"Continuous queries process real-time streaming data and output results in streams for a wide range of applications. Due to the fluctuating stream characteristics, a streaming database system needs to dynamically adapt query execution. This dissertation proposes novel solutions to continuous query adaptation in three core areas, namely dynamic query optimization, dynamic plan migration and partitioned query adaptation. Runtime query optimization needs to efficiently generate plans that satisfy both CPU and memory resource constraints. Existing work focus on minimizing intermediate query results, which decreases memory and CPU usages simultaneously. However, doing so cannot assure that both resource constraints are being satisfied, because memory and CPU can be either positively or negatively correlated. This part of the dissertation proposes efficient optimization strategies that utilize both types of correlations to search the entire query plan space in polynomial time when a typical exhaustive search would take at least exponential time. Extensive experimental evaluations have demonstrated the effectiveness of the proposed strategies. Dynamic plan migration is concerned with on-the-fly transition from one continuous plan to a semantically equivalent yet more efficient plan. It is a must to guarantee the continuation and repeatability of dynamic query optimization. However, this research area has been largely neglected in the current literature. The second part of this dissertation proposes migration strategies that dynamically migrate continuous queries while guaranteeing the integrity of the query results, meaning there are no missing, duplicate or incorrect results. The extensive experimental evaluations show that the proposed strategies vary significantly in terms of output rates and memory usages given distinct system configurations and stream workloads. Partitioned query processing is effective to process continuous queries with large stateful operators in a distributed system. Dynamic load redistribution is necessary to balance uneven workload across machines due to changing stream properties. However, existing solutions generally assume static query plans without runtime query optimization. This part of the dissertation evaluates the benefits of applying query optimization in partitioned query processing and shows dramatic performance improvement of more than 300%. Several load balancing strategies are then proposed to consider the heterogeneity of plan shapes across machines caused by dynamic query optimization. The effectiveness of the proposed strategies is analyzed through extensive experiments using a cluster."
| Identifer | oai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-dissertations-1357 |
| Date | 23 August 2006 |
| Creators | Zhu, Yali |
| Contributors | Elke A. Rundensteiner, Advisor, Murali Mani, Committee Member, George T. Heineman, Committee Member, Volker Markl, Committee Member |
| Publisher | Digital WPI |
| Source Sets | Worcester Polytechnic Institute |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Doctoral Dissertations (All Dissertations, All Years) |
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