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Learning to Optimize Join Queries With Deep Reinforcement Learni

Learning to Optimize Join Queries With Deep Reinforcement Learni

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Exhaustive enumeration of all possible join orders is often avoided, and most optimizers leverage heuristics to prune the search space. The design and implementation of heuristics are well-understood when the cost model is roughly linear, and we find that these heuristics can be significantly subop- timal when there are non-linearities in cost. Ideally, instead of a fixed heuristic, we would want a strategy to guide the search space in a more data-driven way—tailoring the search to a specific dataset and query workload. Recognizing the link between classical Dynamic Programming enumeration methods and recent results in Reinforcement Learning (RL), we propose a new method for learning optimized join search strategies. We present our RL-based DQ optimizer, which cur- rently optimizes select-
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