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

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785887949

Length 516 pages

Edition 2nd Edition

Languages

Java

Tools

Construct

Concepts

Application Development

Author (1):

Javier Fernández González

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Table of Contents (14) Chapters

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 - a numerical summarization application

One of the most common needs when you have a big set of data is to process its elements to measure certain characteristics. For example, if you have a set with the products purchased in a shop, you can count the number of products you have sold, the number of units per product you have sold, or the average amount that each customer spent. We have named that process numerical summarization.

In this chapter, we are going to use streams to obtain some measures of the Online Retail dataset of the UCI Machine Learning Repository, which you can download from http://archive.ics.uci.edu/ml/datasets/Online+Retail. This dataset stores all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.

Unlike other chapters, in this case, we explain the concurrent version using streams and then how to implement a serial equivalent version to verify that concurrency improves performance with streams...