Series 3: Advanced Techniques in Reactive Programming

Please see these articles before you start this

• Unlocking the Power of Reactive Programming in Java https://lnkd.in/dM4AGe3c
• Series Part 1: Unveiling the Magic of Reactive Programming with Java and Spring Boot https://www.dhirubhai.net/pulse/series-part-1-unveiling-magic-reactive-programming-java-tripathi-7vcqf
• Series 2: Intermediate Concepts in Reactive Programming https://www.dhirubhai.net/pulse/series-2-intermediate-concepts-reactive-programming-puneet-tripathi-fse3f

Welcome to the advanced stage of our reactive programming series. Here, we'll tackle complex topics like testing reactive code, optimizing performance, and integrating reactive patterns into microservices architectures.

Testing Reactive Streams

Testing asynchronous code can be challenging, but Project Reactor provides the StepVerifier tool.

Example: Testing a Mono

@Test
public void testGetUser() {
    Mono<User> userMono = userService.getUserById("123");

    StepVerifier.create(userMono)
        .expectNextMatches(user -> user.getId().equals("123"))
        .verifyComplete();
}
        

Explanation:

• StepVerifier.create(): Initializes the test.
• expectNextMatches(): Asserts the emitted item meets certain conditions.
• verifyComplete(): Verifies the sequence completes successfully.

Performance Optimization with Schedulers

Assign specific tasks to appropriate thread pools to optimize performance.

Example: Using Bounded Elastic Scheduler

public Mono<Data> fetchData() {
    return Mono.fromCallable(() -> blockingOperation())
        .subscribeOn(Schedulers.boundedElastic());
}
        

Explanation:

• Schedulers.boundedElastic(): Suitable for blocking I/O operations.
• fromCallable: Wraps a blocking call in a reactive Mono.

Integrating with Microservices

Use non-blocking communication between services with WebClient.

Example: Non-Blocking Service Call

public Mono<Response> callExternalService() {
    return webClient.get()
        .uri("/external/api")
        .retrieve()
        .bodyToMono(Response.class);
}
        

Explanation:

• webClient: Asynchronous, non-blocking HTTP client.
• retrieve(): Performs the HTTP request.
• bodyToMono(): Converts the response body into a Mono.

Circuit Breakers and Resilience

Implement fault tolerance with Resilience4j.

Example: Using Circuit Breaker

public Mono<String> fetchDataWithCircuitBreaker() {
    return reactiveCircuitBreaker.run(
        externalService.fetchData(),
        throwable -> Mono.just("Fallback data")
    );
}
        

Explanation:

• reactiveCircuitBreaker.run(): Wraps the reactive call with circuit breaker logic.
• Fallback: Provides a default value in case of failure.

Handling Large Data Sets

Efficiently process big data without overwhelming system resources.

Example: Streaming Data from a Database

public Flux<Data> streamData() {
    return reactiveMongoTemplate.findAll(Data.class)
        .limitRate(1000);
}
        

Explanation:

• limitRate(1000): Processes 1,000 items at a time.
• Ideal for handling large volumes of data.

Real-Time Data with Server-Sent Events (SSE)

Deliver live updates to clients.

Example: Streaming Server-Sent Events

@GetMapping(value = "/events", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
public Flux<ServerEvent> streamEvents() {
    return eventService.getEvents();
}        

Explanation:

• TEXT_EVENT_STREAM_VALUE: MIME type for SSE.
• Clients receive updates as they occur.

Curious about how to ensure your reactive applications are robust and high-performing in production environments? In next final article, we'll discuss how Spring handles reactive programming, it's best practices, design patterns, and future directions to solidify your expertise.