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Cogset: a high performance MapReduce engine
Author(s) -
Valvåg S.V.,
Johansen Dag,
Kvalnes Åge
Publication year - 2013
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.2827
Subject(s) - computer science , closing (real estate) , interface (matter) , parallel computing , margin (machine learning) , big data , operating system , machine learning , bubble , maximum bubble pressure method , political science , law
SUMMARY Cogset is a generic and efficient engine for reliable storage and parallel processing of distributed data sets. It supports a number of high‐level programming interfaces, including a MapReduce interface compatible with Hadoop. In this paper, we present Cogset's architecture and evaluate its performance as a MapReduce engine, comparing it with Hadoop. Our results show that Cogset generally outperforms Hadoop by a significant margin. We investigate the underlying causes of this difference in performance and demonstrate some relatively minor modifications that markedly improve Hadoop's performance, closing some of the gap. Copyright © 2012 John Wiley & Sons, Ltd.