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WebSockets & Real-Time Systems with Spring · Lesson

Building Reactive Real-Time Services

Develop end-to-end reactive real-time services that leverage the power of Project Reactor.

Building Reactive Real-Time Services is a free WebSockets & Real-Time Systems with Spring 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 WebSockets & Real-Time Systems with Spring learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

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.

Frequently asked questions

Is the “Building Reactive Real-Time Services” lesson free?

Yes — the full text of “Building Reactive Real-Time Services” is free to read here on the web, and the WebSockets & Real-Time Systems with Spring 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 WebSockets & Real-Time Systems with Spring course, upgrade to CoddyKit PRO.

What will I learn in “Building Reactive Real-Time Services”?

Develop end-to-end reactive real-time services that leverage the power of Project Reactor. You practise WebSockets & Real-Time Systems with Spring 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 WebSockets & Real-Time Systems with Spring?

No prior experience is required. WebSockets & Real-Time Systems with Spring 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 “Building Reactive Real-Time Services” 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 WebSockets & Real-Time Systems with Spring lesson?

Yes. Every WebSockets & Real-Time Systems with Spring 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

  1. Introduction to Reactive Programming
  2. WebFlux WebSocket Handlers
  3. Building Reactive Real-Time Services
  4. Handling Backpressure in Reactive Streams
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