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Mastering Concurrency Programming with Java 9, Second Edition

You're reading from   Mastering Concurrency Programming with Java 9, Second Edition Fast, reactive and parallel application development

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785887949
Length 516 pages
Edition 2nd Edition
Languages
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Author (1):
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Javier Fernández González Javier Fernández González
Author Profile Icon Javier Fernández González
Javier Fernández González
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Table of Contents (14) Chapters Close

Preface 1. The First Step - Concurrency Design Principles FREE CHAPTER 2. Working with Basic Elements - Threads and Runnables 3. Managing Lots of Threads - Executors 4. Getting the Most from Executors 5. Getting Data from Tasks - The Callable and Future Interfaces 6. Running Tasks Divided into Phases - The Phaser Class 7. Optimizing Divide and Conquer Solutions - The Fork/Join Framework 8. Processing Massive Datasets with Parallel Streams - The Map and Reduce Model 9. Processing Massive Datasets with Parallel Streams - The Map and Collect Model 10. Asynchronous Stream Processing - Reactive Streams 11. Diving into Concurrent Data Structures and Synchronization Utilities 12. Testing and Monitoring Concurrent Applications 13. Concurrency in JVM - Clojure and Groovy with the Gpars Library and Scala

The third example - the merge sort algorithm


The merge sort algorithm is a very popular sorting algorithm, which is often implemented using the divide and conquer technique, so it's a very good candidate to test the fork/join framework.

To implement the merge sort algorithm, we divide the unsorted lists into sublists of one element. Then, we merge those unsorted sublists to produce ordered sublists until we have processed all the sublists, and we have only the original list, but with all the elements sorted.

To make the concurrent version of our algorithm, we have used the CountedCompleter tasks, introduced in Java 8. The most important characteristic of these tasks is that they include a method to be executed when all their child tasks have finished their execution.

To test out implementations, we have used the Amazon product co-purchasing network metadata (you can download it from https://snap.stanford.edu/data/amazon-meta.html). In particular, we have created a list with the salesrank of...

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