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Beschreibung

If you’re a software developer, architect, or systems engineer, exploring Java’s concurrency utilities and synchronization in the cloud, this book is an essential resource. Tech visionary Jay Wang, with over three decades of experience transforming industry giants, brings unparalleled expertise to guide you through Java’s concurrency and parallel processing in cloud computing.
This comprehensive book starts by establishing the foundational concepts of concurrency and parallelism, vital for cloud-native development, and gives you a complete overview, highlighting challenges and best practices. Wang expertly demonstrates Java’s role in big data, machine learning, microservices, and serverless computing, shedding light on how Java’s tools are effectively utilized in these domains. Complete with practical examples and insights, this book bridges theory with real-world applications, ensuring a holistic understanding of Java in cloud-based scenarios. You’ll navigate advanced topics, such as synchronizing Java’s concurrency with cloud auto-scaling and GPU computing, and be equipped with the skills and foresight to tackle upcoming trends in cloud technology.
This book serves as your roadmap to innovation and excellence in Java cloud applications, giving you in-depth knowledge and hands-on practice for mastering Java in the cloud era.

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Veröffentlichungsjahr: 2024

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Java Concurrency and Parallelism

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

Jay Wang

Java Concurrency and Parallelism

Copyright © 2024 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

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Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

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First published: August 2024

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Published by Packt Publishing Ltd.

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ISBN 978-1-80512-926-4

www.packtpub.com

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 programmingBasic knowledge of cloud computing, especially the AWS environment, as most code examples for cloud applications are in AWSFamiliarity with Java frameworks such as Spring CloudExperience with build tools such as MavenBasic understanding of containerization using DockerAwareness 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.

Software/hardware covered in the book

Operating system requirements

Java, AWS account, Spring cloud, Docker, and Quarkus

Windows, macOS, or Linux

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 <your-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.

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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 LandscapeChapter 2, Introduction to Java's Concurrency Foundations: Threads, Processes, and BeyondChapter 3, Mastering Parallelism in JavaChapter 4, Java Concurrency Utilities and Testing in the Cloud EraChapter 5, Mastering Concurrency Patterns in Cloud Computing