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MapReduce

2008/01/01 by Jeffrey Dean, Jay B. Dean, Sanjay Ghemawat · 18,575 citations
Computer Science · #Advanced Data Storage Technologies #Artificial intelligence #Big data #Cloud Computing and Resource Management #Computation #Computer science #Distributed computing #Function (biology) #Operating system #Parallel Computing and Optimization Techniques #Parallel computing #Petabyte #Programming language #Programming paradigm #Variety (cybernetics)

paper · pdf · doi:10.1145/1327452.1327492

published in Communications of the ACM 51(1), 107-113 (Association for Computing Machinery)

openalex publication_date 2008/01/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/01

Abstract

MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

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