Parallel data processing with MapReduce

A survey

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

Research output: Contribution to journalArticle

366 Citations (Scopus)

Abstract

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
Volume40
Issue number4
DOIs
Publication statusPublished - 2011 Dec 1

Fingerprint

Momentum
Industry

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Parallel data processing with MapReduce : A survey. / Lee, Kyong Ha; Lee, Yoon Joon; Choi, Hyunsik; Chung, Yon Dohn; Moon, Bongki.

In: SIGMOD Record, Vol. 40, No. 4, 01.12.2011, p. 11-20.

Research output: Contribution to journalArticle

Lee, KH, Lee, YJ, Choi, H, Chung, YD & Moon, B 2011, 'Parallel data processing with MapReduce: A survey', SIGMOD Record, vol. 40, no. 4, pp. 11-20. https://doi.org/10.1145/2094114.2094118
Lee, Kyong Ha ; Lee, Yoon Joon ; Choi, Hyunsik ; Chung, Yon Dohn ; Moon, Bongki. / Parallel data processing with MapReduce : A survey. In: SIGMOD Record. 2011 ; Vol. 40, No. 4. pp. 11-20.
@article{211824453b334caea7ee5cd101ecfae8,
title = "Parallel data processing with MapReduce: A survey",
abstract = "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.",
author = "Lee, {Kyong Ha} and Lee, {Yoon Joon} and Hyunsik Choi and Chung, {Yon Dohn} and Bongki Moon",
year = "2011",
month = "12",
day = "1",
doi = "10.1145/2094114.2094118",
language = "English",
volume = "40",
pages = "11--20",
journal = "SIGMOD Record",
issn = "0163-5808",
publisher = "Association for Computing Machinery (ACM)",
number = "4",

}

TY - JOUR

T1 - Parallel data processing with MapReduce

T2 - A survey

AU - Lee, Kyong Ha

AU - Lee, Yoon Joon

AU - Choi, Hyunsik

AU - Chung, Yon Dohn

AU - Moon, Bongki

PY - 2011/12/1

Y1 - 2011/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84863162233&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84863162233&partnerID=8YFLogxK

U2 - 10.1145/2094114.2094118

DO - 10.1145/2094114.2094118

M3 - Article

VL - 40

SP - 11

EP - 20

JO - SIGMOD Record

JF - SIGMOD Record

SN - 0163-5808

IS - 4

ER -