@inproceedings{596cef58d51b49fe815ee85010b54ab5,
title = "MRPacker: An SQL to MapReduce optimizer",
abstract = "There have been recently quite a few works on optimizing the MapReduce execution plans, which either optimize the join operators or apply a set of translation rules to reduce the number of MapReduce jobs in an execution plan. However, none of these works has put into consideration and utilized how MapReduce jobs are generated and combined. To further improve the efficiency of MapReduce execution plans, we incorporate into our optimization approach the way how MapReduce jobs are generated and combined. In this paper, we propose MRPacker, a novel SQL-to-MapReduce optimizer by (a) using a set of transformation rules to reduce the number of MapReduce jobs, and (b) merging MapReduce jobs in a more reasonable way. We have finally experimentally demonstrated the effectiveness and efficiency of MRPacker, using the TPC-H benchmark.",
keywords = "MapReduce, Optimizer, SQL, Translator",
author = "Xuelian Lin and Yue Ye and Shuai Ma",
year = "2013",
doi = "10.1145/2505515.2507813",
language = "英语",
isbn = "9781450322638",
series = "International Conference on Information and Knowledge Management, Proceedings",
pages = "1157--1160",
booktitle = "CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management",
note = "22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 ; Conference date: 27-10-2013 Through 01-11-2013",
}