Data partitioning for minimizing transferred data in mapreduce

Miguel Liroz-Gistau, Reza Akbarinia, Divyakant Agrawal, Esther Pacitti, Patrick Valduriez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Reducing data transfer in MapReduce's shuffle phase is very important because it increases data locality of reduce tasks, and thus decreases the overhead of job executions. In the literature, several optimizations have been proposed to reduce data transfer between mappers and reducers. Nevertheless, all these approaches are limited by how intermediate key-value pairs are distributed over map outputs. In this paper, we address the problem of high data transfers in MapReduce, and propose a technique that repartitions tuples of the input datasets, and thereby optimizes the distribution of key-values over mappers, and increases the data locality in reduce tasks. Our approach captures the relationships between input tuples and intermediate keys by monitoring the execution of a set of MapReduce jobs which are representative of the workload. Then, based on those relationships, it assigns input tuples to the appropriate chunks. We evaluated our approach through experimentation in a Hadoop deployment on top of Grid5000 using standard benchmarks. The results show high reduction in data transfer during the shuffle phase compared to Native Hadoop.

Original languageEnglish
Title of host publicationData Management in Cloud, Grid and P2P Systems - 6th International Conference, Globe 2013, Proceedings
Pages1-12
Number of pages12
DOIs
Publication statusPublished - 10 Oct 2013
Event6th International Conference on Data Management in Grid and P2P Systems, Globe 2013 - Prague, Czech Republic
Duration: 28 Aug 201329 Aug 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8059 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Data Management in Grid and P2P Systems, Globe 2013
CountryCzech Republic
CityPrague
Period28/8/1329/8/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liroz-Gistau, M., Akbarinia, R., Agrawal, D., Pacitti, E., & Valduriez, P. (2013). Data partitioning for minimizing transferred data in mapreduce. In Data Management in Cloud, Grid and P2P Systems - 6th International Conference, Globe 2013, Proceedings (pp. 1-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8059 LNCS). https://doi.org/10.1007/978-3-642-40053-7-1