In this lesson, we’re going to address a common performance issue in GraphQL known as the N+1 problem and how to solve it using Data Loaders.

The N+1 problem occurs when your GraphQL server makes an excessive number of database or API calls to satisfy nested queries. For example, if you fetch a list of books along with their authors, your server might make one query to get the books (1 query) and then one additional query per book to get the author (N queries), leading to a total of N+1 queries. This can significantly degrade the performance of your application.

Data Loaders help to batch and cache the requests, effectively reducing the number of queries made and improving the performance.

Benefits of Using Data Loaders:

• Batching: Combines multiple requests into a single batch query.
• Caching: Reduces redundant queries by remembering previously fetched results.

In GraphQL, the schema defines the shape of the data and the queries you can perform. To illustrate how Data Loaders can solve the N+1 problem, we’ll create a simple GraphQL schema with authors and books.

Here’s how you can define the schema:

TypeScript
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1import { ApolloServer, gql } from 'apollo-server';
2
3// Sample data
4const authors = [
5  { id: '1', name: 'J.R.R. Tolkien' },
6  { id: '2', name: 'J.K. Rowling' }
7];
8const books = [
9  { id: '1', title: 'The Hobbit', author: '1' },
10  { id: '2', title: 'Harry Potter', author: '2' }
11];
12
13// Define schema
14const typeDefs = gql`
15  type Author {
16    id: ID!
17    name: String!
18  }
19
20  type Book {
21    id: ID!
22    title: String!
23    author: Author
24  }
25
26  type Query {
27    books: [Book]
28    author(id: ID!): Author
29  }
30`;

In this schema:

• We define Author and Book types.
• The Book type has a nested Author type.
• The Query type fetches a list of books and a single author by ID.

Data Loaders serve two primary functions: batching and caching requests.

1. Batching: Data Loaders collect multiple requests made in a single event loop tick and combine them into a single query, reducing the total number of database/API calls.
2. Caching: Once a piece of data is fetched, Data Loaders cache the result. If the same data is requested again, the Data Loader returns the cached value instead of making another request.

Before implementing the resolvers, we need to initialize a Data Loader for batching and caching authors:

TypeScript
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1import DataLoader from 'dataloader';
2
3// Initialize dataloader
4const authorLoader = new DataLoader(async (ids: readonly string[]) => {
5  return ids.map(id => authors.find(author => author.id === id) as typeof authors[0]);
6});

Here’s how you can implement resolvers using Data Loaders:

TypeScript
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1// Resolvers
2const resolvers = {
3  Query: {
4    books: () => books,
5    author: async (_: unknown, { id }: { id: string }, { authorLoader }: { authorLoader: DataLoader<string, typeof authors[0]> }) => authorLoader.load(id)
6  },
7  Book: {
8    author: async (book: { author: string }, _: unknown, { authorLoader }: { authorLoader: DataLoader<string, typeof authors[0]> }) => {
9      return authorLoader.load(book.author)
10    }
11  }
12};

In this example:

• Query Resolvers:
  • books returns all books directly.
  • author uses authorLoader.load(id) to fetch an author by ID. The Data Loader batches requests made within the same event loop tick and caches the results.
• Nested Resolver:
  • The author field within the Book type uses authorLoader.load(book.author) to retrieve the author, leveraging Data Loader's batching and caching capabilities.

Now, let’s integrate the initialized Data Loader into our Apollo Server:

TypeScript
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1// Initialize Apollo Server
2const server = new ApolloServer({ 
3  typeDefs, 
4  resolvers,
5  context: () => ({ authorLoader })
6});
7
8// Start the server
9server.listen().then(({ url }) => {
10  console.log(`🚀 Server ready at ${url}`);
11});

Here’s what’s happening:

• The context function adds the Data Loader to the context so it is available to the resolvers.

Finally, let's test our implementation by running some queries in a separate run.ts file:

TypeScript
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1import fetch from 'node-fetch';
2
3const query = `
4  query {
5    books {
6      title
7      author {
8        name
9      }
10    }
11  }
12`;
13
14const url = 'http://localhost:4000/';
15
16fetch(url, {
17  method: 'POST',
18  headers: {
19    'Content-Type': 'application/json',
20  },
21  body: JSON.stringify({
22    query,
23  }),
24})
25  .then(response => response.json())
26  .then(data => console.log(JSON.stringify(data, null, 2)))
27  .catch(error => console.error('Error:', error));
28
29const authorQuery = `
30  query {
31    author(id: "1") {
32      name
33    }
34  }
35`;
36
37fetch(url, {
38  method: 'POST',
39  headers: {
40    'Content-Type': 'application/json',
41  },
42  body: JSON.stringify({
43    query: authorQuery,
44  }),
45})
46  .then(response => response.json())
47  .then(data => console.log(JSON.stringify(data, null, 2)))
48  .catch(error => console.error('Error:', error));

Output expected:

JSON
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1{
2  "data": {
3    "books": [
4      {
5        "title": "The Hobbit",
6        "author": {
7          "name": "J.R.R. Tolkien"
8        }
9      },
10      {
11        "title": "Harry Potter",
12        "author": {
13          "name": "J.K. Rowling"
14        }
15      }
16    ]
17  }
18}

JSON
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1{
2  "data": {
3    "author": {
4      "name": "J.R.R. Tolkien"
5    }
6  }
7}

Everything should work correctly, fetching the required data while only making the necessary requests.

In this lesson, you learned about the N+1 problem in GraphQL and how to resolve it efficiently using Data Loaders. By defining the schema, implementing resolvers, integrating Data Loaders, and testing, you now have the skills to optimize data fetching in GraphQL applications.

You’ve reached the end of this course! Congratulations on making it this far. Now, dive into the practice exercises to reinforce your new skills and prepare for creating more powerful and efficient GraphQL APIs.