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Using a Greedy Algorithm for the Improvement of a MapReduce, Theta join, M-Bucket-I Heuristic
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 2,  2016, pp.229-236
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.2.229
 Title & Authors
Using a Greedy Algorithm for the Improvement of a MapReduce, Theta join, M-Bucket-I Heuristic
Kim, Wooyeol; Shim, Kyuseok;
Theta join is one of the essential and important types of queries in database systems. As the amount of data needs to be processed increases, processing theta joins with a single machine becomes impractical. Therefore, theta join algorithms using distributed computing frameworks have been studied widely. Although one of the state-of-the-art theta-join algorithms uses M-Bucket-I heuristic, it is hard to use since running time of M-Bucket-I heuristic, which computes a mapping from a record to a reducer (i.e., reducer mapping), is O(n) where n is the size of input data. In this paper, we propose MBI-I algorithm which reduces the running time of M-Bucket-I heuristic to and gives the same result as M-Bucket-I heuristic does. We also conducted several experiments to show algorithm and confirmed that our algorithm can improve the performance of a theta join by 10%.
theta join;distributed computing;MapReduce;histogram;
 Cited by
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