Practical Concurrent Haskell: With Big Data Applications

Practical Concurrent Haskell: With Big Data Applications1484227808

Practical Concurrent Haskell: With Big Data Applications

By 作者: Stefania Loredana Nita – Marius Mihailescu

ISBN-10 书号: 1484227808
ISBN-13 书号: 9781484227800
Edition 版本: 1st ed.

Release Finelybook 出版日期: 2017-10-13
pages 页数: 266


Book Description to Finelybook sorting

Learn to use the APIs and frameworks for parallel and concurrent applications in Haskell. This book will show you how to exploit multicore processors with the help of parallelism in order to increase the performance of your applications.
Practical Concurrent Haskell teaches you how concurrency enables you to write programs using threads for multiple interactions. After accomplishing this, you will be ready to make your move into application development and portability with applications in cloud computing and big data. You’ll use MapReduce and other, similar big data tools as part of your Haskell big data applications development.
What You’ll Learn
Program with Haskell
Harness concurrency to Haskell
Apply Haskell to big data and cloud computing applications
Use Haskell concurrency design patterns in big data
Accomplish iterative data processing on big data using Haskell
Use MapReduce and work with Haskell on large clusters
Who This Book Is For
Those with at least some prior experience with Haskell and some prior experience with big data in another programming language such as Java, C#, Python, or C++.
Part I: Haskell Foundations. General Introductory Notions
Chapter 1: Introduction
Chapter 2: Programming with Haskell
Chapter 3: Parallelism and Concurrency with Haskell
Chapter 4: Strategies Used in the Evaluation Process
Chapter 5: Exceptions
Chapter 6: Cancellation
Chapter 7: Transactional Memory Case Studies
Chapter 8: Debugging Techniques Used in Big Data
Part II: Haskell for Big Data and Cloud Computing
Chapter 9: Haskell in the Cloud
Chapter 10: Haskell in Big Data
Chapter 11: Concurrency Design Patterns
Chapter 12: Large-Scale Design in Haskell
Chapter 13: Designing a Shared Memory Approach for Hadoop Streaming Performance
Chapter 14: Interactive Debugger for Development and Portability Applications Based on Big Data
Chapter 15: Iterative Data Processing on Big Data
Chapter 16: MapReduce
Chapter 17: Big Data and Large Clusters


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