Practical concurrent Haskell : with big data applications

✍ Scribed by Mihailescu, Marius; Nita, Stefania Lorna

• Publisher: Apress
• Year: 2017
• Tongue: English
• Leaves: 272
• Category: Library

✦ Synopsis

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++.

✦ Table of Contents

Front Matter ....Pages i-xv
Front Matter ....Pages 1-1
Introduction (Stefania Loredana Nita, Marius Mihailescu)....Pages 3-11
Programming with Haskell (Stefania Loredana Nita, Marius Mihailescu)....Pages 13-46
Parallelism and Concurrency with Haskell (Stefania Loredana Nita, Marius Mihailescu)....Pages 47-65
Strategies Used in the Evaluation Process (Stefania Loredana Nita, Marius Mihailescu)....Pages 67-76
Exceptions (Stefania Loredana Nita, Marius Mihailescu)....Pages 77-86
Cancellation (Stefania Loredana Nita, Marius Mihailescu)....Pages 87-100
Transactional Memory Case Studies (Stefania Loredana Nita, Marius Mihailescu)....Pages 101-112
Debugging Techniques Used in Big Data (Stefania Loredana Nita, Marius Mihailescu)....Pages 113-131
Front Matter ....Pages 133-133
Haskell in the Cloud (Stefania Loredana Nita, Marius Mihailescu)....Pages 135-164
Haskell in Big Data (Stefania Loredana Nita, Marius Mihailescu)....Pages 165-175
Concurrency Design Patterns (Stefania Loredana Nita, Marius Mihailescu)....Pages 177-194
Large-Scale Design in Haskell (Stefania Loredana Nita, Marius Mihailescu)....Pages 195-203
Designing a Shared Memory Approach for Hadoop Streaming Performance (Stefania Loredana Nita, Marius Mihailescu)....Pages 205-220
Interactive Debugger for Development and Portability Applications Based on Big Data (Stefania Loredana Nita, Marius Mihailescu)....Pages 221-230
Iterative Data Processing on Big Data (Stefania Loredana Nita, Marius Mihailescu)....Pages 231-235
MapReduce (Stefania Loredana Nita, Marius Mihailescu)....Pages 237-245
Big Data and Large Clusters (Stefania Loredana Nita, Marius Mihailescu)....Pages 247-252
Back Matter ....Pages 253-266

✦ Subjects

Haskell (Computer program language);Big data;Cloud computing;COMPUTERS / Programming / General