Data-Intensive Text Processing with MapReduce PDF Download Ebook. Jimmy Lin, Chris Dyer and Graeme Hirst focus on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.
Authors introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers.
The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance.
This book provides a valuable discussion of the strategies involved in developing MapReduce algorithms. Although I have done considerable parallel programming using MPI and OpenMP, I find MapReduce algorithms to be somewhat non-intuitive, and this book has helped me to overcome that barrier.
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