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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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Toc

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 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...

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