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

The first example - searching data without an index

In Chapter 8, Processing Massive Datasets with Parallel Streams - The Map and Reduce Model, you learned how to implement a search tool to look for the documents similar to an input query using an inverted index. This data structure makes the search operation easier and faster, but there will be situations where you will have to make a search operation over a big set of data and you won't have an inverted index to help you. In these cases, you have to process all the elements of the dataset to get the correct results. In this example, you will see one of these situations and how the reduce() method of the Stream API can help you.

To implement this example, you will use a subset of the Amazon product co-purchasing network metadata that includes information about 548,552 products sold by Amazon, which includes title, salesrank, and the lists of similar products, categories, and reviews. You can download this dataset from https://snap.stanford...