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Mastering Concurrency Programming with Java 8

You're reading from   Mastering Concurrency Programming with Java 8 Master the principles and techniques of multithreaded programming with the Java 8 Concurrency API

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Product type Paperback
Published in Feb 2016
Publisher Packt
ISBN-13 9781785886126
Length 430 pages
Edition 1st Edition
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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 (13) Chapters Close

Preface 1. The First Step – Concurrency Design Principles 2. Managing Lots of Threads – Executors FREE CHAPTER 3. Getting the Maximum from Executors 4. Getting Data from the Tasks – The Callable and Future Interfaces 5. Running Tasks Divided into Phases – The Phaser Class 6. Optimizing Divide and Conquer Solutions – The Fork/Join Framework 7. Processing Massive Datasets with Parallel Streams – The Map and Reduce Model 8. Processing Massive Datasets with Parallel Streams – The Map and Collect Model 9. Diving into Concurrent Data Structures and Synchronization Utilities 10. Integration of Fragments and Implementation of Alternatives 11. Testing and Monitoring Concurrent Applications Index

The third example – the merge sort algorithm


The merge sort algorithm is a very popular sorting algorithm that is always implemented using the divide and conquer technique, so it's a very good candidate to test with 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 new Fork/Join tasks, the CountedCompleter tasks, introduced in the Java 8 version. The most important characteristics of these tasks are 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...

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