In the era of cloud-native transformation, building resilient, scalable, and non-blocking HTTP clients is no longer optional—it’s strategic. Spring Boot’s WebClient, powered by Project Reactor and Spring WebFlux, is the modern alternative to the legacy RestTemplate. This post explores why WebClient is the future, explore retry with WebFlux WebClient how its retry mechanism works, and how to architect fault-tolerant reactive systems.

Why Replace RestTemplate with WebClient?

FeatureRest TemplateWebClient (WebFlux)
Blocking I/OYesNon-blocking but can be blocking via .block()
Reactive Streams SupportNoYes
Backpressure HandlingNoYes
Retry & ResilienceManual / LimitedDeclarativa via Reactor e.g. retry()
Future SupportDepcrated (Spring 6+)Actively Maintained

Spring Framework 6 and Spring Boot 3 have deprecated RestTemplate, signaling a shift toward reactive-first design. WebClient is now the preferred HTTP client for modern applications.

WebClient Architecture Overview

WebClient is built for reactive systems and supports:

  • Non-blocking I/O via Netty or Servlet 3.1+
  • Reactive types (Mono<T>, Flux<T>)
  • Backpressure-aware pipelines
  • Declarative error handling and retry logic
WebClient webClient = WebClient.create("https://api.example.com");

Mono<Response> responseMono = webClient.get()
    .uri("/data")
    .retrieve()
    .bodyToMono(Response.class);
Java

Retry with WebFlux WebClient

There are a few ways in which you can implement retry, listed below are some of those ways,

Basic Retry

webClient.get()
    .uri("/unstable-endpoint")
    .retrieve()
    .bodyToMono(String.class)
    .retry(3);
Java

This retries on any error but lacks control over exception types or timing.

Advanced Retry with retryWhen

webClient.get()
    .uri("/unstable-endpoint")
    .retrieve()
    .bodyToMono(String.class)
    .retryWhen(
        Retry.backoff(3, Duration.ofSeconds(2))
            .filter(throwable -> throwable instanceof WebClientResponseException)
            .onRetryExhaustedThrow((spec, signal) ->
                new RuntimeException("Retries exhausted", signal.failure()))
    );
Java

This approach enables:

  • Exponential backoff
  • Exception filtering
  • Custom fallback logic

Best Practices for Retry Strategy

  • ✅ Use exponential backoff and jitter to avoid cascading failures
  • ✅ Filter retryable exceptions (e.g., 5xx, timeouts)
  • ✅ Log retry attempts for observability
  • ✅ Combine with circuit breakers (Resilience4j, Spring Cloud)

Real-World Use Case: API Aggregation in Financial Services

Flux<Data> aggregatedData = Flux.merge(
    webClient.get().uri("/source1").retrieve().bodyToMono(Data.class),
    webClient.get().uri("/source2").retrieve().bodyToMono(Data.class)
).retryWhen(Retry.backoff(2, Duration.ofSeconds(1)));
Java

This pattern enables:

  • Concurrent non-blocking calls
  • Resilience against flaky endpoints
  • Improved latency and throughput

Conclusion

WebClient is more than a replacement—it’s a strategic enabler for reactive transformation. With built-in support for retries, backpressure, and non-blocking I/O, it aligns perfectly with the demands of modern microservices, serverless platforms, and event-driven architectures.

While you are here, maybe try one of my apps for the iPhone.

Snap! I was there on the App Store

Listed below are the links to the first two parts of this series

How to build a blog engine with React & Spring Boot – Part 1 – My Day To-Do (mydaytodo.com)

How to build a blog engine with React & Spring Boot – Part 2 – My Day To-Do (mydaytodo.com)

Here are some of my other bloposts,

How to unit test react-redux app – My Day To-Do (mydaytodo.com)


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