Modern applications often need to serve thousands or even millions of concurrent users — especially in microservices, messaging systems, or real-time dashboards. Traditional thread-per-request models can’t scale efficiently under such load.

This is where Reactive Streams and non-blocking I/O come in. With Spring Boot and Spring WebFlux, Java developers can build asynchronous, event-driven systems that handle large volumes of concurrent traffic without exhausting resources.

In this guide, you’ll explore how to build reactive applications with Spring Boot using Project Reactor, understand the non-blocking paradigm, and implement performant reactive APIs with WebFlux.

What are Reactive Streams?

Reactive Streams is a specification that defines a non-blocking, asynchronous communication model between components that process streams of data with backpressure.

Key interfaces:

  • Publisher: Emits items
  • Subscriber: Consumes items
  • Subscription: Links them and manages flow
  • Processor: Both a subscriber and publisher

Spring’s Project Reactor provides the core reactive types:

  • Mono<T>: 0 or 1 result
  • Flux<T>: 0 to N results

Why Use Reactive Programming?

Benefits of the reactive approach:

  • Non-blocking I/O — better scalability with fewer threads
  • Backpressure support — handle fast producers and slow consumers gracefully
  • Resilience — easier error propagation and retry logic
  • Responsiveness — ideal for streaming and real-time use cases

Common use cases:

  • Real-time messaging systems
  • Chat apps and dashboards
  • Streaming APIs
  • Microservices with high throughput

Setting Up Spring Boot with WebFlux

Add the reactive dependencies in your pom.xml:

<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-webflux</artifactId>
</dependency>

This enables Spring WebFlux — the reactive alternative to Spring MVC.

Creating a Reactive REST Controller

Define a controller using Mono and Flux:

@RestController
@RequestMapping("/api/users")
public class UserController {

    private final UserService userService;

    public UserController(UserService userService) {
        this.userService = userService;
    }

    @GetMapping
    public Flux<User> getAllUsers() {
        return userService.getAllUsers();
    }

    @GetMapping("/{id}")
    public Mono<ResponseEntity<User>> getUserById(@PathVariable String id) {
        return userService.getUserById(id)
            .map(ResponseEntity::ok)
            .defaultIfEmpty(ResponseEntity.notFound().build());
    }
}

Writing a Reactive Service

Use Flux and Mono to build non-blocking pipelines:

@Service
public class UserService {

    private final UserRepository repository;

    public Flux<User> getAllUsers() {
        return repository.findAll();
    }

    public Mono<User> getUserById(String id) {
        return repository.findById(id);
    }
}

Repositories can be reactive too, using R2DBC or Mongo Reactive.

Reactive MongoDB Example

Add dependency:

<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-mongodb-reactive</artifactId>
</dependency>

Define a reactive repository:

public interface UserRepository extends ReactiveMongoRepository<User, String> {}

Now, your entire stack — controller, service, and DB — is non-blocking.

Backpressure and Flow Control

Backpressure ensures systems don’t crash under high load.

Reactor operators like limitRate, onBackpressureBuffer, and timeout help you manage demand:

Flux.range(1, 1000)
.onBackpressureBuffer(100)
.subscribe(System.out::println);

Always test how your reactive components behave under load and apply proper buffering, rate-limiting, and fallbacks.

Error Handling in Reactive Streams

Reactive error handling uses operators like onErrorResume, onErrorReturn, and retry:

Mono.just("data")
.map(this::process)
.onErrorResume(ex -> Mono.just("fallback"));

You can also centralize error handling using @ControllerAdvice and WebFlux exception resolvers.

Streaming and SSE with Flux

Reactive streams shine in server-sent events (SSE) and live updates:

@GetMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
public Flux<String> streamEvents() {
return Flux.interval(Duration.ofSeconds(1))
.map(seq -> "Event #" + seq);
}

This enables dashboards, logs, or metrics to be pushed to clients in real-time.

Testing Reactive Components

Use StepVerifier for unit tests:

StepVerifier.create(service.getUserById("1"))
.expectNextMatches(user -> user.getId().equals("1"))
.verifyComplete();

Also integrate reactive test slices with Spring Boot’s @WebFluxTest.

Best Practices

  • Use reactive types (Mono, Flux) end-to-end
  • Avoid blocking calls (Thread.sleep, JDBC, etc.)
  • Apply backpressure handling where needed
  • Use reactive database drivers (R2DBC, reactive Mongo)
  • Don’t mix blocking and non-blocking code in the same context

Conclusion

Spring Boot with Reactive Streams provides a modern, scalable way to handle high-throughput, event-driven applications. With Project Reactor, Spring WebFlux, and reactive databases, you can build real-time systems that are resilient and efficient.

By embracing the non-blocking model, you reduce thread contention and unlock the full potential of reactive architecture in your Java applications.