Reactive Streams in Java: Using Project Reactor

Reactive Streams in Java: Using Project Reactor

Harnessing the Power of Asynchronous Data Streams in Java


In the world of modern application development, the ability to handle asynchronous data streams efficiently is critical. Reactive Streams provide a powerful approach to managing this, and Project Reactor, a library for building non-blocking applications on the JVM, is at the forefront of this paradigm. This article will delve into the concepts of Reactive Streams, explore Project Reactor in depth, and provide practical examples to help you harness the power of asynchronous data streams in Java.

Understanding Reactive Streams

Reactive Streams is a specification for asynchronous stream processing with non-blocking backpressure. Servers that support reactive streams enable this functionality by adhering to principles and mechanisms that manage data flow efficiently. Here’s a detailed breakdown of how servers facilitate the operation of reactive streams:

1. Non-Blocking I/O

Non-blocking I/O (Input/Output) is a fundamental aspect that enables reactive streams. In a non-blocking I/O model, server threads do not get blocked while waiting for I/O operations (like reading from a network socket) to complete. Instead, the server can continue processing other tasks while waiting for the I/O operation to finish.

Implementation in Servers:

  • Netty: An asynchronous event-driven network application framework for Java that provides non-blocking I/O operations.

  • Vert.x: A toolkit for building reactive applications on the JVM, utilizing non-blocking I/O for handling a large number of connections with minimal threads.

2. Event Loop Model

The event loop model allows a server to manage numerous connections concurrently with a small number of threads. This is achieved by continuously polling for events and dispatching them to the appropriate handlers.

Key Components:

  • Event Loop: A loop that listens for and dispatches events or messages in a program.

  • Callbacks: Functions that get called when an event occurs (e.g., data is received, a connection is established).

Example: Node.js, which uses the libuv library to implement an event-driven, non-blocking I/O model.

3. Backpressure Management

Backpressure is the mechanism by which a system regulates the flow of data between producers and consumers. In a reactive stream, consumers can signal how much data they can handle, and producers must respect these signals to prevent overwhelming the consumers.

Backpressure Mechanisms:

  • Request-N Method: Consumers request a specific number of items from the producer.

  • Buffers: Temporarily store data when the producer is faster than the consumer.

Implementation in Project Reactor:

  • BaseSubscriber: A base class for implementing backpressure-aware subscribers in Reactor.

  • Buffering Operators: Operators like buffer, window, and onBackpressureBuffer help manage backpressure by controlling data flow.

4. Asynchronous Processing

Asynchronous processing ensures that tasks can be executed without waiting for other tasks to complete, thus making efficient use of system resources and improving responsiveness.

Reactive Programming Libraries:

  • Reactor: Provides abstractions like Mono and Flux for asynchronous programming in Java.

  • RxJava: A Java implementation of Reactive Extensions, providing a similar model for asynchronous data streams.

5. Scheduler Management

Schedulers in reactive programming control the execution context of the data streams, determining on which thread or thread pool the tasks will run.

Types of Schedulers:

  • Immediate Scheduler: Executes tasks immediately on the current thread.

  • Single Scheduler: Runs tasks on a single dedicated thread.

  • Elastic Scheduler: Dynamically creates threads as needed and reuses idle threads.

  • Parallel Scheduler: Uses a fixed pool of workers for parallel processing.

Usage in Project Reactor:

  • publishOn and subscribeOn operators to switch the execution context.

Example Code: Non-Blocking I/O with Project Reactor

Here’s a basic example demonstrating non-blocking I/O with Project Reactor:

import reactor.core.publisher.Flux;
import reactor.netty.http.server.HttpServer;

public class ReactiveServer {
    public static void main(String[] args) {
                  .route(routes ->
                      routes.get("/stream", (request, response) ->
                                                   .map(i -> "Data chunk " + i + "\n"))))

In this example:

  • Netty-based HTTP Server: Uses Reactor Netty to create an HTTP server.

  • Reactive Route: Defines a route /stream that streams data chunks at one-second intervals.

  • Non-Blocking Data Flow: The data is emitted asynchronously using Flux.interval.

Core Concepts of Reactive Streams

  • Publisher: Produces a potentially unbounded number of sequenced elements, publishing them according to the demand received from its Subscriber(s).

  • Subscriber: Consumes elements produced by the Publisher, receiving notifications of new data, errors, or completion.

  • Subscription: Represents a one-to-one lifecycle of a Subscriber subscribing to a Publisher.

  • Processor: A component that acts as both a Subscriber and a Publisher, often used to transform data between the source and the final Subscriber.

Benefits of Reactive Streams

  • Non-blocking: Handles requests asynchronously without blocking threads.

  • Backpressure: Manages flow control by allowing subscribers to dictate how much data they can handle.

  • Composability: Easily combine multiple streams to build complex asynchronous data pipelines.

  • Error Handling: Built-in mechanisms to manage errors gracefully.

Project Reactor

Project Reactor is a fully non-blocking foundation for building reactive applications on the JVM. It is based on the Reactive Streams specification and provides a rich set of operators for composing asynchronous and event-driven programs.

Key Components of Project Reactor

  • Flux: A reactive sequence that can emit zero to many elements.

  • Mono: A reactive sequence that emits zero or one element.

  • Schedulers: Control the execution context of reactive streams, allowing fine-grained control over concurrency and parallelism.

Getting Started with Project Reactor

To start using Project Reactor, include the following dependencies in your pom.xml for Maven:


For Gradle, add:

implementation 'io.projectreactor:reactor-core:3.4.8'

Reactive Streams - Flux and Mono

Creating a Flux

A Flux can emit multiple items, complete, or signal an error. Here's a simple example of creating and subscribing to a Flux:

import reactor.core.publisher.Flux;

public class FluxExample {
    public static void main(String[] args) {
        Flux<String> flux = Flux.just("Hello", "World", "From", "Project", "Reactor");

                       error -> System.err.println("Error: " + error),
                       () -> System.out.println("Completed"));

This code creates a reactive stream using Project Reactor's Flux that emits a series of strings. It then subscribes to this Flux to print each string to the console, handle any potential errors, and print a completion message once all strings have been emitted.

Creating a Mono

A Mono represents a single-valued or empty result. Here's an example of creating and subscribing to a Mono:

import reactor.core.publisher.Mono;

public class MonoExample {
    public static void main(String[] args) {
        Mono<String> mono = Mono.just("Hello Mono");

                       error -> System.err.println("Error: " + error),
                       () -> System.out.println("Completed"));

Advanced Features of Project Reactor

Combining Streams

Project Reactor provides powerful operators for combining multiple streams. Here’s an example using merge and zip:

import reactor.core.publisher.Flux;

public class CombiningStreamsExample {
    public static void main(String[] args) {
        Flux<String> flux1 = Flux.just("A", "B", "C");
        Flux<String> flux2 = Flux.just("1", "2", "3");

        // Merge two fluxes
        Flux<String> mergedFlux = Flux.merge(flux1, flux2);

        // Zip two fluxes
        Flux<String> zippedFlux =, flux2, (s1, s2) -> s1 + s2);
  • Merging Streams: Using Flux.merge, it merges two Flux instances so that items from both sources are emitted as they arrive.

  • Zipping Streams: Using, it combines items from two Flux instances pairwise, producing a new Flux where each item is the concatenation of the corresponding items from the source Flux instances.

Backpressure Handling in Project Reactor

Backpressure is a critical concept in reactive programming that deals with controlling the flow of data between a producer (which emits data) and a consumer (which processes data) to prevent the consumer from being overwhelmed by the producer. Project Reactor provides several mechanisms to handle backpressure effectively:

Backpressure Strategies

  1. Buffering: Collects all emitted items in a buffer until the consumer is ready to process them. This can be done using operators like onBackpressureBuffer.

     Flux.range(1, 100)

    In this example, onBackpressureBuffer(10) specifies a buffer size of 10. If the buffer is full, the producer will be paused until space is available.

  2. Dropping: Drops items if the consumer cannot keep up. This can be achieved using onBackpressureDrop.

     Flux.range(1, 100)
         .onBackpressureDrop(item -> System.out.println("Dropped: " + item))

    Here, items that cannot be processed immediately are dropped, and a message is printed for each dropped item.

  3. Latest: Keeps only the latest item, discarding the rest until the consumer is ready to process the next item. Use onBackpressureLatest to achieve this.

     Flux.range(1, 100)

    This approach ensures that the consumer always processes the most recent item, discarding intermediate items if necessary.

  4. Error: Propagates an error if the producer cannot emit items due to backpressure. This can be handled using onBackpressureError.

     Flux.range(1, 100)
             error -> System.err.println("Error: " + error)

    When the buffer is full, this method throws an error, which can be handled by the subscriber.

Backpressure Handling with BaseSubscriber

Project Reactor’s BaseSubscriber class allows fine-grained control over backpressure by explicitly requesting items.

import reactor.core.publisher.BaseSubscriber;
import reactor.core.publisher.Flux;

public class BackpressureExample {
    public static void main(String[] args) {
        Flux.range(1, 100)
            .subscribe(new BaseSubscriber<Integer>() {
                protected void hookOnSubscribe(Subscription subscription) {
                    request(1); // Request the first item

                protected void hookOnNext(Integer value) {
                    request(1); // Request the next item after processing the current one

In this example:

  • The hookOnSubscribe method requests the first item.

  • The hookOnNext method processes each item and then requests the next one, effectively controlling the flow of items.

Error Handling in Project Reactor

Error handling is an essential aspect of reactive programming, allowing applications to manage and recover from failures gracefully. Project Reactor provides several mechanisms for error handling:

Error Handling Operators

  1. onErrorReturn: Provides a fallback value if an error occurs.

     Flux<Integer> flux = Flux.just(1, 2, 0, 4)
                              .map(i -> 10 / i)

    In this example, if a division by zero occurs, the onErrorReturn operator provides -1 as a fallback value.

  2. onErrorResume: Switches to an alternative Publisher when an error occurs.

     Flux<Integer> flux = Flux.just(1, 2, 0, 4)
                              .map(i -> 10 / i)
                              .onErrorResume(e -> Flux.just(-1, -2, -3));

    If an error occurs, the onErrorResume operator switches to an alternative Flux emitting -1, -2, and -3.

  3. onErrorMap: Transforms the error into another exception.

     Flux<Integer> flux = Flux.just(1, 2, 0, 4)
                              .map(i -> 10 / i)
                              .onErrorMap(e -> new RuntimeException("Custom exception: " + e.getMessage()));
     flux.subscribe(System.out::println, error -> System.err.println("Error: " + error));

    This example converts the original exception into a custom RuntimeException with a detailed message.

  4. retry: Retries the sequence when an error occurs.

     Flux<Integer> flux = Flux.just(1, 2, 0, 4)
                              .map(i -> 10 / i)
                              .retry(1); // Retry once
     flux.subscribe(System.out::println, error -> System.err.println("Error: " + error));

    The retry operator retries the sequence once if an error occurs. You can specify the number of retries.

Global Error Handling

For more centralized error handling, you can use the doOnError method to log errors or perform other actions globally.

Flux<Integer> flux = Flux.just(1, 2, 0, 4)
                         .map(i -> 10 / i)
                         .doOnError(error -> System.err.println("Error occurred: " + error.getMessage()))


In this example, doOnError logs the error message before the onErrorReturn operator provides a fallback value.

Use Cases of Reactive Streams with Project Reactor

Real-time Data Processing

Project Reactor is ideal for real-time data processing scenarios, such as financial tickers, live sports scores, or social media feeds.

Example: A livestock ticker application that processes and displays stock prices in real-time.

import reactor.core.publisher.Flux;

import java.time.Duration;

public class StockTicker {
    public static void main(String[] args) {
            .map(tick -> "Stock price at tick " + tick + ": " + (100 + Math.random() * 10))

Microservices Communication

Reactive Streams facilitate efficient communication between microservices. Project Reactor can be used to build non-blocking REST APIs and manage inter-service communication.

Example: A service that aggregates data from multiple microservices and provides a consolidated response.

import org.springframework.web.reactive.function.client.WebClient;
import reactor.core.publisher.Mono;

public class AggregatorService {
    private final WebClient webClient = WebClient.create();

    public Mono<String> aggregateData() {
        Mono<String> service1 = webClient.get().uri("http://service1/data").retrieve().bodyToMono(String.class);
        Mono<String> service2 = webClient.get().uri("http://service2/data").retrieve().bodyToMono(String.class);

        return, service2, (data1, data2) -> "Combined Data: " + data1 + ", " + data2);

    public static void main(String[] args) {
        AggregatorService service = new AggregatorService();


Reactive Streams and Project Reactor offer a robust framework for handling asynchronous data streams in Java. By leveraging these technologies, developers can build highly responsive, resilient, and scalable applications. Whether you are dealing with real-time data processing, microservices communication, or complex event-driven systems, Project Reactor provides the tools and abstractions needed to succeed.

As you explore the potential of Reactive Streams in your projects, consider the best practices and examples provided in this article to maximize the effectiveness of your applications.