Mastering concurrency programming

Now, in Java 8, it has included the stream APIand new methods and classes to facilitate the implementation of concurrent applications.This book covers the most important elements of the

Mastering Concurrency Programming with Java 8

Mastering Concurrency Programming with Java 8Credits

Deadlock

The basic reader

The advanced reader

Additional information about executors

Summary

The second concurrent version – multiple documents per taskThe MultipleIndexingTask class

Comparing the solutions

The second example – a data filtering algorithm

Common parts

The ConcurrentFileLoader class

The fifth approach – using our own executor

Getting data from the inverted index – the ConcurrentData classGetting the number of words in a file

Getting the average tfxidf value in a file

Getting the maximum and minimum tfxidf values in the indexThe ConcurrentMain class

Comparing the solutions

Summary

8 Processing Massive Datasets with Parallel Streams – The Map and Collect ModelUsing streams to collect data

Interfaces

BlockingQueueBlockingDequeConcurrentMapTransferQueueClasses

LinkedBlockingQueueConcurrentLinkedQueueLinkedBlockingDequeConcurrentLinkedDequeArrayBlockingQueueDelayQueue

LinkedTransferQueuePriorityBlockingQueueConcurrentHashMapUsing the new features

First example with ConcurrentHashMap

The forEach() methodThe search() methodThe reduce() methodThe compute() methodAnother example with ConcurrentHashMap

The spliterator() methodAtomic variables

10 Integration of Fragments and Implementation of AlternativesBig-block synchronization mechanisms

Implementation of alternatives with concurrent programmingThe k-nearest neighbors’ algorithm

Building an inverted index of a collection of documents

A best-matching algorithm for words

A genetic algorithm

A keyword extraction algorithm

Mastering Concurrency Programming with Java 8

Javier Fernández González is a software architect with almost 15 years’ experience with

Java technologies He has worked as a teacher, researcher, programmer, analyst, writer,and now as an architect in all types of projects related to Java, especially J2EE As ateacher, he has taught over 1,000 hours of training in basic Java, J2EE, and Struts

framework As a researcher, he has worked in the field of information retrieval,

developing applications in order to process large amounts of data in Java and has been apart of several journal articles and conference presentations as a coauthor In recent years,

he has worked on developing J2EE web applications for various clients from differentsectors (public administration, insurance, healthcare, transportation, and so on) Currently,

he is working as a software architect at Capgemini, which includes developing and

maintaining applications for an insurance company Also, he is the author of the book

Java 7 Concurrency Cookbook, Packt Publishing.

To Nuria, Paula, and Pelayo, for your infinite love and patience

Antonio Gomes Rodrigues has worked with high-traffic websites, traders applications,

Cloud applications, and so on He has experience of working with many performancetools, such as JProfiler, Yourkit, VisualVM, Dynatrace, AppDynamics, Introscope,

NewRelic, JMeter, LoadRunner, and so on To put in place, he has worked on performancestrategies, load testing, training, troubleshooting, and so on He shares his knowledge inhis French blog (http://arodrigues.developpez.com/), conferences, and book reviews

I would like to thank my wife Aurélie for her support and my child Timothée

Bradley Symons graduated with a BSc (Hons) degree in Computer Science and has

gained the Oracle Java Professional Programmer certification His current occupation is as

a Java server-side developer, but he has previously worked with a variety of languages,including Ruby and Python With over a decade of coding experience, he has worked for avariety of business sectors, including AI, Finance, Travel, and Entertainment He is highlyinterested in Spring, AOP, and concurrency Currently, he is learning Cuba among otherrecent developments within the Java world He is well-regarded as an avid follower ofcoding best practices, refactoring patterns, and TDD He personally expresses his

admiration for the technical contributions from the legendary Martin Fowler

I would like to thank Jim Combes, my old team lead, for offering me the role as a JavaDeveloper and allowing my enthusiastic interest in Java to grow and develop over theyears

Tagir Valeev, PhD, is a research assistant in A.P Ershov Institute of Informatics Systems,

Novosibirsk, Russia, and a lead developer in Development Group Ltd., Novosibirsk,

Russia He is a Java programming professional, a contributor to the FindBugs project(Java static analysis tool), and the author of the StreamEx project, which enhances theJava 8 Stream API He is also officially an OpenJDK author, contributing enhancementsand bug fixes in Stream API implementation He answered many questions on

StackOverflow related to Stream API and Java concurrency

Big thanks to my wife Ekaterina and my son Artyom, for support and patience whichhelped me so much to finish the reviewing

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Nowadays, computer systems (and other related systems, such as tablets or smartphones)allow you to do several tasks simultaneously This can be possible because they haveconcurrent operating systems that control several tasks at the same time You can alsohave one application that executes several tasks (read a file, show a message, or read dataover a network) if you work with the concurrency API of your favorite programminglanguage Java includes a very powerful concurrency API that allows you to implementany kind of concurrency application with little effort This API increases the featuresprovided to programmers in every version Now, in Java 8, it has included the stream APIand new methods and classes to facilitate the implementation of concurrent applications.This book covers the most important elements of the Java concurrency API, showing youhow to use them in real-world applications These elements are as follows:

The executor framework, to control the execution of lots of task

The Phaser class, to execute tasks that can be divided into phases

The Fork/Join framework, to execute the tasks that solve a problem using the divideand conquer technique

The stream API, to process big sources of data

Concurrent data structures, to store the data in concurrent applications

Synchronization mechanisms, to organize concurrent tasks

However, it includes much more: a methodology to design concurrency applications,design patterns, tips and tricks to implement good concurrency applications, and tools andtechniques to test concurrency applications

Chapter 1, The First Step – Concurrency Design Principles, will teach you the design

principles of concurrency applications They will also learn the possible problems ofconcurrency applications and a methodology to design them followed by some designpatterns, tips, and tricks

Chapter 2, Managing Lots of Threads – Executors, will teach you the basic principles of

the executor framework This framework allows you to work with lots of threads withoutcreating or managing them You will implement the k-nearest neighbors algorithm and abasic client/server application

Chapter 3, Getting the Maximum from Executors, will teach you some advanced

characteristics of executors, including cancelation and scheduling of tasks to execute atask after a delay or every certain period of time You will implement an advanced

client/server application and a news reader

Chapter 4, Getting Data from the Tasks – The Callable and Future Interfaces, will teach

you how to work in an executor with tasks that return a result using the Callable andFuture interfaces You will implement a best-matching algorithm and an application tobuild an inverted index

Chapter 5, Running Tasks Divided into Phases – The Phaser class, will teach you how to

use the Phaser class to execute tasks that can be divided into phases in a concurrent way.You will implement a keyword extraction algorithm and a genetic algorithm

Chapter 6, Optimizing Divide and Conquer Solutions – The Fork/Join Framework, will

teach you how to use a special kind of executor optimized by those problems that can beresolved using the divide and conquer technique: the Fork/Join framework and its work-stealing algorithm You will implement the k-means clustering algorithm, a data filteringalgorithm, and the merge-sort algorithm

Chapter 7, Processing Massive Datasets with Parallel Streams – The Map and Reduce

Model, will teach you how to work with streams to process big datasets In this chapter,

you will learn how to implement map and reduce applications using the stream API andmuch more functions of streams You will implement a numerical summarization

Chapter 9, Diving into Concurrent Data Structures and Synchronization Utilities, will

teach you how to work with the most important concurrent data structures (data structuresthat can be used in concurrent applications without causing data race conditions) and all

Chapter 10, Integration of Fragments and Implementation of Alternatives, will teach you

how to implement a big application made by fragments of concurrent applications withtheir own concurrency techniques using shared memory or message passing You will alsolearn different implementation alternatives to the examples presented in the book

Chapter 11, Testing and Monitoring Concurrent Applications, teaches you how to obtain

information about the status of some of the Java concurrency API elements (thread, lock,executor, and so on) You will also learn how to monitor a concurrent application using theJava VisualVM application and how to test concurrent applications with the

MultithreadedTC library and the Java Pathfinder application

To follow this book, you need basic knowledge of the Java programming language Abasic knowledge of concurrency concepts is welcome too

If you are a Java developer who knows the basic principles of concurrent programmingbut you want to get an expert knowledge of the Java concurrency API to develop

optimized applications that takes advantage of all the hardware resources of computers,then this book is for you

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Chapter 1 The First Step – Concurrency Design Principles

Users of computer systems are always looking for better performance for their systems.They want to get higher quality videos, better video games, and faster network speed.Some years ago, processors gave better performance to users by increasing their speed.But now, processors don’t increase their speed Instead of this, they add more cores so thatthe operating system can execute more than one task at a time This is named

concurrency Concurrent programming includes all the tools and techniques to have

multiple tasks or processes running at the same time in a computer, communicating andsynchronizing between them without data loss or inconsistency In this chapter, we willcover the following topics:

First of all, let’s present the basic concepts of concurrency You must understand theseconcepts to follow the rest of the book

Concurrency and parallelism are very similar concepts Different authors give differentdefinitions to these concepts The most accepted definition talks about concurrency whenyou have more than one task in a single processor with a single core and the operatingsystem’s task scheduler quickly switches from one task to another, so it seems that all thetasks run simultaneously The same definition talks about parallelism when you have morethan one task that run simultaneously at the same time, in a different computer, processor,

or core inside a processor

Another definition talks about concurrency when you have more than one task (differenttasks) running simultaneously on your system One more definition discusses parallelismwhen you have different instances of the same task running simultaneously over differentparts of a dataset

The last definition that we include talks about parallelism when you have more than onetask that runs simultaneously in your system and talks about concurrency to explain thedifferent techniques and mechanisms programmers have to synchronize with the tasks andtheir access to shared resources

As you can see, both concepts are very similar and this similarity has increased with thedevelopment of multicore processors