REST vs. GraphQL: Understanding API Design Approaches

Last updated: Apr 13, 2025

1. Introduction: The Role of APIs
2. What is REST?

2.1 Core Principles
2.2 Example
2.3 Pros and Cons

3. What is GraphQL?

3.1 Core Principles
3.2 Example
3.3 Pros and Cons

4. REST vs. GraphQL: Key Differences
5. When to Use Which?
6. Conclusion

1. Introduction: The Role of APIs

Application Programming Interfaces (APIs) are fundamental to modern software development. They act as
contracts or intermediaries that allow different software systems (like a frontend web application and a
backend server) to communicate and exchange data. Choosing the right approach for designing these APIs is
crucial for application performance, scalability, and developer experience.

Two dominant paradigms for building web APIs today are REST (Representational State Transfer) and GraphQL.
While both enable client-server communication, typically over HTTP, they differ significantly in their
philosophy and implementation. This article explores the core concepts, strengths, weaknesses, and key
differences between REST and GraphQL.

2. What is REST?

REST is an architectural style, not a strict protocol or standard, for designing networked applications.
Introduced by Roy Fielding in his 2000 doctoral dissertation, REST relies on a stateless, client-server
communication model, typically leveraging the HTTP protocol. It’s built around the concept of resources.

2.1 Core Principles

Resources:Data and functionality are exposed as resources, identified by unique URIs
(Uniform Resource Identifiers), like/users/123or/products.
HTTP Verbs: Standard HTTP methods are used to perform actions (CRUD - Create, Read,
Update, Delete) on these resources:
- GET: Retrieve a resource.
- POST: Create a new resource.
- PUT/PATCH: Update an existing resource.
- DELETE: Remove a resource.
Statelessness:Each request from a client to the server must contain all the information
needed to understand and process the request. The server does not store any client context between requests.
Uniform Interface:REST defines a consistent way to interact with resources, simplifying
the architecture. This includes resource identification via URIs, resource manipulation through
representations (like JSON), self-descriptive messages, and HATEOAS (Hypermedia as the Engine of Application
State - links within responses guide the client to related actions/resources).
Representations:Resources are decoupled from their representation. Clients interact with
representations of resources, commonly in JSON format, but XML or others are possible.

2.2 Example

Imagine fetching user data:

Request 1: Get user details

GET /api/users/123

Response 1 (JSON):

{
    "id": 123,
    "name": "Alice",
    "email": "alice@example.com",
    "posts_url": "/api/users/123/posts" 
  }

Request 2: Get that user’s posts (based on the URL in the first response - HATEOAS)

GET /api/users/123/posts

Response 2 (JSON):

[
    { "postId": 45, "title": "First Post" },
    { "postId": 88, "title": "Second Post" }
  ]

Notice how multiple requests might be needed to gather related data.

2.3 Pros and Cons

Pros:

Simplicity & Familiarity:Built on standard HTTP methods and concepts, making it
relatively easy to understand and implement.
Scalability:Statelessness makes scaling horizontally easier.
Caching:Leverages standard HTTP caching mechanisms effectively (e.g., caching GET
responses).
Widespread Adoption & Tooling:Vast ecosystem of tools, libraries, and developer
knowledge.
Decoupling:Separates client and server concerns clearly.

Cons:

Over-fetching:Endpoints often return more data than the client needs for a specific
view. (e.g., getting full user details when only the name is needed).
Under-fetching:Clients often need to make multiple requests to different endpoints to
gather all required data for a single view (like the user/posts example above).
Versioning Challenges:Evolving REST APIs without breaking existing clients can be
challenging (often requires URL versioning like/v2/users).
Lack of Strong Typing (by default):Data structures are defined implicitly by
documentation (e.g., OpenAPI/Swagger) rather than enforced by the API structure itself.

3. What is GraphQL?

GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data.
Developed internally by Facebook and open-sourced in 2015, it provides a more efficient, powerful, and
flexible alternative to REST.

3.1 Core Principles

Query Language:Clients ask for exactly the data they need using a specific query syntax.
No more, no less.
Single Endpoint:Typically, a GraphQL API exposes a single endpoint (e.g.,/graphql)
that handles all queries and mutations (writes).
Typed Schema:The API’s capabilities are defined in a strong type system using the Schema
Definition Language (SDL). This schema acts as the contract between client and server.
Client-Dictated Data:The client specifies the shape and fields of the data it requires
in the query.
Introspection:Clients can query the schema itself to understand the available types,
fields, queries, and mutations. This enables powerful tooling.
Operations:Primarily uses Queries (read data), Mutations (write data), and Subscriptions
(real-time updates).

3.2 Example

Using the same user/posts scenario:

Client Query (sent via POST to /graphql):

query GetUserAndPosts {
    user(id: "123") {
      name
      posts {
        title
      }
    }
  }

Server Response (JSON):

{
    "data": {
      "user": {
        "name": "Alice",
        "posts": [
          { "title": "First Post" },
          { "title": "Second Post" }
        ]
      }
    }
  }

Notice how the client gets exactly the requested fields (name, posts.title) for the user in a single
request.

3.3 Pros and Cons

Pros:

Solves Over/Under-fetching:Clients get precisely the data they need in one request,
improving efficiency, especially for mobile clients.
Strong Typing & Introspection:The schema provides a reliable contract and enables
powerful developer tools, auto-completion, and code generation.
Declarative Data Fetching:Clients describe data needs naturally alongside UI components.
API Evolution:Easier to evolve APIs without versioning; fields can be deprecated and new
ones added without breaking existing clients.
Frontend/Backend Collaboration:The schema serves as clear documentation and contract.

Cons:

Caching Complexity:More complex than REST’s standard HTTP caching. Requires client-side
caching strategies (e.g., normalized caches like Apollo Client) or complex server-side setups.
Query Complexity & Performance:Complex, deeply nested queries can potentially overload
the server if not handled carefully (rate limiting, query depth limiting, etc.).
Learning Curve:Requires understanding the query language, schema design, resolvers, and
the GraphQL ecosystem.
File Uploads:Not handled natively in the spec; requires multipart request
implementations or separate upload endpoints.
Tooling/Ecosystem:While growing rapidly, the ecosystem (especially for certain backend
languages) might be less mature than REST’s in some areas.
Error Handling:Errors are typically returned within the JSON response (in anerrorsarray) with an HTTP 200 status, requiring different client-side handling than REST’s HTTP status codes.

4. REST vs. GraphQL: Key Differences

Feature
REST
GraphQL

Endpoint Structure
Multiple Endpoints (one per resource)
Typically Single Endpoint

Data Fetching Control
Server dictates data shape per endpoint
Client dictates exact data shape via query

Over/Under-fetching
Common issue
Eliminated by design

Protocol
Typically HTTP/HTTPS
Typically HTTP/HTTPS (transport-agnostic)

Data Format
Usually JSON (also XML, text, etc.)
Always JSON

Schema/Typing
No built-in schema; relies on external definitions (e.g., OpenAPI)
Strongly typed via Schema Definition Language (SDL)

Caching
Leverages standard HTTP caching easily
More complex; often requires client-side libraries or specific server strategies

API Evolution
Can be challenging (often requires versioning)
Easier via field addition/deprecation

Learning Curve
Generally lower (builds on HTTP)
Higher (new query language, schema design)

Introspection
Not built-in (requires documentation like Swagger/OpenAPI)
Built-in, allows querying the schema itself

5. When to Use Which?

The choice between REST and GraphQL depends heavily on the specific needs of your application and team.

Choose REST when:

You need a simple, resource-oriented API.
Leveraging HTTP caching is a high priority.
You are building public APIs where standardization and widespread tooling are crucial.
Your clients have predictable, stable data requirements.
Your team is highly familiar with HTTP and REST principles.
The overhead of setting up a GraphQL server seems excessive for the project scope.

Choose GraphQL when:

Clients (especially mobile apps) need flexible data fetching to minimize network usage (avoid
over-fetching).
Your application frontend has complex data requirements involving multiple related resources.
Your frontend evolves rapidly, requiring frequent changes to data needs without breaking the backend.
You need a strongly typed API contract between frontend and backend teams.
You are building an API gateway to aggregate data from multiple downstream services (microservices).
Developer experience benefits from introspection and typed schemas are highly valued.

It’s also possible to use both! For example, using GraphQL internally between microservices or for specific
client applications, while exposing simpler, public-facing APIs via REST.

6. Conclusion

REST and GraphQL represent two distinct, powerful approaches to API design. REST offers simplicity,
standardization, and leverages the web’s existing infrastructure effectively, making it a solid choice for
many resource-based APIs. GraphQL provides unparalleled flexibility and efficiency for clients fetching
complex data, solving the over/under-fetching problem inherent in many RESTful designs, albeit with increased
complexity in areas like caching and server implementation.

Neither is universally “better”; the best choice depends on the specific requirements of the application, the
nature of the clients consuming the API, performance considerations, and the experience of the development
team. Understanding the core principles and trade-offs of each paradigm is key to making an informed decision.

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