You're reading from Java Concurrency and Parallelism Master advanced Java techniques for cloud-based applications through concurrency and parallelism

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781805129264

Length 496 pages

Edition 1st Edition

Languages

Java

Tools

Boost

Concepts

Application Development

Author (1):

Jay Wang

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Table of Contents (20) Chapters

Preface

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

2. Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape FREE CHAPTER

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

4. Chapter 3: Mastering Parallelism in Java

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

6. Chapter 5: Mastering Concurrency Patterns in Cloud Computing

7. Part 2: Java's Concurrency in Specialized Domains

8. Chapter 6: Java and Big Data – a Collaborative Odyssey

9. Chapter 7: Concurrency in Java for Machine Learning

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

11. Chapter 9: Serverless Computing and Java’s Concurrent Capabilities

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

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

14. Chapter 11: Advanced Java Concurrency Practices in Cloud Computing

15. Chapter 12: The Horizon Ahead

16. Index

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17. Other Books You May Enjoy

Appendix A: Setting up a Cloud-Native Java Environment

1. Appendix B: Resources and Further Reading

Summary

In this chapter, we embarked on an exhilarating journey, exploring the realm of big data and how Java’s prowess in concurrency and parallel processing empowers us to conquer its challenges. We began by unraveling the essence of big data, characterized by its immense volume, rapid velocity, and diverse variety – a domain where traditional tools often fall short.

As we ventured further, we discovered the power of Apache Hadoop and Apache Spark, two formidable allies in the world of distributed computing. These frameworks seamlessly integrate with Java, enabling us to harness the true potential of big data. We delved into the intricacies of this integration, learning how Java’s concurrency features optimize big data workloads, resulting in unparalleled scalability and efficiency.

Throughout our journey, we placed a strong emphasis on the DataFrame API, which has become the de facto standard for data processing in Spark. We explored how DataFrames provide...