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About this paper
Yesdaulet Izenov, Asoke Datta, Brian Tsan, Abylay Amanbayev, Florin Rusu
Researchers at the University of California Merced proposed an Ensemble Spanning Tree Enumeration (ESTE) framework for query plan optimization, reframing the problem as finding low-cost spanning trees within a join graph with dynamically weighted edges. ESTE consistently delivers better plan quality and execution times than single-plan heuristics like GOO, while maintaining manageable optimization times, even rivaling exhaustive methods for complex queries under estimated cardinalities.
Key insights
- Query optimization can be effectively reformulated as finding low-cost spanning trees, naturally avoiding cross-joins and allowing the application of efficient graph algorithms.
- Incorporating dynamic edge weights into spanning tree algorithms is crucial for accurately modeling multi-table correlations and generating realistic, optimized physical query plans.
- Systematically diversifying the search for spanning trees from every possible starting edge enhances the robustness and quality of enumerated plans, bridging the gap between exhaustive optimality and heuristic speed.
Summary by alphaXiv.
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