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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ma, Shuai Lin, Xuelian Ye, Yue |
| 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. |
| Starting Page | 1157 |
| Ending Page | 1160 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450322638 |
| DOI | 10.1145/2505515.2507813 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-27 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Translator Optimizer Mapreduce Sql |
| Content Type | Text |
| Resource Type | Article |
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