Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By : Javier Fernández González
Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By: Javier Fernández González

Overview of this book

Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. Java 9 includes a comprehensive API with lots of ready-to-use components for easily implementing powerful concurrency applications, but with high flexibility so you can adapt these components to your needs. The book starts with a full description of the design principles of concurrent applications and explains how to parallelize a sequential algorithm. You will then be introduced to Threads and Runnables, which are an integral part of Java 9's concurrency API. You will see how to use all the components of the Java concurrency API, from the basics to the most advanced techniques, and will implement them in powerful real-world concurrency applications. The book ends with a detailed description of the tools and techniques you can use to test a concurrent Java application, along with a brief insight into other concurrency mechanisms in JVM.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback


In this chapter, we were introduced to streams, a new feature introduced in Java 8 inspired by functional programming, and got ready to work with the new lambda expressions. A stream is a sequence of data (is not a data structure), which allows you to apply a sequence of operations in a sequential or concurrent way to filter, convert, sort, reduce, or organize those elements to obtain a final object.

You also learned the main characteristics of the streams that we have to take into account when we use streams in our sequential or concurrent applications.

Finally, we used streams in two samples. In the first sample, we used almost all the methods provided by the Stream interface to calculate the statistical data of a large Dataset. We used the Bank Marketing dataset of the UCI Machine Learning Repository with its 45,211 records. In the second sample, we implemented different approaches to a search application in an inverted index to obtain the most relevant documents to a query. This...