemy as sa


# revision identifiers, used by Alembic.
revision = 'c07fe5d55962'
down_revision = None
branch_labels = None
depends_on = None


def upgrade():
    # ### commands auto generated by Alembic - please adjust! ###
    op.create_table('users',
    sa.Column('id', sa.Integer(), nullable=False),
    sa.Column('email', sa.String(length=64), nullable=False),
    sa.Column('password', sa.String(length=128), nullable=False),
    sa.PrimaryKeyConstraint('id'),
    sa.UniqueConstraint('email')
    )
    # ### end Alembic commands ###


def downgrade():
    # ### commands auto generated by Alembic - please adjust! ###
    op.drop_table('users')
    # ### end Alembic commands ###


A migration script is easy to understand.


Apart from the few metadata on the top, the upgrade and downgrade functions play a significant role. The upgrade function adds all the changes we have made to the model, and the downgrade function reverts the changes made to the database.


It worked as expected. Now let's modify our table to add a new field and run migration. Modify the db.py like so:


class Users(ormar.Model):
    class Meta(BaseMeta):
        tablename = "users"

    id: int = ormar.Integer(primary_key=True)
    email: str = ormar.String(max_length=64, unique=True)
    password: str = ormar.String(max_length=128)
    is_active: bool = ormar.Boolean(default=True) # new


Run the migration:


(fastapi-ormar-alembic) $ alembic revision --autogenerate -m "Added is_active to users table"
INFO  [alembic.runtime.migration] Context impl SQLiteImpl.
INFO  [alembic.runtime.migration] Will assume non-transactional DDL.
INFO  [alembic.autogenerate.compare] Detected added column 'users.is_active'
  Generating /home/amalshaji/Workspace/Python/blog-code-repository/fastapi-ormar-
  alembic/migrations/versions/026a9a23ebbe_added_is_active_to_users_table.py ...  done

(fastapi-ormar-alembic) $ alembic upgrade head


This created a new file, migrations/versions/026a9a23ebbe_added_is_active_to_users_table.py:


"""Added is_active to users table

Revision ID: 026a9a23ebbe
Revises: c07fe5d55962
Create Date: 2021-08-14 12:20:36.817128

"""
from alembic import op
import sqlalchemy as sa


# revision identifiers, used by Alembic.
revision = '026a9a23ebbe'
down_revision = 'c07fe5d55962'
branch_labels = None
depends_on = None


def upgrade():
    # ### commands auto generated by Alembic - please adjust! ###
    op.add_column('users', sa.Column('is_active', sa.Boolean(), nullable=True))
    # ### end Alembic commands ###


def downgrade():
    # ### commands auto generated by Alembic - please adjust! ###
    op.drop_column('users', 'is_active')
    # ### end Alembic commands ###


Let’s verify the same by checking the database schema:


$ sqlite3 db.sqlite
SQLite version 3.31.1 2020-01-27 19:55:54
Enter ".help" for usage hints.
sqlite> .schema users
CREATE TABLE users (
        id INTEGER NOT NULL,
        email VARCHAR(64) NOT NULL,
        password VARCHAR(128) NOT NULL, is_active BOOLEAN,
        PRIMARY KEY (id),
        UNIQUE (email)
);
sqlite> .quit


Now that we have set up Ormar + Alembic let's see how to initialize our database connection in the fastAPI application.

Wiring up FastAPI Application


Create a main.py in the root of the project:


from fastapi import FastAPI

from db import database

app = FastAPI()


@app.on_event("startup")
async def startup():
    if not database.is_connected:
        await database.connect()


@app.on_event("shutdown")
async def shutdown():
    if database.is_connected:
        await database.disconnect()


We used the FastAPI's (Starlette's) startup and shutdown events to create/close the database connections. We are creating a connection pool.


After a database connection is created, it is added to the pool and used again for another request. The process essentially removes the time taken to make a new connection.

Handling multiple ormar tables and migrations

Let's say you have a db.py with the following BaseMeta definition.


class BaseMeta(ormar.ModelMeta):
    database = database
    metadata = metadata


And ormar models in different files.


  1. accounts/models.py


class Users(ormar.Model):
  class Meta(BaseMeta):
      tablename = "users"

  ...


2. posts/models.py


class Posts(ormar.Model):
  class Meta(BaseMeta):
      tablename = "posts"

  ...


For the automatic migration to work, you need to import the models in your env.py


from accounts.models import Users
from posts.models import Posts
from db import BaseMeta


target_metadata = BaseMeta.metadata


There you have it. Your FastAPI application should be up and running now.


First seen here.