Migrate Django API

Migrate Django API#

This tutorial will use a project-driven approach to migrate a simple API from Django to FastAPI. We’ll take a Django-based API and incrementally transition it to FastAPI, covering the application structure, endpoints, and a seamless data migration process using SQLAlchemy.

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This guide is intended for developers who want to convert their API infrastructure while maintaining database consistency.

Project Setup and Initial Structure in Django#

Start by creating a Django project for a basic API that handles users and tasks. This application will include endpoints to create, retrieve, update, and delete records.

Step 1: Setting Up the Django Project#

  1. Create a new Django project and app:

    django-admin startproject project_migration
cd project_migration
python manage.py startapp tasks

    Here, django-admin initiates a new project named project_migration, and startapp creates a tasks app within the project to handle user and task APIs.

  2. In settings.py, configure the database and add the tasks app to INSTALLED_APPS.

  3. Define a simple data model in tasks/models.py for Users and Tasks:

    from django.db import models

class User(models.Model):
    username = models.CharField(max_length=100, unique=True)
    email = models.EmailField(unique=True)

class Task(models.Model):
    user = models.ForeignKey(User, on_delete=models.CASCADE)
    title = models.CharField(max_length=200)
    description = models.TextField(blank=True)
    is_completed = models.BooleanField(default=False)

    This code defines a User model with a unique username and email, and a Task model linked to a User, allowing each user to have multiple tasks. The Task model includes fields for task details and completion status.

  4. Create and apply migrations:

    python manage.py makemigrations
python manage.py migrate

    makemigrations generates migration files for model changes, and migrate applies them to the database, creating tables for the User and Task models.

  5. Implement basic views and URL routes in tasks/views.py and tasks/urls.py:

    from django.shortcuts import get_object_or_404
from .models import User, Task
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json

@csrf_exempt
def create_user(request):
    if request.method == 'POST':
        data = json.loads(request.body)
        user = User.objects.create(username=data['username'], email=data['email'])
        return JsonResponse({'id': user.id, 'username': user.username, 'email': user.email})

@csrf_exempt
def list_tasks(request, user_id):
    tasks = Task.objects.filter(user_id=user_id)
    return JsonResponse([{'id': task.id, 'title': task.title, 'is_completed': task.is_completed} for task in tasks], safe=False)

    The create_user view handles POST requests to add a new user, and list_tasks fetches tasks related to a specified user_id. Each function returns a JSON response, making the endpoints accessible for external requests.

  6. Configure the main URL routes in project_migration/urls.py:

    from django.urls import path, include

urlpatterns = [
    path('api/', include('tasks.urls')),
]

    Here, the main URL configuration includes routes from the tasks app under the /api/ endpoint, centralizing the project’s API structure.

At this point, we have a functioning API in Django. Now, let’s begin the migration process by setting up FastAPI.

Step 2: Setting Up the FastAPI Project#

Create a new directory structure for FastAPI:

mkdir fastapi_migration
cd fastapi_migration

Install FastAPI and an ASGI server, such as uvicorn, for local testing:

pip install fastapi uvicorn sqlalchemy pydantic

This sets up the fastapi_migration folder for our new project, installing FastAPI, uvicorn for running the app, and sqlalchemy and pydantic for data modeling and validation.

Step 3: Setting Up SQLAlchemy Models in FastAPI#

Define SQLAlchemy models similar to Django’s ORM models.

  1. database.py: Set up the connection to the database using SQLAlchemy.

    from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"

engine = create_engine(SQLALCHEMY_DATABASE_URL)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()

    This code establishes a SQLite database connection using SQLAlchemy. SessionLocal creates sessions for database operations, while Base serves as a base class for model definitions.

  2. models.py: Recreate the Django models in SQLAlchemy.

    from sqlalchemy import Column, Integer, String, Boolean, ForeignKey
from sqlalchemy.orm import relationship
from .database import Base

class User(Base):
    __tablename__ = "users"
    id = Column(Integer, primary_key=True, index=True)
    username = Column(String, unique=True, index=True)
    email = Column(String, unique=True, index=True)

class Task(Base):
    __tablename__ = "tasks"
    id = Column(Integer, primary_key=True, index=True)
    title = Column(String, index=True)
    description = Column(String)
    is_completed = Column(Boolean, default=False)
    user_id = Column(Integer, ForeignKey("users.id"))

    user = relationship("User")

    The models here mirror the Django structure, with a User table and a Task table that includes a foreign key to User, enforcing relational integrity between tasks and users.

  3. schemas.py: Define Pydantic models to validate input data.

    from pydantic import BaseModel

class UserBase(BaseModel):
    username: str
    email: str

class TaskBase(BaseModel):
    title: str
    description: str
    is_completed: bool

    These Pydantic models handle validation and serialization of user and task data, ensuring correct data structures for input and output across the API.

  4. crud.py: Implement the CRUD functions using SQLAlchemy’s session.

    from sqlalchemy.orm import Session
from . import models, schemas

def create_user(db: Session, user: schemas.UserBase):
    db_user = models.User(username=user.username, email=user.email)
    db.add(db_user)
    db.commit()
    db.refresh(db_user)
    return db_user

def get_tasks(db: Session, user_id: int):
    return db.query(models.Task).filter(models.Task.user_id == user_id).all()

    create_user adds a new user to the database, while get_tasks retrieves tasks associated with a user ID, utilizing SQLAlchemy’s ORM for data interactions.

Step 4: Exporting Django Data#

In the Django project, use the following script to export data:

import json
from tasks.models import User, Task

data = {
    "users": list(User.objects.values()),
    "tasks": list(Task.objects.values())
}

with open('data.json', 'w') as f:
    json.dump(data, f)

This script collects all User and Task records into dictionaries and writes them to a JSON file, data.json, which will be imported into the FastAPI project.

Step 5: Importing Data into FastAPI#

In the FastAPI project, create an import script:

import json
from sqlalchemy.orm import Session
from .database import SessionLocal, engine
from . import models

models.Base.metadata.create_all(bind=engine)

def import_data():
    db = SessionLocal()
    with open('data.json') as f:
        data = json.load(f)
        for user_data in data['users']:
            user = models.User(id=user_data['id'], username=user_data['username'], email=user_data['email'])
            db.add(user)
        db.commit()

        for task_data in data['tasks']:
            task = models.Task(id=task_data['id'], title=task_data['title'], is_completed=task_data['is_completed'], user_id=task_data['user_id'])
            db.add(task)
        db.commit()

This function reads data.json and inserts each record into the FastAPI database. The Base.metadata.create_all call ensures tables exist before importing data, and each add operation pushes individual records to the database.

Conclusion#

The migration process shows how to transition from Django to FastAPI with data integrity maintained through SQLAlchemy. The provided code snippets create a functional API in FastAPI, complete with database migrations and CRUD operations, serving as a reusable guide for real-world applications where a shift to FastAPI enhances performance.