Guide to Spring Reactive Programming using WebFlux

Guide to Spring Reactive Programming using
WebFlux
Reactive programming is a programming paradigm that promotes an asynchronous,
non-blocking, event driven approach to data processing. Reactive programming
involves modelling data and events as observable data streams and implementing
data processing routines to react to the changes in those streams.
In the reactive style of programming, we make a request for the resource and start
performing other things. When the data is available, we get the notification along
with the data in the form of call back function. In the callback function, we handle
the response as per application /user needs.
Features of Reactive Programming
• Asynchronous & Non-blocking
• Functional style of coding

• Data flow as event driven stream
• Backpressure on data streams
When considering whether to use Spring MVC or Spring WebFlux, there are various
factors you must consider.
Spring MVC: It’s based on a servlet API and follows an imperative programming
model. This means you write code in a step-by-step manner, which is generally easier
to follow.
Spring WebFlux: It offers a reactive programming model. Reactive programming is
about handling asynchronous streams of data. This requires a change in thinking and
can be more challenging than the traditional imperative model.
Spring MVC: If you are familiar with traditional web application development, Spring
MVC’s imperative model might seem more straightforward. It’s easier to read, write,
and understand for developers accustomed to this approach.
Spring WebFlux: Writing reactive code can initially seem complex because of the
shift in mindset. However, for some use cases, like streaming data, it can simplify
your code.
Spring MVC: Debugging is typically more straightforward with an imperative model
because the call stacks are more predictable and easier to trace.

Spring WebFlux: Debugging reactive streams can be tricky, especially for developers
new to the reactive paradigm. However, tools and practices are evolving to better
support this.
Spring MVC: Works naturally with blocking resources like traditional RDBMS using
JDBC or JPA (Java Persistence API).
Spring WebFlux: If you have blocking dependencies like traditional databases, you
might not get the full benefits of the reactive model. However, reactive databases
like MongoDB Reactive, Cassandra Reactive, etc., can be integrated natively with
WebFlux.
Spring MVC: Uses a thread-per-request model. For a high number of simultaneous
connections, this can lead to a large number of threads, which may not be efficient.
Spring WebFlux: Uses an event-loop concurrency model, which can handle a vast
number of simultaneous connections with a smaller number of threads. It’s designed
for high concurrency.
Spring MVC: Typically runs on servlet containers like Tomcat, Jetty, etc.
Spring WebFlux: Runs on reactive runtimes like Netty. This provides non-blocking
and highly scalable operations, suitable for high-performance systems.
Spring MVC: Typically uses annotated controllers.
Spring WebFlux: In addition to annotated controllers, WebFlux supports functional
endpoints which allow for programmatic route definitions.
Spring WebFlux:
As we know, Spring provides Web MVC framework to handle the HTTP requests, but
it is Blocking & Non-Reactive in nature, so to support reactive programming Spring
provides one more web framework in Spring 5 (includes in Spring Boot 2.0) called
WebFlux.

It is a reactive-stack web framework that is fully non-blocking, supports reactive
streams back pressure. It uses project Reactor as a reactive library. The Reactor is a
Reactive Streams Library and therefore, all of its operators support non-blocking
back pressure.
It uses two publishers:
• Mono
• Flux
MONO:
A mono is a specialized Publisher that emits at most one item and then optionally
terminates with an onComplete signal or an onError signal. In short, it returns 0 or 1
element.
• Mono is another implementation of Publisher.
• It emits at most one item and then (optionally) terminates with an
onComplete signal or an onError signal.
• Like Flux, Mono is also asynchronous in nature.
Mono noData = Mono.empty();
Mono data = Mono.just(“rishi”);
FLUX:
A flux is a standard Publisher representing an asynchronous sequence of 0 to N
emitted items, optionally terminated by either a completion signal or an error. These
three types of signals translate to calls to a downstream subscriber’s onNext,
onComplete, or onError methods.
• Flux is an implementation of Publisher.
• It will emit 0 to N elements and/or a complete or an error call.
• Stream pipeline is synchronous whereas Flux pipeline is completely
asynchronous. It will emit values only when there is a downstream subscriber.
To subscribe, we need to call the subscribe method on Flux. There are different
variants of the subscribe method available, which we need to use as per the need:
Flux flux1 = Flux.just(“foo”, “bar”, “foobar”);

Flux flux2 = Flux.fromIterable(Arrays.asList(“A”, “B”, “C”));
Flux flux3 = Flux.range(5, 3);
// subscribe
flux.subscribe();
Frequently used operations on Mono/Flux
• just(-): Create a new Mono that emits the specified item, which is captured at
instantiation time.
• fromArray(-): Create a Flux that emits the items contained in the provided
array.
• fromIterable(-): Create a Flux that emits the items contained in the provided
iterable. The Iterable.iterator() method will be invoked at least once and at
most twice for each subscriber.
• fromStream(-): Create a Flux that emits the items contained in a Stream
created by the provided Supplier for each subscription. The Stream is closed
automatically by the operator on cancellation, error, or completion.
• empty(): Create a Flux that completes without emitting any item.
• doOnNext(-): Add behaviour (side-effect) triggered when the Flux emits an
item.
• doOnComplete(-): Add behaviour (side-effect) triggered when the Flux
completes successfully.
• doOnError(-): Add behaviour (side-effect) triggered when the Flux completes
with an error.
• map(-): Transform the items emitted by this Flux by applying a synchronous
function to each item.
• flatMap(-): Transform the item emitted by this Mono asynchronously,
returning the value emitted by another Mono (possibly changing the value
type).
• subscribe(-, -, -): Subscribe a Consumer to this Flux that will consume all the
elements in the sequence. It will request an unbounded demand.
• log(): Observe all Reactive Streams signals and trace them using Logger
support. Default will use Level.INFO and java.util.logging. If SLF4J is available,
it will be used instead.

• delayElements(-): Delay each of this Flux elements (Subscriber.onNext signals)
by a given Duration. Signals are delayed and continue the parallel default
Scheduler, but empty sequences or immediate error signals are not delayed.
• block(): Subscribe to this Mono and block indefinitely until a next signal is
received. Returns that value, or null if the Mono completes empty. In case the
Mono errors, the original exception is thrown (wrapped in a RuntimeException
if it was a checked exception)
Working with Spring Web Flux –
Understanding the Reactive nature
Requirement: Send Promos to all the customers of an e-Commerce website
Step-1: Create a Spring Boot project using Maven (Choose Spring Boot version 2.0 or
later)
Step-2: Add the below spring-boot-starter-webflux dependency in pom.xml
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-webflux</artifactId>
</dependency>
This dependency includes the below dependencies
• spring-webflux framework
• reactor-core that we need for reactive streams
reactor-netty (the default server that supports reactive streams). Any other servlet
3.1+ containers like Tomcat, Jetty or non-servlet containers like Undertow can be
used as well
Version will be picked from spring-boot-starter-parent dependency version
Step-3: Create a Customer DTO class with the fields Id, Name & Email Id

Step-4: Create a Customer Repo with 2 functions loadCustomers(),
loadCustomerStream() as in the below snapshot.

Step-5: Create a Customer Service with 2 functions, one is to send promos to list of
customers, another is to send promos to customer stream

Step-6: Create a Customer REST Controller with 2 end points as in the below
screenshot
Summary:
Spring introduced a Multi-Event Loop model to enable a reactive stack known as
WebFlux. It is a fully non-blocking and annotation-based web framework built on
Project Reactor which allows building reactive web applications on the HTTP layer. It
provides support for popular inbuilt severs like Netty, Undertow, and Servlet 3.1
containers.
WebFlux is also relevant for applications that need scalability or to stream request
data in real time. While implementing a micro-service in WebFlux we must consider
that the entire flow uses reactive and asynchronous programming and none of the
operations are blocking in nature.
Originally published by: Guide to Spring Reactive Programming using WebFlux

Guide to Spring Reactive Programming using WebFlux

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