You need to build a highly scalable real-time data processing application. How would you leverage Spring Boot's reactive features?

Spring Boot's reactive stack, built on WebFlux with Mono and Flux, provides the non-blocking, backpressure-aware foundation needed for a highly scalable real-time data processing application.

Key Points: • Non-blocking I/O lets a small thread pool handle many concurrent connections, unlike the thread-per-request model of traditional Spring MVC. • Flux models unbounded, real-time streams of events, ideal for continuously arriving data. • Backpressure ensures downstream consumers aren't overwhelmed when data arrives faster than it can be processed. • Reactive operators compose transformations declaratively, making complex event-processing pipelines readable and testable. • WebFlux integrates naturally with reactive messaging systems like Kafka or RSocket for end-to-end non-blocking pipelines.

Example: A real-time analytics service ingests a Flux of sensor events, applies windowing and aggregation operators, and streams results to subscribers over Server-Sent Events, all without blocking threads while waiting on I/O.

Code Example:

@GetMapping(value = "/events", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
public Flux<SensorEvent> streamEvents() {
    return sensorEventPublisher.getEventStream()
            .onBackpressureBuffer(1000);
}

Interview Tip: A concise interview answer is:

"I'd build it on WebFlux, using Flux to model the continuous stream of incoming data and Mono for single-value operations. The non-blocking model lets a small thread pool handle high concurrency, and backpressure keeps a fast producer from overwhelming a slower consumer, which is exactly what a real-time, high-throughput system needs."