Converged Optimizer for Efficient Join Order Optimization
<|reference_start|>Converged Optimizer for Efficient Join Order Optimization: Existing methods for join order optimization (JOOP) primarily fall into two categories: translation-based join order optimizers (JOPTs), which translate graph queries into relational queries for optimization, and index-based JOPTs, which leverage both relational and graph optimizer techniques. However, translation-based JOPTs often fail to fully exploit the inherent features of graphs, while index-based JOPTs may neglect optimal plans during the optimization process. In this paper, we propose a novel converged JOPT with efficient optimizations. Our approach enables efficient graph query optimization by facilitating precise cost estimation. Theoretical performance analysis shows that JOOP with our method can be exponentially faster than traditional methods. Experimental results further validate that our optimizer not only reduces optimization time but also generates superior query plans compared to existing solutions. Ultimately, this work addresses significant challenges in JOOP and contributing to advancements in relational optimization strategies.<|reference_end|>
@article{lou2024converged,
    title={Converged Optimizer for Efficient Join Order Optimization},
    author={Yunkai Lou},
    journal={arXiv preprint arXiv:2408.13480},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13480},
    primaryClass={cs.DB}
}