Journal
DISTRIBUTED AND PARALLEL DATABASES
Volume 32, Issue 4, Pages 583-605Publisher
SPRINGER
DOI: 10.1007/s10619-014-7150-1
Keywords
Multiple query optimization; Query processing; SQL-rewriting; Subexpression identification
Funding
- NSFC [61202025, 61373031, 61373156, 61272099, 61261160502]
- Program for Changjiang Scholars and Innovative Research Team in University of China (PCSIRT) [IRT1158]
- Scientific Innovation Act of STCSM [13511504200]
- Singapore NRF [CREATE E2S2]
- EU FP7 CLIMBER project [PIRSES-GA-2012-318939]
- State High-Tech Development Plan [2013AA01A601]
- Microsoft Research Asia (the Urban Informatics Research Grant)
- STCSM [12ZR1414900]
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Multiple query optimization (MQO) in the cloud has become a promising research direction due to the popularity of cloud computing, which runs massive data analysis queries (jobs) routinely. These CPU/IO intensive analysis queries are complex and time-consuming but share common components. It is challenging to detect, share and reuse the common components among thousands of SQL-like queries. Previous solutions to MQO, heuristic or genetic based, are not appropriate for the large growing query set situation. In this paper, we develop a sharing system called LSShare using our proposed Lineage-Signature approach. By LSShare, we can efficiently solve the MQO problem in a recurring query set situation in the cloud. Our system has been prototyped in a distributed system built for massive data analysis based on Alibaba's cloud computing platform (http://www.alibaba.com/). Experimental results on real data sets demonstrate the efficiency and effectiveness of the proposed approach.
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