Learning to Optimize Join Queries With Deep Reinforcement Learning

@article{Krishnan2018LearningTO,
  title={Learning to Optimize Join Queries With Deep Reinforcement Learning},
  author={Sanjay Krishnan and Zongheng Yang and Kenneth Y. Goldberg and Joseph M. Hellerstein and Ion Stoica},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.03196}
}
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 suboptimal 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… CONTINUE READING

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