Parallel data processing with MapReduce: A survey

Kyong Ha Lee, Yoon Joon Lee, Hyunsik Choi, Yon Dohn Chung, Bongki Moon

Research output: Contribution to journalArticlepeer-review

467 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)11-20
Number of pages10
JournalSIGMOD Record
Issue number4
Publication statusPublished - 2011 Dec

ASJC Scopus subject areas

  • Software
  • Information Systems


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