Book Image

Learning RxJava - Second Edition

By : Nick Samoylov, Thomas Nield
Book Image

Learning RxJava - Second Edition

By: Nick Samoylov, Thomas Nield

Overview of this book

RxJava is not just a popular library for building asynchronous and event-based applications; it also enables you to create a cleaner and more readable code base. In this book, you’ll cover the core fundamentals of reactive programming and learn how to design and implement reactive libraries and applications. Learning RxJava will help you understand how reactive programming works and guide you in writing your first example in reactive code. You’ll get to grips with the workings of Observable and Subscriber, and see how they are used in different contexts using real-world use cases. The book will also take you through multicasting and caching to help prevent redundant work with multiple Observers. You’ll then learn how to create your own RxJava operators by reusing reactive logic. As you advance, you’ll explore effective tools and libraries to test and debug RxJava code. Finally, you’ll delve into RxAndroid extensions and use Kotlin features to streamline your Android apps. By the end of this book, you'll become proficient in writing reactive code in Java and Kotlin to build concurrent applications, including Android applications.
Table of Contents (22 chapters)
1
Section 1: Foundations of Reactive Programming in Java
5
Section 2: Reactive Operators
12
Section 3: Integration of RxJava applications
Appendix B: Functional Types
Appendix E: Understanding Schedulers

Flowable and Backpressure

In the previous chapter, we learned about different operators that intercept rapidly firing emissions and either consolidate or omit them to decrease the number of emissions passed downstream. Another – arguably better – way to address the issue of when a source is producing emissions faster than the downstream can process them is to proactively make the source slow down and emit at a pace that agrees with the downstream operations. This last technique is especially important when we do not want to miss any of the emissions but do not want to consolidate them or cannot provide a large enough buffer to keep all the excess emissions in the queue.

Sending the request to slow down to the source is known as a backpressure, or flow control, and it can be enabled by using a Flowable instead of an Observable. This will be the main focus of this...