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Hands-On Reactive Programming with Clojure
Hands-On Reactive Programming with Clojure

Hands-On Reactive Programming with Clojure: Create asynchronous, event-based, and concurrent applications , Second Edition

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Profile Icon Konrad Szydlo Profile Icon Leonardo Borges
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Profile Icon Konrad Szydlo Profile Icon Leonardo Borges
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Hands-On Reactive Programming with Clojure

A Look at Reactive Extensions

Reactive Extensions (Rx) is a Reactive Programming library from Microsoft that's used for building complex asynchronous programs. It models time-varying values and events as observable sequences and is implemented by extending the Observer design pattern.

Its first target platform was .NET, but Netflix has ported Rx to JVM under the name RxJava. Microsoft also develops and maintains a port of Rx to JavaScript called RxJS, which is the tool we used to build the sine wave application. The two ports work a treat for us, since Clojure runs on JVM and ClojureScript in JavaScript environments.

As we saw in Chapter 1, What is Reactive Programming?, Rx is inspired by Functional Reactive Programming but uses different terminology. In FRP, the two main abstractions are behaviors and events. Although the implementation details are different, observable...

The observer pattern revisited

In Chapter 1, What is Reactive Programming?, we saw a brief overview of the Observer design pattern and a simple implementation of it in Clojure using watches. Here's how we did it:

(def numbers (atom [])) 
 
(defn adder [key ref old-state new-state] 
  (print "Current sum is " (reduce + new-state))) 
 
(add-watch numbers :adder adder)  

In the preceding example, our observable subject is var known as numbers. The observer is the adder watch. When the observable changes, it pushes its changes to the observer synchronously.

Now, contrast this to working with sequences:

(->> [1 2 3 4 5 6] 
     (map inc) 
     (filter even?) 
     (reduce +)) 

This time around, the vector is the subject being observed and the functions processing it can be thought of as the observers. However, this works in a pull-based model. The vector doesn...

Creating observables

This chapter is all about Reactive Extensions, so let's go ahead and create a project called rx-playground which we will be using in our exploratory tour. We will use RxClojure (see https://github.com/ReactiveX/RxClojure), a library that provides Clojure bindings for RxJava() (see https://github.com/ReactiveX/RxJava):

$ lein new rx-playground

Open the project file and add a dependency on RxJava's Clojure bindings:

(defproject rx-playground "0.1.0-SNAPSHOT" 
  :description "FIXME: write description" 
  :url "http://example.com/FIXME" 
  :license {:name "Eclipse Public License" 
            :url "http://www.eclipse.org/legal/epl-v10.html"} 
  :dependencies [[org.clojure/clojure "1.9.0"] 
                 [io.reactivex/rxclojure "1.0.0"]]) 

Now, fire up a REPL in the project&apos...

Manipulating observables

Now that we know how to create observables, we should look at what kinds of interesting things we can do with them. In this section, we will see what it means to treat observables as sequences.

We'll start with something simple. Let's print the sum of the first five positive even integers from an observable of all integers:

(rx/subscribe (->> (Observable/interval 1 TimeUnit/MICROSECONDS) 
                   (rx/filter even?) 
                   (rx/take 5) 
                   (rx/reduce +)) 
                   prn-to-repl) 

This is starting to look awfully familiar to us. We create an interval that will emit all positive integers starting at zero every one microsecond. Then, we filter all even numbers in this observable. Obviously, this is too big a list to handle, so we simply take the first five elements from it. Finally, we reduce the...

flatmap and friends

In the previous section, we learned how to transform and combine observables with operations such as map, reduce, and zip. However, the two observables that we just looked at—musicians and bands—were perfectly capable of producing values on their own; they did not need any extra input.

In this section, we will examine a different scenario: we'll learn how we can combine observables, where the output of one is the input of another. We encountered flatmap before, in Chapter 1, What is Reactive Programming? If you have been wondering what its role is, this section addresses exactly that.

Here's what we are going to do: given an observable representing a list of all positive integers, we'll calculate the factorial for all even numbers in that list. Since the list is too big, we'll take five items from it. The end result should...

Error handling

A very important aspect of building reliable applications is knowing what to do when things go wrong. It is naive to assume that the network is reliable, that hardware won't fail, or that we, as developers, won't make mistakes.

RxJava embraces this fact and provides a rich set of combinators to deal with failure, a few of which we will examine here.

OnError

Let's get started by creating a badly behaved observable that always throws an exception:

(defn exceptional-obs [] 
  (rx/observable* 
   (fn [observer] 
     (rx/on-next observer (throw (Exception. "Oops. Something went wrong"))) 
     (rx/on-completed observer)))) 

Now, let's watch what happens if we subscribe to it:

(rx/subscribe...

Backpressure

Another issue we might be faced with is observables that produce items faster than we can consume them. The problem that arises in this scenario is to do with the ever-growing backlog of items.

As an example, think about zipping two observables together. The zip operator (or map in RxClojure) will only emit a new value when all observables have emitted an item.

So, if one of these observables is a lot faster at producing items than the others, map will need to buffer these items and wait for the others, which will most likely cause an error, as shown here:

(defn fast-producing-obs [] 
  (rx/map inc (Observable/interval 1 TimeUnit/MILLISECONDS))) 
 
(defn slow-producing-obs [] 
  (rx/map inc (Observable/interval 500 TimeUnit/MILLISECONDS))) 
 
(rx/subscribe (->> (rx/map vector 
                           (fast-producing-obs) 
                           (slow...

Summary

In this chapter, we took a deep dive into RxJava, a port form of Microsoft's Reactive Extensions from .NET. We learned about its main abstraction, the observable, and how it relates to iterables.

We also learned how to create, manipulate, and combine observables in several ways. The examples shown here were contrived to keep things simple. Nevertheless, all of the concepts that have been presented are extremely useful in real applications and will come in handy for our next chapter, where we will put them to use in a more substantial example.

Finally, we finished by looking at error handling and backpressure, both of which are important characteristics of reliable applications that should always be kept in mind.

In the next chapter we will create a stock market monitoring application using observable sequences and RxClojure. They will help us reduce the complexity...

Further reading

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Key benefits

  • Leverage the features of Functional Reactive Programming using Clojure
  • Create dataflow-based systems that are the building blocks of Reactive Programming
  • Use different Functional Reactive Programming frameworks, techniques, and patterns to solve real-world problems

Description

Reactive Programming is central to many concurrent systems, and can help make the process of developing highly concurrent, event-driven, and asynchronous applications simpler and less error-prone. This book will allow you to explore Reactive Programming in Clojure 1.9 and help you get to grips with some of its new features such as transducers, reader conditionals, additional string functions, direct linking, and socket servers. Hands-On Reactive Programming with Clojure starts by introducing you to Functional Reactive Programming (FRP) and its formulations, as well as showing you how it inspired Compositional Event Systems (CES). It then guides you in understanding Reactive Programming as well as learning how to develop your ability to work with time-varying values thanks to examples of reactive applications implemented in different frameworks. You'll also gain insight into some interesting Reactive design patterns such as the simple component, circuit breaker, request-response, and multiple-master replication. Finally, the book introduces microservices-based architecture in Clojure and closes with examples of unit testing frameworks. By the end of the book, you will have gained all the knowledge you need to create applications using different Reactive Programming approaches.

Who is this book for?

If you’re interested in using Reactive Programming to build asynchronous and concurrent applications, this is the book for you. Basic knowledge of Clojure programming is necessary to understand the concepts covered in this book.

What you will learn

  • Understand how to think in terms of time-varying values and event streams
  • Create, compose, and transform observable sequences using Reactive extensions
  • Build a CES framework from scratch using core.async as its foundation
  • Develop a simple ClojureScript game using Reagi
  • Integrate Om and RxJS in a web application
  • Implement a reactive API in Amazon Web Services (AWS)
  • Discover helpful approaches to backpressure and error handling
  • Get to grips with futures and their applications

Product Details

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Length: 298 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789346138
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Length: 298 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789346138
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Table of Contents

13 Chapters
What is Reactive Programming? Chevron down icon Chevron up icon
A Look at Reactive Extensions Chevron down icon Chevron up icon
Asynchronous Programming and Networking Chevron down icon Chevron up icon
Introduction to core.async Chevron down icon Chevron up icon
Creating Your Own CES Framework with core.async Chevron down icon Chevron up icon
Building a Simple ClojureScript Game with Reagi Chevron down icon Chevron up icon
The UI as a Function Chevron down icon Chevron up icon
A New Approach to Futures Chevron down icon Chevron up icon
A Reactive API to Amazon Web Services Chevron down icon Chevron up icon
Reactive Microservices Chevron down icon Chevron up icon
Testing Reactive Apps Chevron down icon Chevron up icon
Concurrency Utilities in Clojure Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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