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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 sharp, Python, or C++.

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PART 1 - HASKELL FOUNDATIONS. GENERAL INTRODUCTORY NOTIONS
Chapter 1. Introduction Chapter 2. Programming with Haskell
Chapter 3. Parallelism and Concurrent with Haskell
Chapter 4. Strategies used in Evaluation Process
Chapter 5. Exceptions for Input/Output
Chapter 6. Cancellation
Chapter 7. Transactional Memory Case Studies
Chapter 8. Debugging Techniques for Big Data
PART 2 - HASKELL FOR BIG DATA AND CLOUD COMPUTING
Chapter 9. Towards Haskell in Cloud Chapter 10. Towards Haskell in Big Data
Chapter 11. Concurrency Design Patterns Chapter 12. Large-scale Design in Haskell
Chapter 13. Designing 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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