From REST to GraphQL: Transitioning APIs in Python and Java

6 min readOct 3, 2024

In recent years, there’s been a growing shift in the way developers approach building APIs. For decades, REST (Representational State Transfer) has been the standard for designing APIs, providing a simple and structured way to interact with resources. However, as applications grow in complexity, REST’s rigid structure can become a limitation. Enter GraphQL, an open-source query language developed by Facebook that provides more flexibility, efficiency, and developer-friendly API interactions.

In this article, we’ll explore the transition from REST to GraphQL in both Python and Java using frameworks like Graphene and GraphQL-Java. We’ll discuss the performance benefits, flexibility, and challenges of migrating large-scale APIs, along with practical insights on how this transition can benefit your projects.

What’s the Difference Between REST and GraphQL?

Before diving into the technical implementation, it’s important to understand the key differences between REST and GraphQL:

REST:

Multiple Endpoints: Each resource (users, products, etc.) has its own endpoint, requiring different API calls to fetch related data.
Over-fetching & Under-fetching: REST often forces you to retrieve too much or too little data. For example, fetching /users may return all users, but additional requests might be needed to fetch detailed information.
Standard Methods: CRUD operations in REST are tied to HTTP methods like GET, POST, PUT, and DELETE.

GraphQL:

Single Endpoint: GraphQL uses one endpoint to handle all API requests.
Precise Data: Clients specify exactly what data they need, reducing over-fetching and under-fetching.
Strongly Typed: GraphQL relies on a schema that defines the types and structure of the API.

Why Transition to GraphQL?

1. Flexibility

GraphQL allows clients to request exactly the data they need, in the shape they need it. This is especially beneficial for front-end developers who need to build dynamic and fast user interfaces. In REST, the API is rigid, requiring developers to either over-fetch data or make multiple requests.

2. Improved Performance

GraphQL can improve performance by consolidating multiple REST endpoints into a single request. By retrieving only the data that is required, network usage is optimized, especially in mobile or low-bandwidth environments.

3. Strong Typing and Self-Documentation

GraphQL APIs come with a schema that defines the types and relationships between data. This makes it easier to onboard new developers and create clear, self-documenting APIs, in contrast to the often loosely defined REST endpoints.

4. Challenges

Complexity: Migrating from REST to GraphQL can introduce complexity, especially in handling caching, authentication, and error handling.
Learning Curve: Teams must get accustomed to new tools and frameworks for creating and querying GraphQL APIs.

Transitioning from REST to GraphQL in Python

GraphQL in Python: Introducing Graphene

In Python, Graphene is the most popular framework for building GraphQL APIs. It integrates well with Django, Flask, and other popular web frameworks.

Example: A Simple REST API in Python

Let’s start with a simple REST API built with Flask:

from flask import Flask, jsonify

app = Flask(__name__)

users = [
    {"id": 1, "name": "Alice", "email": "alice@example.com"},
    {"id": 2, "name": "Bob", "email": "bob@example.com"}
]

@app.route('/users', methods=['GET'])
def get_users():
    return jsonify(users)


if __name__ == '__main__':
    app.run()

This API returns a list of users. However, to fetch detailed information about a user’s posts or orders, we’d need to make additional requests.

Migrating to GraphQL with Graphene

Now let’s build the same API using Graphene:

import graphene

class User(graphene.ObjectType):
    id = graphene.Int()
    name = graphene.String()
    email = graphene.String()

class Query(graphene.ObjectType):
    users = graphene.List(User)
    def resolve_users(parent, info):
        return [
            {"id": 1, "name": "Alice", "email": "alice@example.com"},
            {"id": 2, "name": "Bob", "email": "bob@example.com"}
        ]

schema = graphene.Schema(query=Query)
query = '''
{
  users {
    id
    name
    email
  }
}
'''
result = schema.execute(query)
print(result.data)

In this case, we create a User type and a Query to retrieve the users. The client can specify exactly what fields they need (e.g., id, name, email), eliminating the need for over-fetching.

Transitioning Strategy in Python

Use Both REST and GraphQL: Many teams start by offering both REST and GraphQL APIs in parallel. This allows developers to migrate gradually.
GraphQL as a Gateway: You can keep your existing REST services but use GraphQL as a “gateway” that wraps around them, providing a more flexible API to the client.

Transitioning from REST to GraphQL in Java

GraphQL in Java: Introducing GraphQL-Java

For Java developers, the GraphQL-Java library is the most widely used solution for building GraphQL APIs. It integrates easily with Spring Boot and other popular Java frameworks.

Example: A Simple REST API in Java with Spring Boot

Here’s a basic REST API in Spring Boot:

@RestController
public class UserController {

private List<User> users = Arrays.asList(
        new User(1, "Alice", "alice@example.com"),
        new User(2, "Bob", "bob@example.com")
    );
    @GetMapping("/users")
    public List<User> getUsers() {
        return users;
    }
}

This endpoint returns a list of users, but just like in Python, additional endpoints would be required for related resources.

Migrating to GraphQL with GraphQL-Java

Now let’s create the same API using GraphQL-Java:

import graphql.schema.*;
import org.springframework.stereotype.Component;

@Component
public class GraphQLProvider {
    private GraphQL graphQL;
    public GraphQLProvider() {
        GraphQLSchema schema = GraphQLSchema.newSchema()
            .query(GraphQLObjectType.newObject()
                .name("Query")
                .field(GraphQLFieldDefinition.newFieldDefinition()
                    .name("users")
                    .type(new GraphQLList(userType()))
                    .dataFetcher(environment -> Arrays.asList(
                        new User(1, "Alice", "alice@example.com"),
                        new User(2, "Bob", "bob@example.com")
                    ))
                )
                .build())
            .build();
        this.graphQL = GraphQL.newGraphQL(schema).build();
    }
    public GraphQL getGraphQL() {
        return graphQL;
    }
    private GraphQLObjectType userType() {
        return GraphQLObjectType.newObject()
            .name("User")
            .field(GraphQLFieldDefinition.newFieldDefinition().name("id").type(GraphQLInt))
            .field(GraphQLFieldDefinition.newFieldDefinition().name("name").type(GraphQLString))
            .field(GraphQLFieldDefinition.newFieldDefinition().name("email").type(GraphQLString))
            .build();
    }
}

Here, we define a GraphQL schema for querying users, similar to the REST endpoint. GraphQL-Java allows you to define the schema programmatically, giving you fine control over the structure of the API.

Transitioning Strategy in Java

Use Spring Boot: GraphQL-Java integrates well with Spring Boot, making the transition smoother for developers familiar with Spring.
Hybrid Approach: Just like in Python, you can start with a hybrid approach by offering both REST and GraphQL endpoints while gradually moving more functionality to GraphQL.

Real-World Case Studies

1. GitHub’s Transition to GraphQL

GitHub was one of the first major platforms to adopt GraphQL. Previously, their REST API had multiple endpoints for users, repositories, and issues, resulting in over-fetching and the need for numerous requests to gather data. By transitioning to GraphQL, GitHub was able to consolidate their API into a single, flexible query interface, improving both developer experience and performance.

2. Shopify’s GraphQL API

Shopify’s REST API had limitations in terms of the number of requests and the amount of data clients could retrieve. With GraphQL, Shopify allowed clients to request exactly the data they needed, reducing API calls and improving performance on both ends.

Challenges of Migrating Large-Scale APIs

1. Complexity in Caching

While caching is straightforward in REST (since each endpoint represents a resource), caching in GraphQL can be more complex. Solutions like Apollo Server and GraphQL-Java provide tools to cache responses, but it requires more fine-tuning.

2. Authentication and Authorization

Handling authentication and authorization is more challenging in GraphQL because all requests hit a single endpoint. Developers need to be mindful of securing individual fields and ensuring that sensitive data is protected.

3. Over-fetching and N+1 Problem

While GraphQL prevents over-fetching at the client level, it can introduce the N+1 query problem on the server side, where multiple queries are made to fetch related data. Libraries like Dataloader can help mitigate this issue by batching and optimizing queries.

Conclusion

The transition from REST to GraphQL is gaining momentum in both Python and Java communities. While REST has served us well for many years, GraphQL’s flexibility, performance improvements, and precise querying capabilities make it a compelling option for modern API development. With tools like Graphene in Python and GraphQL-Java in Java, developers can gradually migrate their existing APIs, reaping the benefits of faster, more efficient data retrieval.

Though there are challenges — especially for large-scale migrations — the benefits of GraphQL make it worth considering for any project looking to improve API performance and developer experience.