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Java Concurrency and Parallelism

Java Concurrency and Parallelism

By : Jay Wang
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Java Concurrency and Parallelism

Java Concurrency and Parallelism

5 (1)
By: Jay Wang

Overview of this book

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.
Table of Contents (20 chapters)
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Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing
7
Part 2: Java's Concurrency in Specialized Domains
12
Part 3: Mastering Concurrency in the Cloud – The Final Frontier

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

Moving beyond theory, let’s dive into real-world scenarios where Java’s concurrency tools shine in cloud architectures. These examples showcase how to optimize resource usage and ensure application responsiveness under varying loads.

Example 1 – keeping data fresh with scheduled tasks

Imagine a cloud-based application that needs to regularly crunch data from various sources. Scheduled tasks are your secret weapon, ensuring data is always up to date, even during peak hours.

Objective: Process data from multiple sources periodically, scaling with data volume.

Environment: A distributed system gathering data from APIs for analysis.

Here is the code snippet:

public class DataAggregator {
    private final ScheduledExecutorService scheduler = Executors.    newScheduledThreadPool(5);
    public DataAggregator(...

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