What this book covers
Chapter 1, The First Step – Concurrency Design Principles, will teach you the design principles of concurrency applications. They will also learn the possible problems of concurrency applications and a methodology to design them followed by some design patterns, tips, and tricks.
Chapter 2, Managing Lots of Threads – Executors, will teach you the basic principles of the executor framework. This framework allows you to work with lots of threads without creating or managing them. You will implement the k-nearest neighbors algorithm and a basic client/server application.
Chapter 3, Getting the Maximum from Executors, will teach you some advanced characteristics of executors, including cancelation and scheduling of tasks to execute a task after a delay or every certain period of time. You will implement an advanced client/server application and a news reader.
Chapter 4, Getting Data from the Tasks – The Callable and Future Interfaces, will teach you how to work in an executor with tasks that return a result using the Callable and Future interfaces. You will implement a best-matching algorithm and an application to build an inverted index.
Chapter 5, Running Tasks Divided into Phases – The Phaser class, will teach you how to use the Phaser class to execute tasks that can be divided into phases in a concurrent way. You will implement a keyword extraction algorithm and a genetic algorithm.
Chapter 6, Optimizing Divide and Conquer Solutions – The Fork/Join Framework, will teach you how to use a special kind of executor optimized by those problems that can be resolved using the divide and conquer technique: the Fork/Join framework and its work-stealing algorithm. You will implement the k-means clustering algorithm, a data filtering algorithm, and the merge-sort algorithm.
Chapter 7, Processing Massive Datasets with Parallel Streams – The Map and Reduce Model, will teach you how to work with streams to process big datasets. In this chapter, you will learn how to implement map and reduce applications using the stream API and much more functions of streams. You will implement a numerical summarization algorithm and an information retrieval search tool.
Chapter 8, Processing Massive Datasets with Parallel Streams – The Map and Collect Model, will teach you how to use the collect() method of the stream API to perform a mutable reduction of a stream of data into a different data structure, including the predefined collectors defined in the Collectors class. You will implement a tool to search data without indexing, a recommendation system, and an algorithm to calculate the list of common contacts of two persons in a social network.
Chapter 9, Diving into Concurrent Data Structures and Synchronization Utilities, will teach you how to work with the most important concurrent data structures (data structures that can be used in concurrent applications without causing data race conditions) and all the synchronization mechanisms included in the Java concurrency API to organize the execution of tasks.
Chapter 10, Integration of Fragments and Implementation of Alternatives, will teach you how to implement a big application made by fragments of concurrent applications with their own concurrency techniques using shared memory or message passing. You will also learn different implementation alternatives to the examples presented in the book.
Chapter 11, Testing and Monitoring Concurrent Applications, teaches you how to obtain information about the status of some of the Java concurrency API elements (thread, lock, executor, and so on). You will also learn how to monitor a concurrent application using the Java VisualVM application and how to test concurrent applications with the MultithreadedTC library and the Java Pathfinder application.
