Book Image

Hands-On Reactive Programming with Clojure - Second Edition

By : Konrad Szydlo, Leonardo Borges
Book Image

Hands-On Reactive Programming with Clojure - Second Edition

By: Konrad Szydlo, Leonardo Borges

Overview of this book

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.
Table of Contents (15 chapters)

Futures and blocking IO

The choice of using ForkJoinPool for imminent is deliberate. ForkJoinPool, which was added in Java 7, is extremely smart. When created, you give it a desired level of parallelism, which defaults to the number of available processors.

ForkJoinPool then attempts to honor this desired parallelism by dynamically shrinking and expanding the pool as required. When a task is submitted to this pool, it doesn't necessarily create a new thread if it doesn't have to. This allows the pool to serve an extremely large number of tasks with a much smaller number of actual threads.

However, it cannot guarantee such optimizations in the face of blocking IO, as it can't know whether the thread is blocking, waiting for an external resource. Nevertheless, ForkJoinPool provides a mechanism by which threads can notify the pool when they might block.

Imminent takes...