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WebSockets & Real-Time Systems with Spring · 강의

반응형 실시간 서비스 구축

Project Reactor의 강력한 기능을 활용하는 엔드투엔드 반응형 실시간 서비스를 개발합니다.

반응형 실시간 서비스 구축은(는) CoddyKit의 무료 WebSockets & Real-Time Systems with Spring 강의입니다. 이것은 4개 중 3번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 WebSockets & Real-Time Systems with Spring 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. WebSockets & Real-Time Systems with Spring 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

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 (like void).

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 Flux can generate continuous data streams.
  • We implemented a WebSocketHandler to 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.

자주 묻는 질문

“반응형 실시간 서비스 구축” 강의는 무료인가요?

네 — “반응형 실시간 서비스 구축” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 WebSockets & Real-Time Systems with Spring 강의 전체를 잠금 해제할 수 있습니다. WebSockets & Real-Time Systems with Spring 강의에는 총 4개의 강의가 포함되어 있습니다.

“반응형 실시간 서비스 구축”에서 뭘 배우나요?

Project Reactor의 강력한 기능을 활용하는 엔드투엔드 반응형 실시간 서비스를 개발합니다. 브라우저에서 직접 실행하는 실습 코드로 WebSockets & Real-Time Systems with Spring을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

WebSockets & Real-Time Systems with Spring을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 WebSockets & Real-Time Systems with Spring은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 3번째 강의입니다.

“반응형 실시간 서비스 구축” 강의는 얼마나 걸리나요?

대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.

이 WebSockets & Real-Time Systems with Spring 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 WebSockets & Real-Time Systems with Spring 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.

이 강의의 모든 강의

  1. 반응형 프로그래밍 입문
  2. WebFlux WebSocket 처리기
  3. 반응형 실시간 서비스 구축
  4. 리액티브 스트림의 백프레셔 처리
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