Book description
Master the principles and techniques of multithreaded programming with the Java 8 Concurrency API
About This Book
- Implement concurrent applications using the Java 8 Concurrency API and its new components
- Improve the performance of your applications or process more data at the same time, taking advantage of all of your resources.
- Construct real-world examples related to machine learning, data mining, image processing, and client/server environments
Who This Book Is For
If you are a competent Java developer with a good understanding of concurrency but have no knowledge of how to effectively implement concurrent programs or use streams to make processes more efficient, then this book is for you.
What You Will Learn
- Design concurrent applications by converting a sequential algorithm into a concurrent one
- Discover how to avoid all the possible problems you can get in concurrent algorithms
- Use the Executor framework to manage concurrent tasks without creating threads
- Extend and modify Executors to adapt their behavior to your needs
- Solve problems using the divide and conquer technique and the Fork/Join framework
- Process massive data sets with parallel streams and Map/Reduce implementation
- Control data-race conditions using concurrent data structures and synchronization mechanisms
- Test and monitor concurrent applications
In Detail
Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. All the sub-tasks are combined together once the required results are achieved; they are then merged to get the final output. The whole process is very complex. This process goes from the design of concurrent algorithms to the testing phase where concurrent applications need extra attention. Java includes a comprehensive API with a lot of ready-to-use components to implement powerful concurrency applications in an easy way, but with a high flexibility to adapt these components to your needs.
The book starts with a full description of design principles of concurrent applications and how to parallelize a sequential algorithm. We'll show you how to use all the components of the Java Concurrency API from basics to the most advanced techniques to implement them in powerful concurrency applications in Java.
You will be using real-world examples of complex algorithms related to machine learning, data mining, natural language processing, image processing in client / server environments. Next, you will learn how to use the most important components of the Java 8 Concurrency API: the Executor framework to execute multiple tasks in your applications, the phaser class to implement concurrent tasks divided into phases, and the Fork/Join framework to implement concurrent tasks that can be split into smaller problems (using the divide and conquer technique). Toward the end, we will cover the new inclusions in Java 8 API, the Map and Reduce model, and the Map and Collect model. The book will also teach you about the data structures and synchronization utilities to avoid data-race conditions and other critical problems. Finally, the book ends with a detailed description of the tools and techniques that you can use to test a Java concurrent application.
Style and approach
A complete guide implementing real-world examples with algorithms related to machine learning, data mining, and natural language processing in client/server environments. All the examples are explained in a step-by-step approach.
Table of contents
-
Mastering Concurrency Programming with Java 8
- Table of Contents
- Mastering Concurrency Programming with Java 8
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
-
1. The First Step – Concurrency Design Principles
- Basic concurrency concepts
- Possible problems in concurrent applications
- A methodology to design concurrent algorithms
- Java concurrency API
- Concurrency design patterns
- The Java memory model
-
Tips and tricks to design concurrent algorithms
- Identify the correct independent tasks
- Implement concurrency at the highest possible level
- Take scalability into account
- Use thread-safe APIs
- Never assume an execution order
- Prefer local thread variables over static and shared when possible
- Find the more easily parallelizable version of the algorithm
- Using immutable objects when possible
- Avoiding deadlocks by ordering the locks
- Using atomic variables instead of synchronization
- Holding locks for as short a time as possible
- Taking precautions using lazy initialization
- Avoiding the use of blocking operations inside a critical section
- Summary
- 2. Managing Lots of Threads – Executors
- 3. Getting the Maximum from Executors
-
4. Getting Data from the Tasks – The Callable and Future Interfaces
- Introducing the Callable and Future interfaces
-
First example – a best-matching algorithm for words
- The common classes
- A best-matching algorithm – the serial version
- A best-matching algorithm – the first concurrent version
- A best-matching algorithm – the second concurrent version
- The word exists algorithm – a serial version
- The word exists algorithm – the concurrent version
- Comparing the solutions
- The second example – creating an inverted index for a collection of documents
- Summary
- 5. Running Tasks Divided into Phases – The Phaser Class
-
6. Optimizing Divide and Conquer Solutions – The Fork/Join Framework
- An introduction to the Fork/Join framework
- The first example – the k-means clustering algorithm
- The second example – a data filtering algorithm
- The third example – the merge sort algorithm
- Other methods of the Fork/Join framework
- Summary
-
7. Processing Massive Datasets with Parallel Streams – The Map and Reduce Model
- An introduction to streams
- The first example – a numerical summarization application
-
The second example – an information retrieval search tool
- An introduction to the reduction operation
- The first approach – full document query
- The second approach – reduced document query
- The third approach – generating an HTML file with the results
- The fourth approach – preloading the inverted index
- The fifth approach – using our own executor
- Getting data from the inverted index – the ConcurrentData class
- Getting the number of words in a file
- Getting the average tfxidf value in a file
- Getting the maximum and minimum tfxidf values in the index
- The ConcurrentMain class
- The serial version
- Comparing the solutions
- Summary
- 8. Processing Massive Datasets with Parallel Streams – The Map and Collect Model
- 9. Diving into Concurrent Data Structures and Synchronization Utilities
-
10. Integration of Fragments and Implementation of Alternatives
- Big-block synchronization mechanisms
- An example of a document clustering application
-
Implementation of alternatives with concurrent programming
- The k-nearest neighbors' algorithm
- Building an inverted index of a collection of documents
- A best-matching algorithm for words
- A genetic algorithm
- A keyword extraction algorithm
- A k-means clustering algorithm
- A filtering data algorithm
- Searching an inverted index
- A numeric summarization algorithm
- A search algorithm without indexing
- A recommendation system using the Map and Collect model
- Summary
- 11. Testing and Monitoring Concurrent Applications
- Index
Product information
- Title: Mastering Concurrency Programming with Java 8
- Author(s):
- Release date: February 2016
- Publisher(s): Packt Publishing
- ISBN: 9781785886126
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