Journal
SIGMOD RECORD
Volume 40, Issue 4, Pages 11-20Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2094114.2094118
Keywords
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Funding
- National Research Fund (NRF)
- Korea government(MEST) [2011-0016282]
- National Research Foundation of Korea [2011-0016282] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. While MapReduce is used in many areas where massive data analysis is required, there are still debates on its performance, efficiency per node, and simple abstraction. This survey intends to assist the database and open source communities in understanding various technical aspects of the MapReduce framework. In this survey, we characterize the MapReduce framework and discuss its inherent pros and cons. We then introduce its optimization strategies reported in the recent literature. We also discuss the open issues and challenges raised on parallel data analysis with MapReduce.
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