Integrating Django REST Framework and GraphQL in a Single Django Project

You don’t have to choose between Django REST Framework (DRF) and GraphQL—both can coexist in your Django project, each serving distinct needs and complementing one another.

Why Combine DRF and GraphQL?

• Different Use Cases:
  DRF is excellent for building conventional RESTful APIs that are simple, widely adopted, and perfect for many client-side applications. Conversely, GraphQL shines when you need precise control over data retrieval, helping to avoid over-fetching or under-fetching—especially useful in modern, data-driven frontends with complex relationships.

• Gradual Migration:
  If you already have an existing DRF API, you can introduce GraphQL incrementally. This approach allows you to add new endpoints with GraphQL without rewriting your entire API at once.

• Hybrid Approach and API Versioning:
  Use DRF for straightforward CRUD operations while leveraging GraphQL’s powerful schema and type system for more complex queries and mutations. This setup can also help you manage API versioning—maintaining older versions via DRF while introducing newer features with GraphQL.

How to Integrate Them

1. Install the Necessary Packages

Run the following command to install both DRF and Graphene for Django:

pip install djangorestframework graphene-django

2. Configure GraphQL

• Create a Schema:
  In your Django app (or at the project level), create a schema.py file to define your GraphQL schema using the graphene library. For example:

  import graphene
  
  class Query(graphene.ObjectType):
      # Define your GraphQL queries here
      hello = graphene.String()
  
      def resolve_hello(root, info):
          return "Hello, GraphQL!"
  
  schema = graphene.Schema(query=Query)
  
  

• Add the GraphQL URL Pattern:
  In your urls.py, include a route for the GraphQL endpoint. Enabling graphiql=True provides an interactive explorer during development:

  from django.urls import path
  from graphene_django.views import GraphQLView
  
  urlpatterns = [
      # ... other URL patterns
      path('graphql/', GraphQLView.as_view(graphiql=True)),
  ]
  
  

3. Set Up DRF

Follow the standard DRF setup process:

• Define your serializers.
• Create viewsets.
• Register routes (e.g., under /api/).

4. Run Both APIs Together

Once configured, your project will serve:

• DRF endpoints (e.g., /api/) for traditional RESTful interactions.
• GraphQL endpoint (e.g., /graphql/) for flexible, query-driven data retrieval.

Example: Implementing a Book Model

Imagine you have a Book model in your Django app.

• DRF Implementation:
  You might create a BookSerializer and a BookViewSet to manage CRUD operations via endpoints like /api/books/.

• GraphQL Implementation:
  Define a GraphQL type for the Book model using DjangoObjectType:

  import graphene
  from graphene_django import DjangoObjectType
  from .models import Book
  
  class BookType(DjangoObjectType):
      class Meta:
          model = Book
  
  class Query(graphene.ObjectType):
      books = graphene.List(BookType)
  
      def resolve_books(root, info):
          return Book.objects.all()
  
  schema = graphene.Schema(query=Query)
  
  

Now, clients can query for books using GraphQL while still accessing the REST API for other operations.

Key Considerations

• Authentication:
  Use a unified authentication mechanism (e.g., JWT) across both DRF and GraphQL to ensure consistency. The graphene-django package integrates well with DRF’s authentication backends.

• Testing:
  Test your DRF and GraphQL endpoints independently to ensure that each works as expected.

• Schema Design:
  Invest time in designing a robust GraphQL schema—consider how your data is related and how clients will query it.

• Performance:
  Be mindful of performance, especially with complex GraphQL queries. Optimize your resolvers and database queries accordingly.

Conclusion

Integrating DRF and GraphQL into your Django project gives you the flexibility to choose the best tool for each task. Whether you prefer the simplicity of REST for basic CRUD operations or the versatility of GraphQL for complex, client-specific queries, you can harness the strengths of both to create efficient, scalable APIs.