Java Concurrency and Parallelism: Master advanced Java techniques for cloud-based applications through concurrency and parallelism

Java Concurrency and Parallelism

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Contributors

About the author

Jay Wang, a trailblazer in the IT sector, boasts a career spanning over two decades, marked by leadership roles at IT powerhouses such as Accenture, IBM, and a globally renowned telecommunications firm. An expert in Java since 2001 and cloud technologies since 2018, Jay excels in transitioning projects from monolithic to microservice architectures and cloud. As founder of Digitech Edge, he guides clients through AI-driven cloud solutions. His educational background includes an MS in management of IT from the University of Virginia and an MS in information systems from George Mason University.

About the reviewers

Artur Skowroński is the head of Java/Kotlin engineering at VirtusLab. He has been in the industry for ten years. During this time, he has had the opportunity to work in various roles, such as software engineer, tech lead, architect, and even technical product manager. This diverse experience enables him to approach problems from a holistic perspective. He is also an active member of the tech community, serving as the lead of the Krakow Kotlin User Group and the author of the JVM Weekly Newsletter.

Akshay Phadke started his journey as a software engineer after graduating with a master of science in electrical and computer engineering from Georgia Institute of Technology in 2016. He has worked on building data-intensive applications and experiences across different industries such as networking and telecommunications, enterprise software, and finance. His work has spanned multiple areas of software development such as big data, data and platform engineering, CI/CD and DevOps, developer productivity and tooling, infrastructure and observability, and full stack web development. His professional interests include open source software, distributed systems, and building and scaling products in a start-up environment.

Table of Contents

Preface

Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing

1

Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape

Technical requirements

The dual pillars of concurrency versus parallelism – a kitchen analogy

Defining concurrency

Defining parallelism

The analogy of a restaurant kitchen

When to use concurrency versus parallelism – a concise guide

Java and the cloud – a perfect alliance for cloud-native development

Exploring cloud service models and their impact on software development

Java’s transformation in the cloud – a story of innovation

Java – the cloud-native hero

Java’s cloud-focused upgrades – concurrency and beyond

Real-world examples of successful cloud-native Java applications

Modern challenges in cloud-native concurrency and Java’s weapons of choice

Wrangling distributed transactions in Java – beyond classic commits

Maintaining data consistency in cloud-native Java applications

Handling state in microservices architectures

Cloud database concurrency – Java’s dance moves for shared resources

Parallelism in big data processing frameworks

Cutting-edge tools for conquering cloud-native concurrency challenges

Conquering concurrency – best practices for robust cloud-native applications

Code examples illustrating best practices

Ensuring consistency – the bedrock of robust concurrency strategies

Summary

Exercise – exploring Java executors

Questions

2

Introduction to Java’s Concurrency Foundations: Threads, Processes, and Beyond

Technical requirements

Java’s kitchen of concurrency – unveiling threads and processes

What are threads and processes?

Similarities and differences

The life cycle of threads in Java

Activity – differentiating threads and processes in a practical scenario

The concurrency toolkit – java.util.concurrent

Threads and executors

Synchronization and coordination

Concurrent collections and atomic variables

Hands-on exercise – implementing a concurrent application using java.util.concurrent tools

Synchronization and locking mechanisms

The power of synchronization – protecting critical sections for thread-safe operations

Beyond the gatekeeper – exploring advanced locking techniques

Understanding and preventing deadlocks in multi-threaded applications

Hands-on activity – deadlock detection and resolution

Employing Future and Callable for result-bearing task execution

Safe data sharing between concurrent tasks

Immutable data

Thread local storage

Leveraging thread-safe collections to mitigate concurrency issues

Choosing between concurrent collections and atomic variables

Concurrent best practices for robust applications

Summary

Questions

3

Mastering Parallelism in Java

Technical requirements

Unleashing the parallel powerhouse – the Fork/Join framework

Demystifying Fork/Join – a culinary adventure in parallel programming

Beyond recursion – conquering complexities with dependencies

ForkJoinPool.invokeAll() – the maestro of intertwined tasks

Managing dependencies in the kitchen symphony – a recipe for efficiency

Fine-tuning the symphony of parallelism – a journey in performance optimization

The art of granularity control

Tuning parallelism levels

Best practices for a smooth performance

Streamlining parallelism in Java with parallel streams

Choosing your weapon – a parallel processing showdown in Java

Unlocking the power of big data with a custom Spliterator

Benefits and pitfalls of parallelism

Challenges and solutions in parallel processing

Evaluating parallelism in software design – balancing performance and complexity

Summary

Questions

4

Java Concurrency Utilities and Testing in the Cloud Era

Technical requirements

Uploading your JAR file to AWS Lambda

Introduction to Java concurrency tools – empowering cloud computing

Real-world example – building a scalable application on AWS

Taming the threads – conquering the cloud with the Executor framework

The symphony of cloud integration and adaptation

Real-world examples of thread pooling and task scheduling in cloud architectures

Example 1 – keeping data fresh with scheduled tasks

Example 2 – adapting to the cloud’s dynamics

Utilizing Java’s concurrent collections in distributed systems and microservices architectures

Navigating through data with ConcurrentHashMap

Processing events with ConcurrentLinkedQueue

Best practices for using Java’s concurrent collections

Advanced locking strategies for tackling cloud concurrency

Revisiting lock mechanisms with a cloud perspective

Advanced concurrency management for cloud workflows

Sophisticated Java synchronizers for cloud applications

Utilizing tools for diagnosing concurrency problems

Thread dumps – the developer’s snapshot

Lock monitors – the guardians of synchronization

The quest for clarity – advanced profiling techniques

Weaving the web – integrating profiling tools into CI/CD pipelines

Service mesh and APM – your cloud performance powerhouse

Incorporating concurrency frameworks

Mastering concurrency in cloud-based Java applications – testing and debugging tips

Summary

Questions

5

Mastering Concurrency Patterns in Cloud Computing

Technical requirements

Core patterns for robust cloud foundations

The Leader-Follower pattern

The Circuit Breaker pattern – building resilience in cloud applications

The Bulkhead pattern – enhancing cloud application fault tolerance

Java concurrency patterns for asynchronous operations and distributed communications

The Producer-Consumer pattern – streamlining data flow

The Scatter-Gather pattern: distributed processing powerhouse

The Disruptor pattern – streamlined messaging for low-latency applications

Combining concurrency patterns for enhanced resilience and performance

Integrating the Circuit Breaker and Producer-Consumer patterns

Integrating Bulkhead with Scatter-Gather for enhanced fault tolerance

Blending concurrency patterns – a recipe for high-performance cloud applications

Blending the Circuit Breaker and Bulkhead patterns

Combining Scatter-Gather with the Actor model

Merging Producer-Consumer with the Disruptor pattern

Synergizing event sourcing with CQRS

Summary

Questions

Part 2: Java's Concurrency in Specialized Domains

6

Java and Big Data – a Collaborative Odyssey

Technical requirements

The big data landscape – the evolution and need for concurrent processing

Navigating the big data landscape

Concurrency to the rescue

Hadoop – the foundation for distributed data processing

Hadoop distributed file system

MapReduce – the processing framework

Java and Hadoop – a perfect match

Why Java? A perfect match for Hadoop development

MapReduce in action

Beyond the basics – advanced Hadoop concepts for Java developers and architects

Yet another resource negotiator

HBase

Integration with the Java ecosystem

Spark versus Hadoop – choosing the right framework for the job

Hadoop and Spark equivalents in major cloud platforms

Real-world Java and big data in action

Use case 1 – log analysis with Spark

Use case 2 – a recommendation engine

Use case 3 – real-time fraud detection

Summary

Questions

7

Concurrency in Java for Machine Learning

Technical requirements

An overview of ML computational demands and Java concurrency alignment

The intersection of Java concurrency and ML demands

Parallel processing – the key to efficient ML workflows

Handling big data with ease

An overview of key ML techniques

Case studies – real-world applications of Java concurrency in ML

Java’s tools for parallel processing in ML workflows

DL4J – pioneering neural networks in Java

Java thread pools for concurrent data processing

Achieving scalable ML deployments using Java’s concurrency APIs

Best practices for thread management and reducing synchronization overhead

Generative AI and Java – a new frontier

Leveraging Java’s concurrency model for efficient generative AI model training and inference

Summary

Questions

8

Microservices in the Cloud and Java’s Concurrency

Technical requirements

Core principles of microservices – architectural benefits in cloud platforms

Foundational concepts – microservices architecture and its benefits in the cloud

Real-world examples – Netflix’s evolution and Amazon’s flexibility

Essential Java concurrency tools for microservice management

Concurrency tools – an exploration of Java’s concurrency tools that are tailored for microservices

Challenges and solutions in microservices concurrency

Bottlenecks – diagnosing potential challenges in concurrent microservices architectures

Consistency – ensuring data consistency and smooth inter-service communication

Resilience – achieving system resilience and fault tolerance

Practical design and implementation – building effective Java microservices

Strategic best practices – deploying and scaling microservices

Advanced concurrency patterns – enhancing microservice resilience and performance

Data management patterns

Summary

Questions

9

Serverless Computing and Java’s Concurrent Capabilities

Technical requirements

Fundamentals of serverless computing in java

Core concepts of serverless computing

Advantages of and scenarios for using serverless computing

Drawbacks and trade-offs of serverless computing

When to use serverless?

Adapting Java’s concurrency model to serverless environments

Designing efficient Java serverless applications

Introducing serverless frameworks and services – AWS SAM, Azure Functions Core Tools, Google Cloud Functions, and Oracle Functions

AWS Serverless Application Model

Azure Functions Core Tools

Google Cloud Functions

Oracle Functions

Industry examples – Java serverless functions with a focus on concurrency

Airbnb – optimizing property listings with serverless solutions

LinkedIn – enhancing data processing with serverless architectures

Expedia – streamlining travel booking with serverless solutions

Building with serverless frameworks – a practical approach

Using AWS SAM to define and deploy a serverless application

Summary

Questions

Part 3: Mastering Concurrency in the Cloud – The Final Frontier

10

Synchronizing Java’s Concurrency with Cloud Auto-Scaling Dynamics

Technical requirements

Fundamentals of cloud auto-scaling – mechanisms and motivations

Definition and core concepts

Advantages of cloud auto-scaling

Triggers and conditions for auto-scaling

A guide to setting memory utilization triggers for auto-scaling

Java’s concurrency models – alignment with scaling strategies

Optimizing Java applications for cloud scalability – best practices

Code example – best practices in optimizing a Java application for auto-scaling with AWS services and Docker

Monitoring tools and techniques for Java applications

Real-world case studies and examples

Practical application – building scalable Java-based solutions for real-time analytics and event-driven auto-scaling

Advanced topics

Predictive auto-scaling using ML algorithms

Integration with cloud-native tools and services

Summary

Questions

11

Advanced Java Concurrency Practices in Cloud Computing

Technical requirements

Enhancing cloud-specific redundancies and failovers in Java applications

Leveraging Java libraries and frameworks

Writing correct test scenarios for failover and advanced mechanisms

Practical exercise – resilient cloud-native Java application

GPU acceleration in Java – leveraging CUDA, OpenCL, and native libraries

Fundamentals of GPU computing

CUDA and OpenCL overview – differences and uses in Java applications

TornadoVM – GraalVM-based GPU Acceleration

Practical exercise – GPU-accelerated matrix multiplication in Java

Specialized monitoring for Java concurrency in the cloud

Challenges in monitoring

Monitoring tools and techniques

Summary

Questions

12

The Horizon Ahead

Technical requirements

Future trends in cloud computing and Java’s role

Emerging trends in cloud computing – serverless Java beyond function as a service

Edge computing and Java

Java’s role in edge computing architectures

Frameworks and tools for Java-based edge applications

AI and ML integration

Java’s position in cloud-based AI/ML workflows

Integration of Java with cloud AI services

Use case – serverless AI image analysis with AWS Lambda and Fargate

Emerging concurrency and parallel processing tools in Java

Introduction to Project Loom – virtual threads for efficient concurrency

Code example – implementing a high-concurrency microservice using Project Loom and Akka for the AWS cloud environment

Preparing for the next wave of cloud innovations

Quantum computing

Summary

Questions

Appendix A

Setting up a Cloud-Native Java Environment

General approach – build and package Java applications

Useful links for further information on AWS

Microsoft Azure

Google Cloud Platform

Setting up the Google Cloud Environment

Deploy your Java application to Google Cloud

GKE for containerized applications

Google Cloud Functions for serverless Java functions

Useful links for further information

Appendix B

Resources and Further Reading

Recommended books, articles, and online courses

Chapters 1–3

Chapters 4–6

Chapters 7–9

Chapters 10–12

Answers to the end-of-chapter multiple-choice questions

Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape

Chapter 2: Introduction to Java’s Concurrency Foundations: Threads, Processes, and Beyond

Chapter 3: Mastering Parallelism in Java

Chapter 4: Java Concurrency Utilities and Testing in the Cloud Era

Chapter 5: Mastering Concurrency Patterns in Cloud Computing

Chapter 6: Java and Big Data – a Collaborative Odyssey

Chapter 7: Concurrency in Java for Machine Learning

Chapter 8: Microservices in the Cloud and Java’s Concurrency

Chapter 9: Serverless Computing and Java’s Concurrent Capabilities

Chapter 10: Synchronizing Java’s Concurrency with Cloud Auto-Scaling Dynamics

Chapter 11: Advanced Java Concurrency Practices in Cloud Computing

Chapter 12: The Horizon Ahead

Index

Other Books You May Enjoy

Preface

Welcome to Java Concurrency and Parallelism, which explores Java’s concurrency and parallelism within the context of modern cloud computing. As technology evolves, mastering these concepts is crucial for developing robust, scalable, and efficient cloud-native applications.

This book is structured into three distinct yet interconnected parts, each aimed at providing in-depth knowledge and practical skills to enhance your proficiency in Java concurrency. Part 1 lays the groundwork by introducing the fundamental principles of concurrency and parallelism, essential for any modern software development. This section covers the basics of Java concurrency, including threads and processes, and delves into advanced topics such as the java.util.concurrent package, the Fork/Join framework, and parallel streams. It also explores practical implementation strategies and concurrency patterns tailored for cloud environments, equipping you with the foundational tools needed to tackle the unique challenges of cloud-native Java applications.

Part 2 builds on the foundational knowledge from Part 1, focusing on how Java’s concurrency capabilities address complex challenges across various specialized domains. This part examines Java’s role in big data, machine learning, microservices, and serverless computing. Through practical examples, code snippets, and real-world use cases, it demonstrates how to harness Java’s concurrency features to build scalable, high-performance applications in these cutting-edge fields.

Part 3 synthesizes the knowledge from the previous sections, applying it to the advanced realm of cloud computing. It covers synchronization with cloud auto-scaling, and advanced concurrency practices and explores emerging trends such as edge computing and quantum computing. This section prepares you to leverage Java’s concurrency capabilities to their fullest potential, enabling you to stay at the forefront of technological advancements in cloud computing.

Who this book is for

This book is intended for Java developers, software engineers, and cloud computing professionals who aim to deepen their understanding of concurrent programming and parallel processing in Java. A foundational knowledge of Java and a basic familiarity with cloud computing concepts will be beneficial. Whether you are an aspiring developer or an experienced professional, this book will provide you with the knowledge and skills to excel in developing cloud-native Java applications.

What this book covers

Chapter 1, Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape, introduces the fundamental principles and distinctions between concurrency and parallelism in Java. It sets the stage for understanding how these concepts are critical in the development of modern cloud-native applications.

Chapter 2, Introduction to Java’s Concurrency Foundations: Threads, Processes, and Beyond, explores the basics of Java concurrency, including threads and processes. This chapter provides a solid introduction to the building blocks of concurrent programming in Java.

Chapter 3, Mastering Parallelism in Java, delves into tools and frameworks that enhance Java’s parallel processing capabilities. You will learn how to leverage multi-core processors and distributed systems to improve performance.

Chapter 4, Java Concurrency Utilities and Testing in the Cloud Era, focuses on practical implementation strategies and testing for cloud-based applications. You will discover how to design, implement, and validate concurrent systems that can thrive in distributed environments.

Chapter 5, Mastering Concurrency Patterns in Cloud Computing, provides a toolkit of concurrency patterns for cloud environments. These patterns serve as essential solutions for common challenges in cloud-native Java applications.

Chapter 6, Java and Big Data – a Collaborative Odyssey, examines Java’s role in big data and its concurrency tools. You will learn how Java efficiently processes massive datasets through parallel and distributed computing.

Chapter 7, Concurrency in Java for Machine Learning, demonstrates how Java’s concurrency features accelerate machine learning. This chapter shows how to build scalable, high-performance machine learning applications using multi-threading and parallel processing.

Chapter 8, Microservices in the Cloud and Java’s Concurrency, explores Java’s concurrency capabilities in microservices architectures. You will understand the strategies for managing concurrent operations in distributed environments to create resilient, scalable microservices.

Chapter 9, Serverless Computing and Java’s Concurrent Capabilities, illustrates Java’s adaptation to serverless architectures. You will discover how to build scalable, cost-effective solutions for varying workloads using Java’s concurrency models.

Chapter 10, Synchronizing Java’s Concurrency with Cloud Auto-Scaling Dynamics, covers synchronization with cloud auto-scaling. You will learn how to build applications that seamlessly adapt to the elastic nature of cloud environments.

Chapter 11, Advanced Java Concurrency Practices in Cloud Computing, delves into advanced concurrency techniques for the cloud. This chapter covers cutting-edge techniques such as GPU acceleration and implementing robust redundancy mechanisms.

Chapter 12, The Horizon Ahead, looks at emerging trends and future innovations in cloud computing. You will explore serverless Java beyond function as a service, Java’s role in edge computing, and its integration with AI and machine learning in cloud ecosystems.

To get the most out of this book

You should have a few prerequisite skills or a certain level of knowledge in the following:

Basic knowledge of Java programming

Basic knowledge of cloud computing, especially the AWS environment, as most code examples for cloud applications are in AWS

Familiarity with Java frameworks such as Spring Cloud

Experience with build tools such as Maven

Basic understanding of containerization using Docker

Awareness of Quarkus for building cloud-native applications

We hope this book provides you with the insights and tools needed to master Java concurrency in the cloud era, transforming you from proficient developers into experts ready to tackle the challenges and opportunities of tomorrow’s technological landscape.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from https://github.com/PacktPublishing/Java-Concurrency-and-Parallelism. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Important note

Due to a recent tech update and page limit constraints, many code snippets in this book are shortened versions. They are used in chapters for demonstration purposes only. Some code has also been revised based on the update. For the most current, complete, and functional code, please refer to the book’s accompanying GitHub repository. The repository should be considered the primary and preferred source for all code examples.

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: After submitting the tasks, we use task1.get() and task2.get() to wait for both tasks to complete. The get() method blocks until the task is finished and returns the result.

A block of code is set as follows:

import java.util.stream.IntStream;

public class ParallelKitchen {

  public static void main(String[] args) {

    IntStream.range(0, 10).parallel().forEach(i -> {

        System.out.println(Cooking dish # + i + in parallel...);

        // Simulate task

        try {

            Thread.sleep(600);

        } catch (InterruptedException e) {

            Thread.currentThread().interrupt();

        }

    });

  }

}

Any command-line input or output is written as follows:

aws cloudformation describe-stacks --stack-name

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: Authentication: Verify user credentials and issue secure tokens (e.g., JWTs) for access.

Tips or important notes

Appear like this.

Get in touch

Feedback from our readers is always welcome.

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Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing

The first part of the book lays the groundwork for understanding and implementing concurrency and parallelism in Java, focusing on cloud computing environments. It introduces key concepts, tools, and patterns that form the backbone of efficient and scalable Java applications in the cloud.

This part includes the following chapters:

Chapter 1, Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape

Chapter 2, Introduction to Java's Concurrency Foundations: Threads, Processes, and Beyond

Chapter 3, Mastering Parallelism in Java

Chapter 4, Java Concurrency Utilities and Testing in the Cloud Era

Chapter 5, Mastering Concurrency Patterns in Cloud Computing

1

Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape

Welcome to an exciting journey into the world of Java’s concurrency and parallelism paradigms, which are crucial for developing efficient and scalable cloud-native applications. In this introductory chapter, we’ll establish a solid foundation by exploring the fundamental concepts of concurrency and parallelism, as well as their significance in contemporary software design. Through practical examples and hands-on practice problems, you’ll gain a deep understanding of these principles and their application in real-world scenarios.

As we progress, we’ll delve into the transformative impact of cloud computing on software development and its synergistic relationship with Java. You’ll learn how to leverage Java’s powerful features and libraries to tackle the challenges of concurrent programming in cloud-native environments. We’ll also explore case studies from industry leaders such as Netflix, LinkedIn, X (formerly Twitter), and Alibaba, showcasing how they have successfully harnessed Java’s concurrency and parallelism capabilities to build robust and high-performance applications.

Throughout this chapter, you’ll gain a comprehensive understanding of the software paradigms that shape the cloud era and Java’s pivotal role in this landscape. By mastering the concepts and techniques presented here, you’ll be well-equipped to design and implement concurrent systems that scale seamlessly in the cloud.

So, let’s embark on this exciting journey together and unlock the full potential of concurrency and parallelism in Java cloud-native development. Get ready to acquire the knowledge and skills necessary to build innovative, efficient, and future-proof software solutions.

Technical requirements

Here is the minimal Java JRE/JDK setup guide for macOS, Windows, and Linux. You can follow these steps:

Download the desired version of Java JRE or JDK from the official Oracle website: https://www.oracle.com/java/technologies/javase-downloads.html.

Choose the appropriate version and operating system to download.

Install Java on your system:

macOS:

Double-click the downloaded .dmg file.

Follow the installation wizard and accept the license agreement.

Drag and drop the Java icon into the Applications folder.

Windows:

Run the downloaded executable (.exe) file.

Follow the installation wizard and accept the license agreement.

Choose the installation directory and complete the installation.

Linux:

Extract the downloaded .tar.gz archive to a directory of your choice.

For system-wide installation, move the extracted directory to /usr/local/java.

Set the environment variables:

macOS and Linux:

Open the terminal.

Edit the ~/.bash_profile or ~/.bashrc file (depending on your shell).

Add the following lines (replace with the actual path) in the file: export JAVA_HOME= and export PATH=$JAVA_HOME/bin:$PATH.

Save the file and restart the terminal.

Windows:

Open the Start menu and search for Environment Variables.

Click on Edit the system environment variables.

Click on the Environment Variables button.

Under System Variables, click New.

Set the variable name as JAVA_HOME and the value as the JDK installation directory.

Find the Path variable, select it, and click Edit.

Add %JAVA_HOME%\bin to the Path variable.

Click OK to save the changes.

Verify the installation:

Open a new terminal or command prompt.

Run the following command: java -version.

It should display the installed Java version.

For more detailed installation instructions and troubleshooting, you can refer to the official Oracle documentation:

macOS: https://docs.oracle.com/en/java/javase/17/install/installation-jdk-macos.html

Windows: https://docs.oracle.com/en/java/javase/17/install/installation-jdk-microsoft-windows-platforms.html

Linux: https://docs.oracle.com/en/java/javase/17/install/installation-jdk-linux-platforms.html

Please note that the exact steps may vary slightly depending on the specific Java version and operating system version you are using.

You need to install a Java Integrated Development Environment (IDE) on your laptop. Here are a few Java IDEs and their download URLs:

IntelliJ IDEA

Download URL:https://www.jetbrains.com/idea/download/

Pricing: Free Community Edition with limited features, Ultimate Edition with full features requires a subscription

Eclipse IDE:

Download URL: https://www.eclipse.org/downloads/

Pricing: Free and open source

Apache NetBeans:

Download URL: https://netbeans.apache.org/front/main/download/index.html

Pricing: Free and open source

Visual Studio Code (VS Code):

Download URL: https://code.visualstudio.com/download

Pricing: Free and open source

VS Code offers a lightweight and customizable alternative to the other options on this list. It’s a great choice for developers who prefer a less resource-intensive IDE and want the flexibility to install extensions that are tailored to their specific needs. However, it may not have all the features out of the box compared to the more established Java IDEs.

Further, the code in this chapter can be found on GitHub: https://github.com/PacktPublishing/Java-Concurrency-and-Parallelism.

Important note

Due to a recent tech update and page limit constraints, many code snippets in this book are shortened versions. They are used in chapters for demonstration purposes only. Some code has also been revised based on the update. For the most current, complete, and functional code, please refer to the book’s accompanying GitHub repository. The repository should be considered the primary and preferred source for all code examples.

The dual pillars of concurrency versus parallelism – a kitchen analogy

Welcome to the kitchen of Java concurrency and parallelism! Here, we’ll whisk you through a culinary journey, unveiling the art of multitasking and high-speed cooking in programming. Imagine juggling different tasks like a master chef – that’s concurrency. Then, picture multiple chefs cooking in harmony for a grand feast – that’s parallelism. Get ready to spice up your Java applications with these essential skills, from handling user interactions to crunching massive data. Bon appétit to the world of efficient and responsive Java cooking!

Defining concurrency

In Java, concurrency allows a program to manage multiple tasks such that they seem to run simultaneously, enhancing performance even on single-core systems. A core refers to a processing unit within a computer’s CPU that is capable of executing programming instructions. While true parallel execution requires multiple cores, with each core handling a different task at the same time, Java’s concurrency mechanisms can create the illusion of parallelism by efficiently scheduling and executing tasks in a way that maximizes the use of available resources. They can do this on a single- or multi-core system. This approach enables Java programs to achieve high levels of efficiency and responsiveness.

Defining parallelism

Parallelism is the simultaneous execution of multiple tasks or calculations, typically on multi-core systems. In parallelism, each core handles a separate task concurrently, leveraging the principle of dividing large problems into smaller, independently solvable subtasks. This approach harnesses the power of multiple cores to achieve faster execution and efficient resource utilization. By assigning tasks to different cores, parallelism enables true simultaneous processing, as opposed to concurrency, which creates the illusion of simultaneous execution through time-sharing techniques. Parallelism requires hardware support in the form of multiple cores or processors to achieve optimal performance gains.

The analogy of a restaurant kitchen

Imagine a restaurant kitchen as a metaphor for a Java application. From this perspective, we will understand the role of concurrency and parallelism in Java applications.

First, we’ll consider concurrency. In a concurrent kitchen, there’s one chef (the main thread) who can handle multiple tasks such as chopping vegetables, grilling, and plating. They do one task at a time, switching between tasks (context switching). This is similar to a single-threaded Java application managing multiple tasks asynchronously.

Next, we come to parallelism. In a parallel kitchen, there are multiple chefs (multiple threads) working simultaneously, each handling a different task. This is like a Java application utilizing multi-threading to process different tasks concurrently.

The following is a Java code example for concurrency:

import java.util.concurrent.ExecutionExcept ion;

import java.util.concurrent.ExecutorService;

import java.util.concurrent.Executors;

import java.util.concurrent.Future;

public class ConcurrentKitchen {

  public static void main(String[] args) {

    ExecutorService executor = Executors.newFixedThreadPool(2);

    Future task1 = executor.submit(() -> {

        System.out.println(Chopping vegetables...);

        // Simulate task

        try {

            Thread.sleep(600);

        } catch (InterruptedException e) {

            Thread.currentThread().interrupt();

        }

    });

    Future task2 = executor.submit(() -> {

        System.out.println(Grilling meat...);

        // Simulate task

        try {

            Thread.sleep(600);

        } catch (InterruptedException e) {

            Thread.currentThread().interrupt();

        }

    });

    // Wait for both tasks to complete

    try {

        task1.get();

        task2.get();

    } catch (InterruptedException | ExecutionException e) {

        e.printStackTrace();

    }

    executor.shutdown();

  }

}

Here is an explanation of the preceding code example:

We create a fixed thread pool with two threads using Executors.newFixedThreadPool(2). This allows the tasks to be executed concurrently by utilizing multiple threads.

We submit two tasks to the executor using executor.submit(). These tasks are analogous to chopping vegetables and grilling meat.

After submitting the tasks, we use task1.get() and task2.get() to wait for both tasks to complete. The get() method blocks until the task is finished and returns the result (in this case, there is no result since the tasks have a void return type).

Finally, we shut down the executor using executor.shutdown() to release the resources.

Next, we will look at a Java code example for parallelism:

import java.util.stream.IntStream;

public class ParallelKitchen {

  public static void main(String[] args) {

    IntStream.range(0, 10).parallel().forEach(i -> {

        System.out.println(Cooking dish # + i + in parallel...);

        // Simulate task

        try {

            Thread.sleep(600);

        } catch (InterruptedException e) {

            Thread.currentThread().interrupt();

        }

    });

  }

}

Explanation of the preceding code example is as follows.

This Java code demonstrates parallel processing using IntStream and the parallel method, which is ideal for simulating tasks in a Parallel Kitchen. The main method utilizes an integer stream to create a range of 0 to 9, representing a range of different dishes.

By invoking .parallel() on IntStream, the code ensures that the processing of these dishes happens in parallel, leveraging multiple threads. Each iteration simulates cooking a dish, identified by the index, i, and is executed concurrently with other iterations.

The Thread.sleep(600) inside the forEach lambda expression mimics the time taken to cook each dish. The sleep duration is set for simulation purposes and is not indicative of actual cooking times.

In the case of InterruptedException, the thread’s interrupt flag is set again with Thread.currentThread().interrupt(), adhering to best practices in handling interruptions in Java.

Having seen the two examples, let us understand the key differences between concurrency and parallelism:

Focus: Concurrency is about managing multiple tasks, while parallelism is about executing tasks simultaneously for performance gains

Execution: Concurrency can work on single-core processors, but parallelism benefits from multi-core systems

Both concurrency and parallelism play crucial roles in building efficient and responsive Java applications. The right approach for you depends on the specific needs of your program and the available hardware resources.

When to use concurrency versus parallelism – a concise guide

Armed with the strengths of concurrency and parallelism, let’s dive into picking the perfect tool. We’ll weigh up complexity, environment, and task nature to ensure that your Java applications sing. Buckle up, master chefs, as we unlock optimal performance and efficiency!

Concurrency

Concurrency is essential for effectively managing multiple operations simultaneously, particularly in three key areas:

Simultaneous task management: This is ideal for efficiently handling user requests and Input/Output (I/O) operations, especially with the use of non-blocking I/O. This technique allows programs to execute other tasks without waiting for data transfer to complete, significantly enhancing responsiveness and throughput.

Resource sharing: Through synchronization tools such as locks, concurrency ensures safe access to shared resources among multiple threads, preserving data integrity and preventing conflicts.

Scalability: Scalability is crucial in developing systems capable of expansion such as microservices in cloud environments. Concurrency facilitates the execution of numerous tasks across different servers or processes, improving the system’s overall performance and capacity to handle growth.

Let’s look at some examples to illustrate each of the three key areas where concurrency is essential.

The first example is related to simultaneous task management. Here is a web server