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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

Unlocking the power of big data with a custom Spliterator

Java’s Splittable Iterator (Spliterator) interface offers a powerful tool for dividing data into smaller pieces for parallel processing. But for large datasets, such as those found on cloud platforms such as Amazon Web Services (AWS), a custom Spliterator can be a game-changer.

For example, imagine a massive bucket of files in AWS Simple Storage Service (S3). A custom Spliterator designed specifically for this task can intelligently chunk the data into optimal sizes, considering factors such as file types and access patterns. This allows you to distribute tasks across CPU cores more effectively, leading to significant performance boosts and reduced resource utilization.

Now, imagine you have lots of files in an AWS S3 bucket and want to process them at the same time using Java Streams. Here’s how you could set up a custom Spliterator for these AWS S3 objects:

// Assume s3Client is an initialized AmazonS3...

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