Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By : Javier Fernández González
Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By: Javier Fernández González

Overview of this book

Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. Java 9 includes a comprehensive API with lots of ready-to-use components for easily implementing powerful concurrency applications, but with high flexibility so you can adapt these components to your needs. The book starts with a full description of the design principles of concurrent applications and explains how to parallelize a sequential algorithm. You will then be introduced to Threads and Runnables, which are an integral part of Java 9's concurrency API. You will see how to use all the components of the Java concurrency API, from the basics to the most advanced techniques, and will implement them in powerful real-world concurrency applications. The book ends with a detailed description of the tools and techniques you can use to test a concurrent Java application, along with a brief insight into other concurrency mechanisms in JVM.

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Table of Contents (21 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Dedication
Dedication
Preface
Preface
Free Chapter
1
The First Step - Concurrency Design Principles
The First Step - Concurrency Design Principles
Basic concurrency concepts
Possible problems in concurrent applications
A methodology to design concurrent algorithms
Java Concurrency API
Concurrency design patterns
Tips and tricks for designing concurrent algorithms
Summary
2
Working with Basic Elements - Threads and Runnables
Working with Basic Elements - Threads and Runnables
Threads in Java
First example: matrix multiplication
Second example - file search
Summary
3
Managing Lots of Threads - Executors
Managing Lots of Threads - Executors
An introduction to executors
First example - the k-nearest neighbors algorithm
Second example - concurrency in a client/server environment
Summary
4
Getting the Most from Executors
Getting the Most from Executors
Advanced characteristics of executors
First example - an advanced server application
Second example - executing periodic tasks
Additional information about executors
Summary
5
Getting Data from Tasks - The Callable and Future Interfaces
Getting Data from Tasks - The Callable and Future Interfaces
Introducing the Callable and Future interfaces
First example - a best-matching algorithm for words
The second example - creating an inverted index for a collection of documents
Summary
6
Running Tasks Divided into Phases - The Phaser Class
Running Tasks Divided into Phases - The Phaser Class
An introduction to the Phaser class
First example - a keyword extraction algorithm
The second example - a genetic algorithm
Summary
7
Optimizing Divide and Conquer Solutions - The Fork/Join Framework
Optimizing Divide and Conquer Solutions - The Fork/Join Framework
An introduction to the fork/join framework
The first example - the k-means clustering algorithm
The second example - a data filtering algorithm
The third example - the merge sort algorithm
Other methods of the fork/join framework
Summary
8
Processing Massive Datasets with Parallel Streams - The Map and Reduce Model
Processing Massive Datasets with Parallel Streams - The Map and Reduce Model
An introduction to streams
The first example - a numerical summarization application
The second example - an information retrieval search tool
Summary
9
Processing Massive Datasets with Parallel Streams - The Map and Collect Model
Processing Massive Datasets with Parallel Streams - The Map and Collect Model
Using streams to collect data
The first example - searching data without an index
The second example - a recommendation system
The third example - common contacts in a social network
Summary
10
Asynchronous Stream Processing - Reactive Streams
Asynchronous Stream Processing - Reactive Streams
Introduction to reactive streams in Java
The first example - a centralized system for event notification
The second example - a news system
Summary
11
Diving into Concurrent Data Structures and Synchronization Utilities
Diving into Concurrent Data Structures and Synchronization Utilities
Concurrent data structures
Synchronization mechanisms
Summary
12
Testing and Monitoring Concurrent Applications
Testing and Monitoring Concurrent Applications
Monitoring concurrency objects
Monitoring concurrency applications
Testing concurrency applications
Summary
13
Concurrency in JVM - Clojure and Groovy with the Gpars Library and Scala
Concurrency in JVM - Clojure and Groovy with the Gpars Library and Scala
Concurrency in Clojure
Concurrency in Groovy with the GPars library
Software transactional memory
Concurrency in Scala
Summary

First example - a best-matching algorithm for words

The main objective of a best-matching algorithm for words is to find the words most similar to a string passed as a parameter. To implement one of these algorithms, you need the following:

  • A list of words: In our case, we have used the UK Advanced Cryptics Dictionary (UKACD), which is a word list compiled for the crossword community. It has 250,353 words and idioms. It can be downloaded for free from http://www.crosswordman.com/wordlist.html.
  • A metric to measure the similarity between two words: We have used the Levenshtein distance that is used to measure the difference between two sequences of characters. The Levenshtein distance is the minimal number of insertions, deletions, or substitutions that is necessary to transform the first string into the second string. You can find a brief description of this metric at https://en.wikipedia.org/wiki/Levenshtein_distance.

In our example, you will implement two operations:

  • The first operation returns...
Previous Section
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