Practical Concurrent Haskell

PART 1 - HASKELL FOUNDATIONS. GENERAL INTRODUCTORY NOTIONS Chapter 1. Introduction1.1. Terminology and Keywords1.2. Environments, Tools and Other Resources1.3. Sample Codes Chapter 2. Programming with Haskell2.1. Getting Started2.2. Functional vs. Object-Oriented Programming2.2. Languages Basics2.2.1. Arithmetic2.2.2. Pairs, Triples and Much More2.2.3. Lists2.2.4. Source Code Files2.2.5. Functions2.3. Types2.3.1. Simple vs. Polymorphic Types2.3.2. Type Classes2.3.3. Function Types2.3.4. Data Types2.4. Input/Output Mechanisms2.5. Modules2.6. Advanced Characteristics2.6.1. Operators used as Sections and Infix2.6.2. Local Declarations2.6.3. Partial Application2.6.4. Pattern Matching2.6.5. Guards2.6.6. Instance Declarations2.6.7. Other Lists2.6.8. Arrays2.6.10. Finite Maps2.6.11. Layout2.6.12. The Final Word on Lists2.7. Advanced Types2.8. Monads2.9. Other Advanced Techniques Chapter 3. Parallelism and Concurrent with Haskell3.1. Parallelism for Dataflow3.2. Parallel Programming using REPA3.3. Concurrent Servers for a Network3.4. Threads for Parallel Programming3.5. Threads and MVars3.6. Distributed Programming3.7. EVAL Monad for Parallelism 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 Cloud9.1. Processes and messages9.1.1. Processes9.1.2. Messages to processes9.1.3. Serialization9.1.4. Starting and locating processes9.1.5. Fault Tolerance9.1.6. Receiving and matching9.2. Matching Messages9.3. Matching without blocking9.4. Messages within channels9.5. Combination with ports9.6. Closures Chapter 10. Towards Haskell in Big Data Chapter 11. Concurrency Design Patterns11.1. How to use Haskell Concurrency Design Patterns in Big Data11.2. Active Object11.3. Balking pattern11.4. Barrier11.5. Disruptor11.6. Double-checked locking11.7. Guarded suspension11.8. Monitor Object11.9. Reactor pattern11.10. Read write lock pattern11.11. Scheduler pattern11.12. Thread pool pattern11.13. Thread-local storage 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 Data14.1. Approaches to runtime type reconstruction14.2. Run-time Type Inference14.3. RTTI and new types14.4. Practical concerns14.5. Termination and efficiency14.6. Implementation in Haskell Chapter 15. Iterative Data Processing on Big Data15.1. Programming model15.2. Loop-aware Task Scheduling15.2.1. Inter-Iteration Locality15.2.2. Scheduling Algorithm15.2.3. Caching/Indexing15.2.4. Reducing Input Cache15.2.5. Reducing Output Cache15.2.6. Mapper Input Cache15.2.7. Cache Reloading15.3. Experimental Tests and Implementation Chapter 16. MapReduce16.1. Incremental and Iterative Techniques16.2. Iterative Computation in MapReduce16.3. Incremental Iterative Processing on MRBGraph Chapter 17. Big Data and Large Clusters17.1. Data Processing on Large Clusters17.2. Programming Model17.3. Master Data Structures17.4. Fault Tolerance17.5. Locality17.6. Task Granularity17.7. Backup Tasks17.8. Partitioning Function17.9. Implementation of Data Processing Techniques