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title seoTitle seoDescription datePublished cuid slug canonical cover tags
Shifting Gears: Why GraphQL is Turbocharging APIs
GraphQL vs REST: Enhancing API Efficiency & Flexibility
Discover the definitive comparison of GraphQL vs REST APIs. Learn how GraphQL elevates API efficiency, query flexibility, and developer experience.
Tue Apr 08 2025 10:17:30 GMT+0000 (Coordinated Universal Time)
cm98cjlgd002u09l81t5ic4fl
shifting-gears-why-graphql-is-turbocharging-apis
apis, graphql, rest-api, graphql-api, graphql-mutation

The API landscape is constantly evolving, and developers like us crave tools that streamline data fetching and boost performance. For years, REST reigned supreme, but a challenger has emerged – GraphQL. So, is GraphQL truly a "better version" of REST? Let's explore this question through code and performance comparisons.

Keploy.io: The Sidekick for Your API Test Drive

As development teams move towards GraphQL and microservice-dominant architectures, testing becomes increasingly important and more complicated. That's where Keploy.io steps in. Keploy is like a co-pilot for your API development process—recording real user traffic and automatically translating it into test cases and mocks. Whether you're working with complicated GraphQL resolvers or stringing together multiple service calls, Keploy keeps your APIs stable and regression-free without the overhead of manual testing. It's the simplest path to shift-left, increase developer productivity, and maintain your releases smooth even when your stack keeps changing at top speed.

REST: Tried and True, but Overloaded?

REST APIs leverage HTTP verbs and resource representations to manage data exchange. Despite its reliability, REST faces challenges when it comes to efficiency:

  • Overfetching burdens clients with more data than needed.

  • Multiple Endpoints slow us down with additional roundtrips.

  • Fixed Data Structures restrict client-side autonomy.

Introducing GraphQL: A Developer's Delight

GraphQL is not just another tool; it's a shift in API design philosophy. It empowers clients to ask for precisely what they need, nothing more, nothing less.
Now let's roll up our sleeves and set up our very own GraphQL server. We'll walk through the process step by step, using Node.js and Express.

Setting Up Your First GraphQL Server

Setting up a GraphQL server is straightforward. Here, we’ll set up a basic GraphQL server using Node.js, Express, and a GraphQL HTTP server.

A. Prerequisites

  • Install Node.js and npm.

  • Basic knowledge of JavaScript and Node.js.

B. Step-by-Step Setup

  1. Initialize a Node.js Project Create a new directory for your project and initialize it with npm:

    mkdir graphql-server
cd graphql-server
npm init -y

  2. Install Dependencies Install Express and GraphQL libraries:

npm install express express-graphql graphql

GraphQL queries allow clients to request exactly the data they need. Unlike REST, where you receive a predefined response, GraphQL queries are about asking for specific fields on objects.

Crafting a Basic Query

Let's create a Basic Query to add a new type Book and a query to retrieve books.

  1. Create GraphQL Schema Define a simple schema in a new file schema.js:

     const { buildSchema } = require('graphql');
 
 const schema = buildSchema(`
   type Query {
     hello: String
     books: [Book]
   }
   type Book {
     title: String
     author: String
   }
 `);
 
 module.exports = schema;

  2. Set Up Server and Resolvers In your main file (e.g., index.js), set up the server:

    const express = require('express');
const { graphqlHTTP } = require('express-graphql');
const schema = require('./schema');

const root = {
  hello: () => 'Hello, world!',
  books: () => [
    { title: 'The Great Gatsby', author: 'F. Scott Fitzgerald' },
  ],
};
const app = express();
app.use('/graphql', graphqlHTTP({
  schema: schema,
  rootValue: root,
  graphiql: true,
}));

app.listen(4000, () => console.log('Server running on http://localhost:4000/graphql'));

  3. Mutations for Data Manipulation

    In GraphQL, mutations are used to modify server-side data (similar to POST, PUT, and DELETE in REST).

    1. Adding a Mutation to Schema (schema.js):

      const schema = buildSchema(`
  type Query {
     hello: String
    books: [Book]
  }
  type Mutation {
    addBook(title: String, author: String): Book
  }
  type Book {
    title: String
    author: String
  }
`);

    2. Implementing the Mutation Resolver (index.js):

      let books = [ /* existing books array */ ];
const root = {
  // ...previous resolvers
  addBook: ({ title, author }) => {
    const newBook = { title, author };
    books.push(newBook);
    return newBook;
  },
};

      Start Your Server: Run node index.js to start your server and navigate to http://localhost:4000/graphql to access the GraphiQL interface.

  4. Running Mutations and Queries

    Before querying data, let's add some to our store:

    mutation {
  addBook(title: "New Book", author: "John Doe") {
    title
    author
  }
}

    Query to Retrieve Books:

    query {
  books {
    title
    author
  }
}

GraphQL vs REST: A Technical Comparison Using a Book Application

Performance Aspects

  1. Data Retrieval Efficiency

    • GraphQL: Enables fetching of multiple, related book data in a single request.

      Example: Fetching Book Details and Author Info

      query {
  book(id: "123") {
    title
    genre
    author {
      name
      bio
    }
  }
}

      This query retrieves the title and genre of a book and its author's details in one go.

    • REST: Requires separate requests for the book and its author, increasing network overhead.
      Example: Fetching Book Details and Author Info

      GET /books/123
GET /authors/456

      Here, one request fetches the book, and another fetches the author, leading to multiple network calls.

  2. Query Flexibility

    • GraphQL: Clients can specify exactly which book fields to retrieve, reducing over-fetching.

      GraphQL Query Example: Specific Fields

      query {
  book(id: "123") {
    title  // Fetches only the title of the book
  }
}

      This query efficiently fetches just the title of a specific book.

    • REST: Returns a fixed data structure, which might include unnecessary information.

      REST Query Example: Book Details

      GET /books/123

      The REST endpoint returns the complete book object, including details the client might not need.

Developer Experience

  1. Ease of Querying

    • GraphQL: Its intuitive query language is especially beneficial for developers working with complex book databases.

      GraphQL Querying Example: Nested Data

      query {
  book(id: "123") {
    title
    author {
      name
      books {
        title  // Titles of other books by the same author
      }
    }
  }
}

      This nested query exemplifies GraphQL's ability to retrieve related data in a readable and efficient manner.

  2. Documentation and Discovery

    • GraphQL: The schema of the book application serves as a live, interactive documentation.

      GraphQL Schema Example: Book Type

      type Book {
  id: ID!
  title: String!
  author: Author
}

      This schema snippet provides clarity on the book-related operations available.

Scalability and Maintenance

  1. Endpoint Management

    • GraphQL: A single endpoint simplifies the management of book-related data.

      GraphQL Endpoint Example:

      POST /graphql

      All queries and mutations related to books and authors are handled through this endpoint.

    • REST: Requires multiple endpoints for various book-related operations.

      REST Endpoint Examples:

      GET /books
POST /books
GET /books/123
GET /authors/456

  2. Versioning

    • GraphQL: Adding new fields to the book schema does not impact existing queries.

      GraphQL Versioning Example: Adding a Field

      type Book {
  id: ID!
  title: String!
  pageCount: Int  // Newly added field
}

    • REST: Often necessitates versioned endpoints for similar changes.

      REST Versioning Example:

      GET /v1/books
GET /v2/books  // New version with additional fields

Now We'll use Postman to analyze the performance of both GraphQL and REST by measuring response times and data transferred.
Graphql | REST

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1706470629238/5932e0fd-499b-4582-867b-b9abf53e41b8.png align="center")

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1706470758049/839c34fb-c2ac-4bc6-81a4-05a01134e49d.png align="center")

  • Latency: The response times for GraphQL mutations and queries are competitive, with the mutation being notably fast.

  • Data Size: GraphQL appears to have a slight edge in data efficiency, likely due to its ability to fetch precisely the required information.

  • Overall Performance: While GraphQL shows potential for performance improvements, the real-world impact should be assessed in the context of full-scale application use, including factors like error handling, data complexity, and concurrent requests.

    Addressing GraphQL's Cons

    Since no technology has only pros Graphql is no less, here's a brief overview of some potential downsides to using GraphQL:

  • Complexity: Writing and maintaining complex and deeply nested queries can be challenging.

  • Caching: Unlike REST, which has straightforward HTTP caching, GraphQL requires more complex caching mechanisms as it operates over a single endpoint. Effective caching with GraphQL can be done with libraries like Apollo Client.

  • Rate Limiting: Traditional rate limiting is difficult with GraphQL because it typically uses a single endpoint for all queries.

  • File Upload: GraphQL does not natively support file uploads, requiring custom implementations such as encoding files as Base64 strings or using a separate REST endpoint just for file uploads.

Conclusion

In conclusion, GraphQL and REST serve different architectural paradigms in API design. GraphQL offers a significant leap in client-centric operations, allowing for compound queries with reduced bandwidth and fine-grained data retrieval. Our hands-on setup demonstrated GraphQL's adept handling of mutations and queries within a unified interface, while the comparative analysis underscored its efficiency in reducing both latency and payload size compared to REST.

Despite these advantages, developers must navigate GraphQL's complexity in query construction and the subtleties of its performance management. The decision between GraphQL and REST hinges on specific use cases, performance considerations, and the architectural needs of the application in development.

Here's the link to the codebase to try yourself : https://github.com/Hermione2408/graphql-sample-app

FAQs

  1. What makes GraphQL better than REST for modern applications?

    GraphQL allows clients to request exactly the data they need, reducing over-fetching. It uses a single endpoint instead of multiple routes like REST. This improves performance and simplifies client-server communication. It's especially powerful for complex frontend applications.

  2. How does Keploy.io help in testing GraphQL APIs?

    Keploy captures real GraphQL traffic and auto-generates test cases from it. It also mocks external dependencies like DBs and APIs. This enables accurate, replayable testing without manual setup. You get faster feedback loops and better confidence in changes.

  3. Do I need to write tests manually when using Keploy?

    No, Keploy automatically creates tests based on actual API requests and responses. It listens to traffic during development or staging environments. You can then replay those tests in CI/CD to catch regressions. It’s a hands-off, zero-effort way to test APIs.

  4. Can Keploy work with both REST and GraphQL?

    Yes, Keploy is designed to work seamlessly with both REST and GraphQL APIs. It captures HTTP requests regardless of the structure. For GraphQL, it handles queries and mutations effectively. This makes it a versatile choice for any API-first project.