Construcción de servicios reactivos en tiempo real
Desarrolle servicios reactivos de extremo a extremo en tiempo real que aprovechen la potencia de Project Reactor.
Construcción de servicios reactivos en tiempo real es una lección gratuita de WebSockets & Real-Time Systems with Spring en CoddyKit. Esta es la lección 3 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de WebSockets & Real-Time Systems with Spring, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de WebSockets & Real-Time Systems with Spring incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
Reactive Real-Time Services
Welcome! In this lesson, we'll build end-to-end reactive real-time services using Spring WebFlux and Project Reactor.
Reactive services are excellent for handling many concurrent connections efficiently. They offer better scalability and responsiveness compared to traditional blocking approaches.
Project Reactor: Flux & Mono
At the heart of reactive programming in Spring is Project Reactor. It provides two key types for handling data streams:
Flux: Represents a stream of 0 to N items. Think of it as a publisher that can emit multiple values over time.Mono: Represents a stream of 0 to 1 item. Useful for operations that return a single result or no result (likevoid).
These types allow us to compose asynchronous operations in a clear and non-blocking way.
WebFlux WebSocket Handlers
Spring WebFlux uses the WebSocketHandler interface to manage WebSocket connections. Its main method, handle(), takes a WebSocketSession and returns a Mono.
This Mono signifies that the handling process is complete once the reactive stream it represents finishes. We can use Flux inside to send continuous messages.
Designing a Reactive Data Source
To build a real-time service, we need a source of data. Let's create a simple Flux that emits a message periodically. This simulates a real-time data feed, like a stock ticker or a sensor reading.
We'll use Flux.interval() to generate events and map() to transform them into useful messages.
Implementing a Ticker Service
Here's a basic WebSocketHandler that sends a 'tick' message every second. It uses the Flux.interval() we discussed.
The session.send() method takes a Flux to push data to the client.
import org.springframework.web.reactive.socket.WebSocketHandler;
import org.springframework.web.reactive.socket.WebSocketMessage;
import org.springframework.web.reactive.socket.WebSocketSession;
import reactor.core.publisher.Flux;
import reactor.core.publisher.Mono;
import java.time.Duration;
public class TimeTickerHandler implements WebSocketHandler {
@Override
public Mono<Void> handle(WebSocketSession session) {
// Send messages to the client
Flux<WebSocketMessage> output = Flux.interval(Duration.ofSeconds(1))
.map(i -> session.textMessage("Tick #" + i));
// Receive messages from the client (and ignore them for now)
// We use .then() to ensure the Mono<Void> completes only when the session closes.
Mono<Void> input = session.receive().then();
return session.send(output).and(input);
}
}
Full Runnable Ticker Service
To make our TimeTickerHandler runnable, we need a Spring Boot application. This example sets up the WebFlux server and registers our handler.
Access this via ws://localhost:8080/ticker in a WebSocket client (like Postman or a browser's DevTools console) to see it in action.
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.web.reactive.handler.SimpleUrlHandlerMapping;
import org.springframework.web.reactive.socket.WebSocketHandler;
import org.springframework.web.reactive.socket.WebSocketMessage;
import org.springframework.web.reactive.socket.WebSocketSession;
import org.springframework.web.reactive.socket.server.support.WebSocketHandlerAdapter;
import reactor.core.publisher.Flux;
import reactor.core.publisher.Mono;
import java.time.Duration;
import java.util.HashMap;
import java.util.Map;
@SpringBootApplication
public class ReactiveTickerApplication {
public static void main(String[] args) {
SpringApplication.run(ReactiveTickerApplication.class, args);
}
@Bean
public SimpleUrlHandlerMapping webSocketHandlerMapping(WebSocketHandler webSocketHandler) {
Map<String, WebSocketHandler> map = new HashMap<>();
map.put("/ticker", webSocketHandler);
return new SimpleUrlHandlerMapping(map, 1);
}
@Bean
public WebSocketHandler webSocketHandler() {
return new WebSocketHandler() {
@Override
public Mono<Void> handle(WebSocketSession session) {
// Send a 'tick' message every second
Flux<WebSocketMessage> output = Flux.interval(Duration.ofSeconds(1))
.map(i -> session.textMessage("Tick #" + i + " at " + System.currentTimeMillis()));
// Handle incoming messages (e.g., echo them back, or process commands)
// For this example, we'll just log and then complete the input stream
Mono<Void> input = session.receive()
.doOnNext(msg -> System.out.println("Received: " + msg.getPayloadAsText()))
.then(); // ensures the Mono completes after processing all incoming
return session.send(output).and(input);
}
};
}
@Bean
public WebSocketHandlerAdapter handlerAdapter() {
return new WebSocketHandlerAdapter();
}
}
Handling Client Input
Our previous ticker only sent data. To make it truly interactive, we can also process messages coming from the client.
The session.receive() method returns a Flux that represents incoming messages. You can subscribe to this Flux to react to client input, for example, by filtering, transforming, or using the data to control the output stream.
Error Handling in Reactive Streams
Errors can occur in any part of a reactive pipeline. Project Reactor provides operators to handle these gracefully, preventing your application from crashing:
onErrorResume(): Recovers from an error by switching to an alternative publisher.doOnError(): Performs a side-effect (like logging) when an error occurs, then re-throws it or completes.retry(): Retries the sequence if an error occurs.
Using these helps build robust real-time services that can recover from transient issues.
Backpressure Management
Backpressure is crucial for reactive systems. It's a mechanism where a consumer can signal to a producer that it's receiving data too quickly and needs the producer to slow down.
Project Reactor handles backpressure automatically. When a client can't keep up, the WebSocket connection might buffer messages or eventually close, but the server-side Flux won't overwhelm itself or the network.
Reactive Service Concepts
Which of the following are key characteristics of building reactive real-time services with Spring WebFlux and Project Reactor?
Recap: Reactive Real-Time
We've explored how to build reactive real-time services using Spring WebFlux and Project Reactor.
- We saw how
Fluxcan generate continuous data streams. - We implemented a
WebSocketHandlerto push these streams to clients. - We configured a basic Spring Boot application to host our reactive WebSocket endpoint.
- We touched upon error handling and backpressure, vital for robust systems.
These principles enable highly scalable and responsive real-time applications.
Preguntas frecuentes
¿La lección «Construcción de servicios reactivos en tiempo real» es gratis?
Sí — el texto completo de «Construcción de servicios reactivos en tiempo real» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de WebSockets & Real-Time Systems with Spring, actualiza a CoddyKit PRO. El curso de WebSockets & Real-Time Systems with Spring incluye 4 lecciones en total.
¿Qué aprenderé en «Construcción de servicios reactivos en tiempo real»?
Desarrolle servicios reactivos de extremo a extremo en tiempo real que aprovechen la potencia de Project Reactor. Practicas WebSockets & Real-Time Systems with Spring con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.
¿Necesito experiencia previa para empezar WebSockets & Real-Time Systems with Spring?
No se requiere experiencia previa. WebSockets & Real-Time Systems with Spring en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 3 de 4.
¿Cuánto tiempo toma la lección «Construcción de servicios reactivos en tiempo real»?
La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.
¿Puedo escribir y ejecutar código en esta lección de WebSockets & Real-Time Systems with Spring?
Sí. Cada lección de WebSockets & Real-Time Systems with Spring incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.
Todas las lecciones de este curso
- Introducción a la programación reactiva
- Handlers WebSocket de WebFlux
- Construcción de servicios reactivos en tiempo real
- Gestión del backpressure en streams reactivos