Improvement of Join Algorithms for Low-Selectivity Joins on MapReduce

  • Akiyoshi MatonoEmail author
  • Hirotaka Ogawa
  • Isao Kojima
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9093)


So far, many studies on join operations on MapReduce have already been proposed. Some of those studies focus on the low-selectivity joins that are frequently used in query processing for datasets among several management domains, such as those used with Linked Open Data. We found there is room for improvement of the state-of-the-art approach for the low-selectivity join on MapReduce called the per-split semi-join[5]. In this paper, we first thus extend the per-split semi-join to improve performance. Our approach can reduce three costs for low-selectivity joins: the amount of network traffic, the number of jobs, and the amount of disk I/O. Moreover, when the number of input relations is large, the selectivity becomes low and thus the effect of our proposed reductions is maximized. Therefore, we also propose an extension of the reduce-side join, which can easily apply three or more inputs, based on the extension of the per-split semi-join. In our experiments, we evaluated two comparisons: the per-split semi-join and our extension of it and the reduce-side join and our extension of it. The first experiment shows that our extension is better than its competitor in any case. In the second experiment, we found that our extension is superior to its competitor when the selectivity is low and the number of inputs is three or more.


Query Processing Hash Table Computing Node Link Open Data Management Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.National Institute of Advanced Industrial Science and TechnologyIbarakiJapan

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