This document provides an overview of MapReduce theory and implementation. It discusses functional programming concepts like map and fold that inspired MapReduce. It then explains the MapReduce programming model, which uses map and reduce functions to parallelize large-scale data processing across clusters. Users implement map to process input key-value pairs, and reduce to combine intermediate outputs. The document outlines how MapReduce provides automatic parallelization, fault tolerance, and monitoring tools.

1. Distributed Computing Seminar Lecture 2: MapReduce Theory and Implementation Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet Summer 2007 Except as otherwise noted, the contents of this presentation are © Copyright 2007 University of Washington and licensed under the Creative Commons Attribution 2.5 License.

16. MapReduce

24. Example: Count word occurrences map(String input_key, String input_value): // input_key: document name // input_value: document contents for each word w in input_value: EmitIntermediate (w, "1"); reduce(String output_key, Iterator intermediate_values): // output_key: a word // output_values: a list of counts int result = 0; for each v in intermediate_values: result += ParseInt(v); Emit (AsString(result));