MapReduce: simplified data processing on large clusters
Open accessAbstract
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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“Rather than routing every interaction through a main agent, agents asynchronously draw tasks from the queue, read accumulated progress from the shared context, perform local reasoning, and write back compact verified updates.”
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“This distributed approach mirrors successful patterns in traditional computing—from MapReduce to modern distributed databases—where data partitioning and coordinated computation across nodes achieve scales unattainable by a single machine (Dean and Ghemawat, 2008).”
- Authors
- Dean, J., & Ghemawat, S.
- Affiliations
- Google (United States) (US)
- ORCID
- Ghemawat, Sanjay
- Venue
- Communications of the ACM
- Published
- 2008-01
- Volume
- 51
- Issue
- 1
- Pages
- 107-113
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2173213060
- Cited by
- 18,476
- References
- 21
- Citations by year
- 2026: 214 · 2025: 366 · 2024: 358 · 2023: 384 · 2022: 463 · 2021: 670 · 2020: 873 · 2019: 1185 · 2018: 1354 · 2017: 1470 · 2016: 1807 · 2015: 1975 · 2014: 1899 · 2013: 1654 · 2012: 1322
- FWCI
- 2464.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- MapReduce, distributed computing, parallel programming models, big data processing, fault tolerance, cluster computing, Google, job scheduling
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