[PDR15] A Theoretical and Experimental Comparison of Filter-based Equijoins in MapReduce

Revue Internationale avec comité de lecture : Journal Large-scale Data and Knowledge-Centered Systems (TLDKS), pp. 41-80, 2015

Mots clés: map reduce, big data, join

Résumé: MapReduce has become an increasingly popular framework for large-scale data processing. However, complex operations such as \emph{joins} are quite expensive and require sophisticated techniques. In this paper, we review state-of-the-art strategies for joining several relations in a MapReduce environment and study their extension with \emph{filter-based approaches}. The general objective of filters is to eliminate non-matching data as early as possible in order to reduce the I/O, communication and CPU costs. We examine the impact of systematically adding filters as early as possible in MapReduce join algorithms, both analytically with cost models and practically with evaluations. The study covers binary joins, multi-way joins and recursive joins, and addresses the case of large inputs that gives rise to the most intricate challenges.


@article {
title="{A Theoretical and Experimental Comparison of Filter-based Equijoins in MapReduce}",
author="T. PHAN and L. d'Orazio and P. Rigaux",
journal="Large-scale Data and Knowledge-Centered Systems (TLDKS)",