MapReduce join strategies for key-value storage

This paper analyses MapReduce join strategies used for big data analysis and mining known as map-side and reduce-side joins. The most used joins will be analysed in this paper, which are theta-join algorithms including all pair partition join, repartition join, broadcasting join, semi join, per-spli...

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Bibliographic Details
Published in:JCSSE 2014 : 2014 11th International Joint Conference on Computer Science and Software Engineering : Chonburi, Thailand, May 14-16, 2014 pp. 164 - 169
Main Authors: Van Hieu, Duong, Smanchat, Sucha, Meesad, Phayung
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2014
Subjects:
Big Data
Codes
Compounds
Data mining
Data models
Distributed databases
join strategy
MapReduce
NoSQL
Software engineering
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Summary:This paper analyses MapReduce join strategies used for big data analysis and mining known as map-side and reduce-side joins. The most used joins will be analysed in this paper, which are theta-join algorithms including all pair partition join, repartition join, broadcasting join, semi join, per-split semi join. This paper can be considered as a guideline for MapReduce application developers for the selection of join strategies. The analysis of several join strategies for big data analysis and mining is accompanied by comprehensive examples.
DOI:10.1109/JCSSE.2014.6841861