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Title: Optimizing complex queries with multiple relation instances
Authors: Cao, Y.
Das, G.C. 
Chan, C.-Y. 
Tan, K.-L. 
Keywords: Interleaved execution
Query optimization
Query processing
Shared scan
Issue Date: 2008
Citation: Cao, Y.,Das, G.C.,Chan, C.-Y.,Tan, K.-L. (2008). Optimizing complex queries with multiple relation instances. Proceedings of the ACM SIGMOD International Conference on Management of Data : 525-538. ScholarBank@NUS Repository.
Abstract: Today's query processing engines do not take advantage of the multiple occurrences of a relation in a query to improve performance. Instead, each instance is treated as a distinct relation and has its own independent table access method. In this paper, we present MAPLE, a Multi-instance-Aware PLan Evaluation engine that enables multiple instances of a relation to share one plrysical scan (called SharedScan) with limited buffer space. During execution, as SharedScan pulls a tuple for any instance, that tuple is also pushed to the buffers of other instances with matching predicates. To avoid buffer overflow, a novel interleaved execution strategy is proposed: whenever an instance's buffer becomes full, the execution is temporarily switched to a drainer (an ancestor blocking operator of the instance) to consume all the tuples in the buffer. Thus, the execution is interleaved between normal processing and drainers. We also propose a cost-based approach to generate a plan to maximize the shared scan benefit as well as to avoid interleaved execution deadlocks. MAPLE is light-weight and can be easily integrated into existing RDBMS executors. We have implemented MAPLE in PostgreSQL, and our experimental study on the TPC-DS benchmark shows significant reduction in execution time. © Copyright 2008 ACM.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
ISBN: 9781605581026
ISSN: 07308078
DOI: 10.1145/1376616.1376671
Appears in Collections:Staff Publications

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