Book description
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
- Cover
- Frontmatter
- 1. Haskell Foundations. General Introductory Notions
-
2. Haskell for Big Data and Cloud Computing
- 9. Haskell in the Cloud
- 10. Haskell in Big Data
- 11. Concurrency Design Patterns
- 12. Large-Scale Design in Haskell
- 13. Designing a Shared Memory Approach for Hadoop Streaming Performance
- 14. Interactive Debugger for Development and Portability Applications Based on Big Data
- 15. Iterative Data Processing on Big Data
- 16. MapReduce
- 17. Big Data and Large Clusters
- Backmatter
Product information
- Title: Practical Concurrent Haskell: With Big Data Applications
- Author(s):
- Release date: September 2017
- Publisher(s): Apress
- ISBN: 9781484227817
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