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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

Service mesh and APM – your cloud performance powerhouse

Imagine your cloud application as a bustling marketplace, with microservices such as vendors conducting transactions. Without a conductor, things get chaotic. Service mesh, such as Istio and Linkerd, ensures each microservice plays its part flawlessly:

  • Transparent observability: See how data flows between services, identify bottlenecks, and debug issues, all without modifying your code
  • Traffic management: Route requests efficiently, avoiding overloads and ensuring smooth performance even during peak traffic
  • Consistent policy enforcement: Set rules (e.g., retry policies, rate limits) globally for all services, simplifying management and guaranteeing predictable behavior

Now, imagine a skilled musician analyzing the marketplace soundscape. That’s what APM solutions such as Dynatrace, New Relic, and Elastic APM do:

  • Observability beyond monitoring: Go beyond basic metrics to correlate...

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