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

Hadoop – the foundation for distributed data processing

As a Java developer, you’re in the perfect position to harness this power. Hadoop is built with Java, offering a rich set of tools and APIs to craft scalable big data solutions. Let’s dive into the core components of Hadoop’s HDFS and MapReduce. Here’s a detailed explanation of each component.

Hadoop distributed file system

Hadoop distributed file system or HDFS is the primary storage system used by Hadoop applications. It is designed to store massive amounts of data across multiple commodity hardware nodes, providing scalability and fault tolerance. The key characteristics of HDFS include the following:

Scaling out, not up: HDFS splits large files into smaller blocks (typically, 128 MB) and distributes them across multiple nodes in a cluster. This allows for parallel processing and enables a system to handle files that are larger than the capacity of a single node.
Resilience...