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Optimizing adaptive multi-route query processing via time-partitioned indices

JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2013年第3期79卷 330-348页

作者： Works, Karen Rundensteiner, Elke A. Agu, Emmanuel Worcester Polytech Inst Worcester MA 01609 USA

Adaptive multi-route query processing (AMR) is an emerging paradigm for processing stream queries in highly fluctuating environments. The. content of stream data can be unpredictable. Thus, instead of selecting a fixed plan;AMR dynamically routes batches of tuples to operators in the query network based on up-to-date system statistics. The workload of query access patterns in AMR systems is ever changing. Selecting a single best index may not efficiently support all query access patterns at all times. While maintaining multiple indices to match a variety of query access patterns increases overhead and decreases throughput. Index design, while paramount for efficient query execution, is particularly challenging in AMR systems as the indices must serve the continuously evolving query access patterns. Our proposed Adaptive Multi-Route Index (AMRI) employs a bitmap time-partitioned design that serves a diverse ever changing workload of query access patterns and remains lightweight in terms of maintenance and storage requirements. We propose a high quality yet efficient assessment method modeled after hierarchical heavy hitters that exploits route relationships by modeling the frequency of the search access patterns used as nodes in a lattice. We also design assessment scheduling methods for AMRI based upon detecting changes in the search access patterns used. Our AMRI incorporates migration strategies that seek to meet the needs of both old partially serviced and new incoming search requests. Our experimental study using both synthetic and real data streams demonstrates that AMRI strikes a balance between effectively supporting dynamic stream environments while keeping the index overhead to a minimum. Using an environmental data set collected in the Intel Berkeley Research lab, our AMRI produced on average 68% more cumulative throughput than the state-of-the-art approach. (C) 2012 Elsevier Inc. All rights reserved.

关键词： data stream database systems Indexing Adaptive query processing

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Multi-route query processing and optimization

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2013年第3期79卷 312-329页

作者： Nehme, Rimma V. Works, Karen Lei, Chuan Rundensteiner, Elke A. Bertino, Elisa Worcester Polytech Inst Worcester MA 01609 USA Purdue Univ W Lafayette IN 47907 USA

A modern query optimizer typically picks a single query plan for all data based on overall data statistics. However, many have observed that real-life datasets tend to have non-uniform distributions. Selecting a single query plan may result in ineffective query execution for possibly large portions of the actual data. In addition most stream query processing systems, given the volume of data, cannot precisely model the system state much less account for uncertainty due to continuous variations. Such systems select a single query plan based upon imprecise statistics. In this paper, we present "Query Mesh" (or QM), a practical alternative to state-of-the-art data stream processing approaches. The main idea of QM is to compute multiple routes (i.e., query plans), each designed for a particular subset of the data with distinct statistical properties. We use terms "plans" and "routes" interchangeably in our work. A classifier model is induced and used to assign the best route to process incoming tuples based upon their data characteristics. We formulate the QM search space and analyze its complexity. Due to the substantial search space, we propose several cost-based query optimization heuristics designed to effectively find nearly optimal QMs. We propose the Self-Routing Fabric (SRF) infrastructure that supports query execution with multiple plans without physically constructing their topologies nor using a central router like Eddy. We also consider how to support uncertain route specification and execution in QM which can occur when imprecise statistics lead to more than one optimal route for a subset of data. Our experimental results indicate that QM consistently provides better query execution performance and incurs negligible overhead compared to the alternative state-of-the-art data stream approaches. (C) 2012 Elsevier Inc. All rights reserved.

关键词： data stream database systems Adaptive query processing

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