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MapReduce: Simplified Data Processing on Large Cluster
Abstract
MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day.
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Cited by
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
Apache Spark: A unified engine for big data processing
Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., et al. (2016) · Communications of the ACM · doi:10.1145/2934664 · Cited by 2,757
Machine learning with big data: challenges and approaches.
L'Heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. M. (2017) · IEEE Access · doi:10.1109/access.2017.2696365 · Cited by 1,023
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2014.01.003 · Cited by 819
Akidau, T., Bradshaw, R., Chambers, C., Chernyak, S., Fernández-Moctezuma, R. J., Lax, R., McVeety, S., Mills, D., Perry, F., Schmidt, E., et al. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2824032.2824076 · Cited by 750
Metadata
- Authors
- Senior Software Developer, A. U., & Dayalan, M.
- Venue
- International Journal of Research and Engineering
- Publisher
- Marwah Infotech
- Published
- 2018-04
- Volume
- 5
- Issue
- 5
- Pages
- 399-403
- ISSN
- 2348-7852 (ISSN-L)
- 2348-7860
- OpenAlex
- W2122465391
- Cited by
- 2,948
- References
- 0
- FWCI
- 215.33
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
