GraphQL vs REST: The Future of API Development

For over a decade, REST (Representational State Transfer) has been the de facto standard for designing APIs. Its principles of statelessness and structured access to resources built the web we know today. Yet, as applications have become more complex and data-driven, the limitations of REST have become increasingly apparent.

Enter GraphQL, a technology born out of Facebook’s need to build rich, complex mobile applications efficiently. It’s not a library or a framework; it’s a query language for your API and a server-side runtime for executing those queries. GraphQL doesn’t aim to replace REST entirely but offers a powerful, flexible, and efficient alternative that is revolutionizing how developers think about data fetching.

The Problem: A Tale of Over-fetching and Under-fetching

To understand why GraphQL is so significant, we first need to appreciate the problems it solves. Imagine a typical REST API scenario: you need to display a user’s profile along with the titles of their last three blog posts.

1. First Call (Under-fetching): You hit the /users/1 endpoint to get the user’s details.
2. Second Call (Under-fetching): The user data is returned, but it doesn’t include their posts. So, you have to make a second call to /users/1/posts.
3. Data Bloat (Over-fetching): The /posts endpoint returns an array of post objects, each with a title, body, full text, creation date, and comments. For your UI, you only needed the titles. All the other data was fetched unnecessarily, wasting bandwidth and slowing down the application.

This cycle of making multiple round trips and filtering out unwanted data is a classic inefficiency in client-server communication. GraphQL was designed to fix this.

The GraphQL Paradigm Shift: The Client is in Control

The core principle of GraphQL is simple: the client asks for exactly the data it needs, in the shape it wants, and the server returns precisely that—nothing more, nothing less.

If REST is like ordering from a set menu with fixed dishes, GraphQL is like going to a buffet and picking exactly what you want on your plate.

A single GraphQL request can replace the multiple REST calls from our example above:

GraphQL Query:

GraphQL

query {
  user(id: "1") {
    name
    email
    posts(last: 3) {
      title
    }
  }
}

JSON Response:

JSON

{
  "data": {
    "user": {
      "name": "Radek Zítek",
      "email": "radek@example.com",
      "posts": [
        { "title": "My First Post" },
        { "title": "Learning GraphQL" },
        { "title": "Fastify is Fast" }
      ]
    }
  }
}

In one round trip, the client gets exactly the data it requires. If the UI changes to also show the post creation dates, you simply add createdAt to the query without needing any backend changes. This flexibility is a game-changer for frontend developers.

The Three Pillars of GraphQL

GraphQL operations are categorized into three main types:

1. Queries (Reading Data): As shown above, queries are used for fetching data. They are the “read” operation in GraphQL. Clients can specify nested fields and retrieve related data in a single request.
2. Mutations (Writing Data): If you need to change data (create, update, or delete), you use a mutation. Mutations are explicit and descriptive, making it clear that a write operation is taking place. GraphQLmutation { createPost(userId: "1", title: "New Adventures", content: "...") { id title } } By convention, mutations return the data that was changed, which is useful for updating the client-side state without needing another fetch.
3. Subscriptions (Real-time Data): For real-time functionality, GraphQL offers subscriptions. A client can “subscribe” to a specific event, and the server will push data to the client whenever that event occurs. This is perfect for live notifications, chat applications, and real-time dashboards, maintaining a persistent connection via technologies like WebSockets.

The Heart of the Server: The Schema

How does the server know what queries are valid? Everything is defined in a schema. The GraphQL schema is the single source of truth for your API. It’s written in the Schema Definition Language (SDL) and defines all available data types and the operations (queries, mutations, subscriptions) that can be performed.

A simple schema might look like this:

GraphQL

type Post {
  id: ID!
  title: String!
  content: String
  author: User!
}

type User {
  id: ID!
  name: String!
  email: String!
  posts: [Post!]
}

# The entry points for our API
type Query {
  user(id: ID!): User
  posts: [Post!]
}

type Mutation {
  createPost(userId: ID!, title: String!): Post
}

This strongly typed schema is the contract between the client and the server. It enables powerful developer tooling, including auto-completion, intelligent error messages, and automatic documentation generation.

GraphQL vs. REST: A Quick Comparison

Export to Sheets

Putting It Into Practice

GraphQL is a specification, meaning you need a server library to implement it. As we discussed, for a Fastify backend, the top choices are:

• mercurius: A high-performance, Fastify-native library.
• @apollo/server-fastify: The official integration for the popular Apollo Server.

Other ecosystems have their own mature libraries, such as Apollo Server for Express/Koa, Graphene for Python/Django, and many others.

Is GraphQL Right for You?

GraphQL is not a silver bullet, but it shines brightly in specific scenarios:

• Complex UIs and Mobile Apps: Where network bandwidth is critical and UIs need data from multiple resources.
• Microservice Architectures: It can act as a gateway, providing a unified API layer over multiple downstream services.
• Applications with Evolving Frontend Needs: It decouples the client from the server, allowing frontend developers to innovate without waiting for backend changes.

As the demands of modern applications continue to grow, GraphQL offers a compelling vision for the future of APIs—one that is more efficient, powerful, and a pleasure to work with.