Practical Concurrent Haskell: With Big Data Applications

by Stefania Loredana Nita, Marius Mihailescu

Overview

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++.

Product Details

ISBN-13:	9781484227800
Publisher:	Apress
Publication date:	09/15/2017
Edition description:	1st ed.
Pages:	266
Product dimensions:	7.01(w) x 10.00(h) x (d)

About the Author

Stefania Loredana Nita holds two B.Sc., one in Mathematics (2013) and one in Computer Science (2016) from the University of Bucharest, Faculty of Mathematics and Computer Science; she received her M.Sc. in Software Engineering (2016) from University of Bucharest, faculty of Mathematics and Computer Science. She has worked as developer for an insurance company (Gothaer Insurance), and as a teacher of Mathematics and Computer Science in private centers of educations. Currently, she is Ph.D. student in Computer Science (from 2016) at Faculty of Mathematics and Computer Science from University of Bucharest. Also, she is teaching assistant at the same university and works since 2015 as researcher and developer at Institute for Computers, Bucharest, Romania. Her domains of interest are cryptography applied in cloud computing and big data, parallel computing and distributed systems, software engineering.


Marius Mihailescu received his B.Sc. in Science and Information Technology (2008) and B.Eng. in Computer Engineering (2009) from the University of Southern Denmark; he holds two M.Sc., one in Software Engineering (2010) from the University of Bucharest and the second one in Information Security Technology (2011) from the Military Technical Academy. His Ph.D. is in Computer Science (2015) from the University of Bucharest, Romania with a thesis on security of biometrics authentication prools. From 2005 to 2011 he worked as a software developer and researcher for different well-known companies (Softwin, NetBridge Investments, Declic) from Bucharest, Romania (software and web development, business analysis, parallel computing, cryptography researching, distributed systems). Starting in 2012 until 2015 he has been an assistant in the Informatics department, University of Titu Maiorescu and Computer Science department, University of Bucharest. Since 2015, he is a lecturer at the University of South-East Lumina.

Table of Contents

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