Implémenter le regroupement et la mise en cache
Intégrez DataLoaders à vos résolveurs Spring Boot pour optimiser la récupération des données et réduire les allers-retours vers la base de données.
Implémenter le regroupement et la mise en cache est une leçon GraphQL APIs with Spring Boot gratuite sur CoddyKit. Ceci est la leçon 3 sur 4. Tu peux lire la leçon complète ci-dessous gratuitement — puis la pratiquer en direct dans le navigateur avec un éditeur de code intégré et un tuteur IA 24/7. Elle fait partie du parcours d'apprentissage GraphQL APIs with Spring Boot, et ta progression se synchronise sur le web et l'application CoddyKit. Le cours GraphQL APIs with Spring Boot comprend 4 leçons au total.
Certaines parties de cette leçon n'ont pas encore été traduites et s'affichent en anglais.
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 aCompletionStage, not the direct object. - The DataLoader collects all
load()calls within the current execution frame before dispatching them to theBatchLoader. - 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, theBatchLoader'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
MockUserServiceto simulate data fetching. - We built a
UserBatchLoaderto handle batching multiple ID requests. - We saw how to initialize and register
DataLoaderinstances in aDataLoaderRegistry. - 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.
Questions Fréquemment Posées
La leçon « Implémenter le regroupement et la mise en cache » est-elle gratuite ?
Oui — le texte complet de « Implémenter le regroupement et la mise en cache » est gratuit à lire ici sur le web. Pour la pratiquer de manière interactive (un éditeur de code intégré et un tuteur IA 24/7) et déverrouiller le reste du cours GraphQL APIs with Spring Boot, passe à CoddyKit PRO. Le cours GraphQL APIs with Spring Boot comprend 4 leçons au total.
Qu'est-ce que j'apprendrai dans « Implémenter le regroupement et la mise en cache » ?
Intégrez DataLoaders à vos résolveurs Spring Boot pour optimiser la récupération des données et réduire les allers-retours vers la base de données. Tu pratiques GraphQL APIs with Spring Boot avec du code pratique que tu exécutes directement dans le navigateur, et un tuteur IA 24/7 répond à tes questions au fur et à mesure que tu avances dans la leçon.
Dois-je avoir de l'expérience pour commencer GraphQL APIs with Spring Boot ?
Aucune expérience préalable n'est requise. GraphQL APIs with Spring Boot sur CoddyKit est structuré pour les débutants jusqu'aux apprenants avancés, donc tu peux commencer ici ou depuis le début et avancer à ton rythme. Ceci est la leçon 3 sur 4.
Combien de temps prend la leçon « Implémenter le regroupement et la mise en cache » ?
La plupart des leçons CoddyKit prennent environ 5–10 minutes. Chacune est courte et interactive, tu progresses régulièrement et tu repiques exactement où tu t'es arrêté sur le web et l'app.
Peux-tu écrire et exécuter du code dans cette leçon GraphQL APIs with Spring Boot ?
Oui. Chaque leçon GraphQL APIs with Spring Boot inclut un éditeur de code intégré, tu écris et exécutes du vrai code directement dans ton navigateur et tu reçois des retours IA instantanés — aucune configuration locale requise.
Toutes les leçons de ce cours
- Le problème N+1 expliqué
- Présentation de GraphQL DataLoaders
- Implémenter le regroupement et la mise en cache
- DataLoaders avec le contexte Spring et l’asynchronisme