Optimizing complex queries with multiple relation instances

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 physical 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 costbased approach to generate a plan to maximi...

• Type: Conference
• Year: 2008
• Where: SIGMOD
• Authors: Yu Cao, Gopal C. Das, Chee Yong Chan, Kian-Lee Tan
• Keywords: Database, Interleaved Execution Deadlocks, Query Processing, Query Processing Engines, SIGMOD 2008