Membangun Layanan Reaktif Waktu Nyata
Kembangkan layanan reaktif waktu nyata menyeluruh yang memanfaatkan kekuatan Project Reactor.
Membangun Layanan Reaktif Waktu Nyata adalah pelajaran WebSockets & Real-Time Systems with Spring gratis di CoddyKit. Ini adalah pelajaran 3 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar WebSockets & Real-Time Systems with Spring, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus WebSockets & Real-Time Systems with Spring mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
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.
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- Kursus
- 12
- Pelajaran
- 48
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Membangun Layanan Reaktif Waktu Nyata” gratis?
Ya — teks lengkap “Membangun Layanan Reaktif Waktu Nyata” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus WebSockets & Real-Time Systems with Spring, upgrade ke CoddyKit PRO. Kursus WebSockets & Real-Time Systems with Spring mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Membangun Layanan Reaktif Waktu Nyata”?
Kembangkan layanan reaktif waktu nyata menyeluruh yang memanfaatkan kekuatan Project Reactor. Kamu berlatih WebSockets & Real-Time Systems with Spring dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
Apakah aku perlu pengalaman untuk memulai WebSockets & Real-Time Systems with Spring?
Tidak diperlukan pengalaman sebelumnya. WebSockets & Real-Time Systems with Spring di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 3 dari 4.
Berapa lama pelajaran “Membangun Layanan Reaktif Waktu Nyata” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran WebSockets & Real-Time Systems with Spring ini?
Ya. Setiap pelajaran WebSockets & Real-Time Systems with Spring menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.
Semua pelajaran dalam kursus ini
- Pengenalan Pemrograman Reaktif
- Penangan WebSocket WebFlux
- Membangun Layanan Reaktif Waktu Nyata
- Menangani Tekanan Balik dalam Aliran Reaktif