SkinnerDB: Reinforcement Learning for Query Optimization
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SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning
INTRODUCTION AND PROBLEM DEFINITION:
The work is on query optimization, more specifically: SkinnerDB focuses on finding the optimal Join Order. Because it has the most impact in practice.
SkinnerDB aims to get expected near optimal results without needing any a-priori information. It does not make strong assumptions either.
CONTRIBUTIONS:
- Introduced a new quality criterion for query evaluation strategies that compares expected and optimal execution cost.
- Proposed several adaptive execution strategies based on reinforcement learning.
- Formally proved correctness and regret bounds for those execution strategies.
- Experimental comparisations of those strategies, implemented in SkinnerDB,