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It describes approaches to implementation of these patterns and provides code samples for their implementation. Chapter 7 looks at more advanced data structures and algorithms that can be used for big data processing.
It starts with using graph processing for solving common problems like shortest distance, friends of friends and PageRank, sketching their implementation in MapReduce. It then demonstrates using Bloom filters for effective membership queries and HyperLogLog for count estimations, showing how these structures can be calculated and hadoop in action free ebook by MapReduce implementations.
Finally, Chapter 8 describes approaches and best practices for debugging, testing and tuning MapReduce applications.
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The book begins by making the basic idea of Hadoop and MapReduce easier to grasp by applying the default Hadoop installation to a few easy-to-follow tasks, such as analyzing changes in word frequency across a body of documents.
This book takes you beyond the mechanics hadoop in action free ebook running Hadoop, teaching you to write meaningful programs in a MapReduce framework.