Java Concurrency and Parallelism
Master advanced Java techniques for cloud-based applications through concurrency and parallelism
1. Auflage
Erschienen am 18. März 2025
496 Seiten
978-1-80512-455-9 (ISBN)
Beschreibung
No detailed description available for "Java Concurrency and Parallelism".
Alle Preise
Weitere Details
Weitere Ausgaben
Andere Ausgaben
Jay Wang
Java Concurrency and Parallelism
Master advanced Java techniques for cloud-based applications through concurrency and parallelism
Buch
08/2024
Packt Publishing
50,60 €
Versand in 3-4 Wochen
Person
Wang Jay :
Jay Wang, a trailblazer in the IT sector, boasts a career spanning over two decades, marked by leadership roles at IT powerhouses such as Accenture, IBM, and a globally renowned telecommunications firm. An expert in Java since 2001 and cloud technologies since 2018, Jay excels in transitioning projects from monolithic to microservice architectures and cloud. As founder of Digitech Edge, he guides clients through AI-driven cloud solutions. His educational background includes an MS in management of IT from the University of Virginia and an MS in information systems from George Mason University.
Jay Wang, a trailblazer in the IT sector, boasts a career spanning over two decades, marked by leadership roles at IT powerhouses such as Accenture, IBM, and a globally renowned telecommunications firm. An expert in Java since 2001 and cloud technologies since 2018, Jay excels in transitioning projects from monolithic to microservice architectures and cloud. As founder of Digitech Edge, he guides clients through AI-driven cloud solutions. His educational background includes an MS in management of IT from the University of Virginia and an MS in information systems from George Mason University.
Inhalt
- Cover
- Copyright
- Contributors
- Table of Contents
- Preface
- Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing
- Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape
- Technical requirements
- The dual pillars of concurrency versus parallelism - a kitchen analogy
- When to use concurrency versus parallelism - a concise guide
- Java and the cloud - a perfect alliance for cloud-native development
- Exploring cloud service models and their impact on software development
- Java's transformation in the cloud - a story of innovation
- Java - the cloud-native hero
- Java's cloud-focused upgrades - concurrency and beyond
- Real-world examples of successful cloud-native Java applications
- Modern challenges in cloud-native concurrency and Java's weapons of choice
- Wrangling distributed transactions in Java - beyond classic commits
- Maintaining data consistency in cloud-native Java applications
- Handling state in microservices architectures
- Cloud database concurrency - Java's dance moves for shared resources
- Parallelism in big data processing frameworks
- Cutting-edge tools for conquering cloud-native concurrency challenges
- Conquering concurrency - best practices for robust cloud-native applications
- Code examples illustrating best practices
- Ensuring consistency - the bedrock of robust concurrency strategies
- Summary
- Exercise - exploring Java executors
- Questions
- Chapter 2: Introduction to Java's Concurrency Foundations: Threads, Processes, and Beyond
- Technical requirements
- Java's kitchen of concurrency - unveiling threads and processes
- What are threads and processes?
- Similarities and differences
- The life cycle of threads in Java
- Activity - differentiating threads and processes in a practical scenario
- The concurrency toolkit - java.util.concurrent
- Threads and executors
- Synchronization and coordination
- Concurrent collections and atomic variables
- Hands-on exercise - implementing a concurrent application using java.util.concurrent tools
- Synchronization and locking mechanisms
- The power of synchronization - protecting critical sections for thread-safe operations
- Beyond the gatekeeper - exploring advanced locking techniques
- Understanding and preventing deadlocks in multi-threaded applications
- Hands-on activity - deadlock detection and resolution
- Employing Future and Callable for result-bearing task execution
- Safe data sharing between concurrent tasks
- Immutable data
- Thread local storage
- Leveraging thread-safe collections to mitigate concurrency issues
- Choosing between concurrent collections and atomic variables
- Concurrent best practices for robust applications
- Summary
- Questions
- Chapter 3: Mastering Parallelism in Java
- Technical requirements
- Unleashing the parallel powerhouse - the Fork/Join framework
- Demystifying Fork/Join - a culinary adventure in parallel programming
- Beyond recursion - conquering complexities with dependencies
- ForkJoinPool.invokeAll() - the maestro of intertwined tasks
- Managing dependencies in the kitchen symphony - a recipe for efficiency
- Fine-tuning the symphony of parallelism - a journey in performance optimization
- The art of granularity control
- Tuning parallelism levels
- Best practices for a smooth performance
- Streamlining parallelism in Java with parallel streams
- Choosing your weapon - a parallel processing showdown in Java
- Unlocking the power of big data with a custom Spliterator
- Benefits and pitfalls of parallelism
- Challenges and solutions in parallel processing
- Evaluating parallelism in software design - balancing performance and complexity
- Summary
- Questions
- Chapter 4: Java Concurrency Utilities and Testing in the Cloud Era
- Technical requirements
- Uploading your JAR file to AWS Lambda
- Introduction to Java concurrency tools - empowering cloud computing
- Real-world example - building a scalable application on AWS
- Taming the threads - conquering the cloud with the Executor framework
- The symphony of cloud integration and adaptation
- Real-world examples of thread pooling and task scheduling in cloud architectures
- Example 1 - keeping data fresh with scheduled tasks
- Example 2 - adapting to the cloud's dynamics
- Utilizing Java's concurrent collections in distributed systems and microservices architectures
- Navigating through data with ConcurrentHashMap
- Processing events with ConcurrentLinkedQueue
- Best practices for using Java's concurrent collections
- Advanced locking strategies for tackling cloud concurrency
- Revisiting lock mechanisms with a cloud perspective
- Advanced concurrency management for cloud workflows
- Sophisticated Java synchronizers for cloud applications
- Utilizing tools for diagnosing concurrency problems
- Thread dumps - the developer's snapshot
- Lock monitors - the guardians of synchronization
- The quest for clarity - advanced profiling techniques
- Weaving the web - integrating profiling tools into CI/CD pipelines
- Service mesh and APM - your cloud performance powerhouse
- Incorporating concurrency frameworks
- Mastering concurrency in cloud-based Java applications - testing and debugging tips
- Summary
- Questions
- Chapter 5: Mastering Concurrency Patterns in Cloud Computing
- Technical requirements
- Core patterns for robust cloud foundations
- The Leader-Follower pattern
- The Circuit Breaker pattern - building resilience in cloud applications
- The Bulkhead pattern - enhancing cloud application fault tolerance
- Java concurrency patterns for asynchronous operations and distributed communications
- The Producer-Consumer pattern - streamlining data flow
- The Scatter-Gather pattern: distributed processing powerhouse
- The Disruptor pattern - streamlined messaging for low-latency applications
- Combining concurrency patterns for enhanced resilience and performance
- Integrating the Circuit Breaker and Producer-Consumer patterns
- Integrating Bulkhead with Scatter-Gather for enhanced fault tolerance
- Blending concurrency patterns - a recipe for high-performance cloud applications
- Blending the Circuit Breaker and Bulkhead patterns
- Combining Scatter-Gather with the Actor model
- Merging Producer-Consumer with the Disruptor pattern
- Synergizing event sourcing with CQRS
- Summary
- Questions
- Part 2: Java's Concurrency in Specialized Domains
- Chapter 6: Java and big data - a Collaborative Odyssey
- Technical requirements
- The big data landscape - the evolution and need for concurrent processing
- Navigating the big data landscape
- Concurrency to the rescue
- Hadoop - the foundation for distributed data processing
- HDFS
- MapReduce - the processing framework
- Java and Hadoop - a perfect match
- Why Java? A perfect match for Hadoop development
- MapReduce in action
- Beyond the basics - advanced Hadoop concepts for Java developers and architects
- Yet another resource negotiator
- HBase
- Integration with the Java ecosystem
- Spark versus Hadoop - choosing the right framework for the job
- Hadoop and Spark equivalents in major cloud platforms
- Real-world Java and big data in action
- Use case 1 - log analysis with Spark
- Use case 2 - a recommendation engine
- Use case 3 - real-time fraud detection
- Summary
- Questions
- Chapter 7: Concurrency in Java for Machine Learning
- Technical requirements
- An overview of ML computational demands and Java concurrency alignment
- The intersection of Java concurrency and ML demands
- Parallel processing - the key to efficient ML workflows
- Handling big data with ease
- An overview of key ML techniques
- Case studies - real-world applications of Java concurrency in ML
- Java's tools for parallel processing in ML workflows
- DL4J - pioneering neural networks in Java
- Java thread pools for concurrent data processing
- Achieving scalable ML deployments using Java's concurrency APIs
- Best practices for thread management and reducing synchronization overhead
- Generative AI and Java - a new frontier
- Leveraging Java's concurrency model for efficient generative AI model training and inference
- Summary
- Questions
- Chapter 8: Microservices in the Cloud and Java's Concurrency
- Technical requirements
- Core principles of microservices - architectural benefits in cloud platforms
- Foundational concepts - microservices architecture and its benefits in the cloud
- Real-world examples - Netflix's evolution and Amazon's flexibility
- Essential Java concurrency tools for microservice management
- Concurrency tools - an exploration of Java's concurrency tools that are tailored for microservices
- Challenges and solutions in microservices concurrency
- Bottlenecks - diagnosing potential challenges in concurrent microservices architectures
- Consistency - ensuring data consistency and smooth inter-service communication
- Resilience - achieving system resilience and fault tolerance
- Practical design and implementation - building effective Java microservices
- Strategic best practices - deploying and scaling microservices
- Advanced concurrency patterns - enhancing microservice resilience and performance
- Data management patterns
- Summary
- Questions
- Chapter 9: Serverless Computing and Java's Concurrent Capabilities
- Technical requirements
- Fundamentals of serverless computing in java
- Core concepts of serverless computing
- Advantages of and scenarios for using serverless computing
- Drawbacks and trade-offs of serverless computing
- When to use serverless?
- Adapting Java's concurrency model to serverless environments
- Designing efficient Java serverless applications
- Introducing serverless frameworks and services - AWS SAM, Azure Functions Core Tools, Google Cloud Functions, and Oracle Functions
- AWS Serverless Application Model
- Azure Functions Core Tools
- Google Cloud Functions
- Oracle Functions
- Industry examples - Java serverless functions with a focus on concurrency
- Airbnb - optimizing property listings with serverless solutions
- LinkedIn - enhancing data processing with serverless architectures
- Expedia - streamlining travel booking with serverless solutions
- Building with serverless frameworks - a practical approach
- Using AWS SAM to define and deploy a serverless application
- Summary
- Questions
- Part 3: Mastering Concurrency in the Cloud - The Final Frontier
- Chapter 10: Synchronizing Java's Concurrency with Cloud Auto-Scaling Dynamics
- Technical requirements
- Fundamentals of cloud auto-scaling - mechanisms and motivations
- Definition and core concepts
- Advantages of cloud auto-scaling
- Triggers and conditions for auto-scaling
- A guide to setting memory utilization triggers for auto-scaling
- Java's concurrency models - alignment with scaling strategies
- Optimizing Java applications for cloud scalability - best practices
- Code example - best practices in optimizing a Java application for auto-scaling with AWS services and Docker
- Monitoring tools and techniques for Java applications
- Real-world case studies and examples
- Practical application - building scalable Java-based solutions for real-time analytics and event-driven auto-scaling
- Advanced topics
- Predictive auto-scaling using ML algorithms
- Integration with cloud-native tools and services
- Summary
- Questions
- Chapter 11: Advanced Java Concurrency Practices in Cloud Computing
- Technical requirements
- Enhancing cloud-specific redundancies and failovers in Java applications
- Leveraging Java libraries and frameworks
- Writing correct test scenarios for failover and advanced mechanisms
- Practical exercise - resilient cloud-native Java application
- GPU acceleration in Java - leveraging CUDA, OpenCL, and native libraries
- Fundamentals of GPU computing
- CUDA and OpenCL overview - differences and uses in Java applications
- TornadoVM - GraalVM-based GPU Acceleration
- Practical exercise - GPU-accelerated matrix multiplication in Java
- Specialized monitoring for Java concurrency in the cloud
- Challenges in monitoring
- Monitoring tools and techniques
- Summary
- Questions
- Chapter 12: The Horizon Ahead
- Technical requirements
- Future trends in cloud computing and Java's role
- Emerging trends in cloud computing - serverless Java beyond function as a service
- Edge computing and Java
- Java's role in edge computing architectures
- Frameworks and tools for Java-based edge applications
- AI and ML integration
- Java's position in cloud-based AI/ML workflows
- Integration of Java with cloud AI services
- Use case - serverless AI image analysis with AWS Lambda and Fargate
- Emerging concurrency and parallel processing tools in Java
- Introduction to Project Loom - virtual threads for efficient concurrency
- Code example - implementing a high-concurrency microservice using Project Loom and Akka for the AWS cloud environment
- Preparing for the next wave of cloud innovations
- Quantum computing
- Summary
- Questions
- Appendix A: Setting up a Cloud-Native Java Environment
- General approach - build and package Java applications
- Useful links for further information on AWS
- Microsoft Azure
- Google Cloud Platform
- Setting up the Google Cloud Environment
- Deploy your Java application to Google Cloud
- GKE for containerized applications
- Google Cloud Functions for serverless Java functions
- Useful links for further information
- Appendix B: Resources and Further Reading
- Recommended books, articles, and online courses
- Chapters 1-3
- Chapters 4-6
- Chapters 7-9
- Chapters 10-12
- Answers to the end-of-chapter multiple-choice questions
- Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape
- Chapter 2: Introduction to Java's Concurrency Foundations: Threads, Processes, and Beyond
- Chapter 3: Mastering Parallelism in Java
- Chapter 4: Java Concurrency Utilities and Testing in the Cloud Era
- Chapter 5: Mastering Concurrency Patterns in Cloud Computing
- Chapter 6: Java and Big Data - A Collaborative Odyssey
- Chapter 7: Concurrency in Java for Machine Learning
- Chapter 8: Microservices in the Cloud and Java's Concurrency
- Chapter 9: Serverless Computing and Java's Concurrent Capabilities
- Chapter 10: Synchronizing Java's Concurrency with Cloud Auto-Scaling Dynamics
- Chapter 11: Advanced Java Concurrency Practices in Cloud Computing
- Chapter 12: The Horizon Ahead
- Index
- Other Books You May Enjoy
