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MAPREDUCE: SIMPLIFIED DATA PROCESSING ON LARGE CLUSTERS.

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...

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Published in:Communications of the ACM Vol. 51; no. 1; pp. 107 - 114
Main Authors: Dean, Jeffrey, Ghemawat, Sanjay
Format: Article
Published: Association for Computing Machinery Jan2008
Subjects:
Parallel processing
Data
Electronic data processing
Parallel algorithms
Google Inc.
Computer programmers
Online Access:View this record in EBSCOhost
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          Dean, Jeffrey
          Ghemawat, Sanjay
      su:
        Parallel processing
        Data
        Electronic data processing
        Parallel algorithms
        Google Inc.
        Computer programmers
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          Parallel processing
          Data
          Electronic data processing
          Parallel algorithms
          Google Inc.
          Computer programmers
      ab: 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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    language: English
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