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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

Summary


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...

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