GraphQL APIs with Spring Boot · Lesson

Implementing Batching and Caching

Integrate DataLoaders into your Spring Boot resolvers to optimize data retrieval and reduce database roundtrips.

Implementing Batching and Caching is a free GraphQL APIs with Spring Boot lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the GraphQL APIs with Spring Boot learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

DataLoaders: Time to Implement!

Welcome back! In previous lessons, we learned about the N+1 problem and how DataLoaders provide an elegant solution through batching and caching.

Today, we'll get hands-on and integrate DataLoaders into a Spring Boot GraphQL application. We'll see how to define a BatchLoader, register it, and use it in our GraphQL resolvers.

Simulating a Data Service

First, let's set up a simple mock service that simulates fetching users from a database. This service will be called by our DataLoaders.

Notice the System.out.println, which will help us observe when the actual 'database' call happens, demonstrating batching later.

import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

// A simple User data class
class User {
  String id;
  String name;
  User(String id, String name) { 
    this.id = id; this.name = name; 
  }
  public String getId() { return id; }
  public String getName() { return name; }
}

// Mock service to fetch users (simulates DB call)
class MockUserService {
  private final Map<String, User> users = Map.of(
    "1", new User("1", "Alice"),
    "2", new User("2", "Bob"),
    "3", new User("3", "Charlie"),
    "4", new User("4", "David")
  );

  public List<User> findAllByIds(List<String> ids) {
    System.out.println("DB Call: Fetching users for IDs: " + ids);
    return ids.stream()
              .map(users::get)
              .filter(user -> user != null)
              .collect(Collectors.toList());
  }
}

public class Main {
  public static void main(String[] args) {
    MockUserService service = new MockUserService();
    List<User> foundUsers = service.findAllByIds(List.of("1", "3"));
    System.out.println("Found: " + foundUsers.size() + " users.");
  }
}

The BatchLoader Interface

The core of DataLoader's batching mechanism is the BatchLoader interface. It defines a single method: load(List<K> keys).

It takes a list of keys (e.g., user IDs).
It returns a CompletionStage (a future-like object) of a list of values (e.g., users).
Crucially, this method is called once for all keys requested in a short window, allowing you to fetch them in a single optimized operation (like a single database query).

Implementing Your BatchLoader

Now, let's create our UserBatchLoader. It will implement the BatchLoader<String, User> interface, meaning it takes a String (user ID) as a key and returns a User object.

It uses our MockUserService to perform the actual batch fetch.

import org.dataloader.BatchLoader;
import java.util.List;
import java.util.concurrent.CompletableFuture;
import java.util.concurrent.CompletionStage;

// (Assume User and MockUserService are defined elsewhere)
// For runnable example, they would be in the same file.

public class UserBatchLoader implements BatchLoader<String, User> {
  private final MockUserService userService;

  public UserBatchLoader(MockUserService userService) {
    this.userService = userService;
  }

  @Override
  public CompletionStage<List<User>> load(List<String> userIds) {
    // This method is called once with all requested IDs.
    // We delegate to our mock service to fetch them in one go.
    return CompletableFuture.supplyAsync(
      () -> userService.findAllByIds(userIds)
    );
  }
}

public class Main {
  public static void main(String[] args) {
    MockUserService userService = new MockUserService();
    UserBatchLoader batchLoader = new UserBatchLoader(userService);
    System.out.println("UserBatchLoader created!");
  }
}

The DataLoaderRegistry

In a Spring Boot GraphQL application, DataLoader instances are managed within a DataLoaderRegistry. This registry acts as a container for all DataLoaders available during a GraphQL request execution.

Each DataLoader is registered with a unique name (e.g., "userDataLoader").
Resolvers retrieve the necessary DataLoader from this registry using its name.
Spring for GraphQL typically handles the lifecycle and scope of this registry per request.

Setting up DataLoaders

Here's how you create a DataLoader instance from your BatchLoader and register it in a DataLoaderRegistry. This setup is crucial for making DataLoaders accessible to your resolvers.

import org.dataloader.DataLoader;
import org.dataloader.DataLoaderRegistry;

// (Assume User, MockUserService, UserBatchLoader are defined)

public class Main {
  public static void main(String[] args) {
    MockUserService userService = new MockUserService();
    UserBatchLoader userBatchLoader = new UserBatchLoader(userService);

    // Create a DataLoader instance
    DataLoader<String, User> userDataLoader =
      DataLoader.newDataLoader(userBatchLoader);

    // Register it in a DataLoaderRegistry
    DataLoaderRegistry registry = new DataLoaderRegistry();
    registry.register("userDataLoader", userDataLoader);

    System.out.println("DataLoader registered as 'userDataLoader'!");
    // In a real app, this registry would be part of the GraphQLContext.
    // Resolvers would then retrieve DataLoaders from it.
  }
}

Using DataLoader in Resolvers

Once your DataLoader is registered, you can use it within your GraphQL resolvers. Instead of directly calling your service, you'll call dataLoader.load(id).

The load() method returns a CompletionStage, not the direct object.
The DataLoader collects all load() calls within the current execution frame before dispatching them to the BatchLoader.
Spring for GraphQL automatically dispatches the DataLoaders at the end of a data fetching cycle.

Resolver Integration in Action

This example simulates a GraphQL query where multiple user IDs are requested. Watch the "DB Call" message in the output to see how DataLoaders batch these requests into a single operation, even if they appear separate in the resolver logic.

import org.dataloader.DataLoader;
import org.dataloader.DataLoaderRegistry;
import java.util.List;
import java.util.Map;
import java.util.concurrent.CompletableFuture;
import java.util.concurrent.CompletionStage;
import java.util.stream.Collectors;

// User, MockUserService, UserBatchLoader definitions (from previous scenes)
class User {
  String id; String name;
  User(String id, String name) { this.id = id; this.name = name; }
  public String getId() { return id; }
  public String getName() { return name; }
}

class MockUserService {
  private final Map<String, User> users = Map.of(
    "1", new User("1", "Alice"), "2", new User("2", "Bob"),
    "3", new User("3", "Charlie"), "4", new User("4", "David")
  );
  public List<User> findAllByIds(List<String> ids) {
    System.out.println("DB Call: Fetching users for IDs: " + ids);
    return ids.stream().map(users::get)
              .filter(user -> user != null).collect(Collectors.toList());
  }
}

class UserBatchLoader implements BatchLoader<String, User> {
  private final MockUserService userService;
  public UserBatchLoader(MockUserService userService) { this.userService = userService; }
  @Override
  public CompletionStage<List<User>> load(List<String> userIds) {
    return CompletableFuture.supplyAsync(() -> userService.findAllByIds(userIds));
  }
}

// Simulate a GraphQL Query Resolver method
public class UserResolver {
  private final DataLoaderRegistry dataLoaderRegistry;

  public UserResolver(DataLoaderRegistry registry) {
    this.dataLoaderRegistry = registry;
  }

  public CompletionStage<User> getUserById(String id) {
    DataLoader<String, User> userDataLoader =
      dataLoaderRegistry.getDataLoader("userDataLoader");
    return userDataLoader.load(id);
  }

  public static void main(String[] args) {
    MockUserService userService = new MockUserService();
    UserBatchLoader userBatchLoader = new UserBatchLoader(userService);
    DataLoader<String, User> userDataLoader =
      DataLoader.newDataLoader(userBatchLoader);

    DataLoaderRegistry registry = new DataLoaderRegistry();
    registry.register("userDataLoader", userDataLoader);

    UserResolver resolver = new UserResolver(registry);

    System.out.println("Requesting User 1 and User 2 concurrently...");

    // Simulate two concurrent GraphQL field fetches
    CompletableFuture<User> user1Future =
      (CompletableFuture<User>) resolver.getUserById("1");
    CompletableFuture<User> user2Future =
      (CompletableFuture<User>) resolver.getUserById("2");

    CompletableFuture.allOf(user1Future, user2Future)
      .thenRun(() -> {
        try {
          System.out.println("Fetched User 1: " + user1Future.join().getName());
          System.out.println("Fetched User 2: " + user2Future.join().getName());
        } catch (Exception e) {
          e.printStackTrace();
        }
        // For this manual demo, we dispatch the DataLoader.
        // In Spring GraphQL, this is handled automatically.
        userDataLoader.dispatch();
      }).join();
  }
}

DataLoader's Built-in Caching

Beyond batching, DataLoaders also provide request-scoped caching out-of-the-box. This means:

If dataLoader.load("1") is called multiple times within the same GraphQL request, the BatchLoader's fetch function for ID "1" will only be invoked once.
Subsequent calls for the same ID within that request will return the already fetched (cached) result.
This prevents redundant database calls for the same data within a single GraphQL operation, further boosting performance.

Check Your Understanding

You've learned how to implement DataLoaders for batching and caching. Let's test your knowledge!

Recap: Batching & Caching

Great job! You've successfully learned how to implement DataLoaders in Spring Boot GraphQL.

We created a MockUserService to simulate data fetching.
We built a UserBatchLoader to handle batching multiple ID requests.
We saw how to initialize and register DataLoader instances in a DataLoaderRegistry.
Finally, we integrated DataLoaders into a resolver, observing how requests are batched and how caching prevents redundant calls.

By using DataLoaders, you significantly optimize your GraphQL API's performance, especially when dealing with complex data graphs and nested objects.

Frequently asked questions

Is the “Implementing Batching and Caching” lesson free?

Yes — the full text of “Implementing Batching and Caching” is free to read here on the web, and the GraphQL APIs with Spring Boot course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the GraphQL APIs with Spring Boot course, upgrade to CoddyKit PRO.

What will I learn in “Implementing Batching and Caching”?

Integrate DataLoaders into your Spring Boot resolvers to optimize data retrieval and reduce database roundtrips. You practise GraphQL APIs with Spring Boot with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start GraphQL APIs with Spring Boot?

No prior experience is required. GraphQL APIs with Spring Boot on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Implementing Batching and Caching” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this GraphQL APIs with Spring Boot lesson?

Yes. Every GraphQL APIs with Spring Boot lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

The N+1 Problem Explained
Introducing GraphQL DataLoaders
Implementing Batching and Caching
DataLoaders with Spring Context and Async

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