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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 2. Working with Basic Elements - Threads and Runnables FREE CHAPTER 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

Summary


Java is not the only programming language that you can use to make programs to the JVM. There are a lot of different programming languages, of different paradigms, that can be used for that purpose. Most of this has its own mechanism to implement concurrent applications.

In this chapter, we have seen how you can implement concurrent applications using three languages of the JVM. First, Clojure, which is an implementation of the Lisp functional programming language that offers different mechanisms to write concurrency applications as Atoms, agents, references, delays, futures, and promises. Then, Groovy with the GPars library, offers us a lot of different possibilities with its actors, its dataflows, and its concurrent data structures. Finally, we have Scala and its concurrency model based on Futures and Promises.

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