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 focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We 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. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
Data-Intensive Text Processing with MapReduce
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1 of 1 people found the following review helpful
Format:Paperback|Amazon Verified Purchase
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.
By Dimitri K
Format:Paperback|Amazon Verified Purchase
The book is educating and, hopefully, will be helpful for writing map-reduce programs. It concentrates not on API, but on algorithms, which is rare and should be appreciated. Text-processing is a good example of data-intensive processing, but the book may be useful in many other fields.
0 of 1 people found the following review helpful
Format:Paperback|Amazon Verified Purchase
I bought this book for a project at work, to prototype a log analysis system using Hadoop. I haven't bought very many technical books in the last few years, but the quality of most online documentation for Hadoop is poor and books seemed like a better option. This book is a good intro to the Map Reduce algorithm, but once I got my head around that I didn't use it very much (probably because my problem space was nothing like the sort of text processing that the book focuses on).
