Please use this identifier to cite or link to this item:
|Title:||Query optimization for massively parallel data processing|
|Authors:||Wu, S. |
|Source:||Wu, S.,Li, F.,Mehrotra, S.,Ooi, B.C. (2011). Query optimization for massively parallel data processing. Proceedings of the 2nd ACM Symposium on Cloud Computing, SOCC 2011. ScholarBank@NUS Repository. https://doi.org/10.1145/2038916.2038928|
|Abstract:||MapReduce has been widely recognized as an efficient tool for large-scale data analysis. It achieves high performance by exploiting parallelism among processing nodes while providing a simple interface for upper-layer applications. Some vendors have enhanced their data warehouse systems by integrating MapReduce into the systems. However, existing MapReduce-based query processing systems, such as Hive, fall short of the query optimization and competency of conventional database systems. Given an SQL query, Hive translates the query into a set of MapReduce jobs sentence by sentence. This design assumes that the user can optimize his query before submitting it to the system. Unfortunately, manual query optimization is time consuming and difficult, even to an experienced database user or administrator. In this paper, we propose a query optimization scheme forMapReduce-based processing systems. Specifically, we embed into Hive a query optimizer which is designed to generate an efficient query plan based on our proposed cost model. Experiments carried out on our in-house cluster confirm the effectiveness of our query optimizer. Copyright 2011 ACM.|
|Source Title:||Proceedings of the 2nd ACM Symposium on Cloud Computing, SOCC 2011|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 11, 2017
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.