提出高效可靠的查询优化提示生成模型，显著降低查询延迟。
HERO: Hint-Based Efficient and Reliable Query Optimizer
- 采用上下文感知模型集成与图存储结构，提升提示推荐效率和可靠性。
- 在JOB基准上实现3倍查询延迟降低，接近粗粒度提示的最佳效果。
- 模型可解释且易调试，适合生产环境部署。
我们提出一种新型学习型查询优化模型，通过生成查询提示来获得更优的执行计划。该模型解决了学习型提示优化中的三大挑战：可靠提示推荐（保证查询延迟不恶化）、高效提示探索以及快速推理。通过对现有基于神经网络的提示优化方法进行深入分析，实验验证了这些挑战的存在。我们的解决方案包括基于上下文感知模型集成的新推理架构、用于可靠提示建议与快速推理的图存储，以及结合局部搜索算法的预算控制训练流程，以应对指数级搜索空间问题。在标准基准上的实验表明，该模型在粗粒度提示下的优化能力接近最优水平。在操作符提示基础上增加对并行度（query dop）的控制，使模型在JOB基准上实现3倍延迟降低，树立了新的优化标准。模型具备可解释性与易调试性，对生产环境部署至关重要。
原文摘要 · Abstract (English)
We propose a novel model for learned query optimization which provides query hints leading to better execution plans. The model addresses the three key challenges in learned hint-based query optimization: reliable hint recommendation (ensuring non-degradation of query latency), efficient hint exploration, and fast inference. We provide an in-depth analysis of existing NN-based approaches to hint-based optimization and experimentally confirm the named challenges for them. Our alternative solution consists of a new inference schema based on an ensemble of context-aware models and a graph storage for reliable hint suggestion and fast inference, and a budget-controlled training procedure with a local search algorithm that solves the issue of exponential search space exploration. In experiments on standard benchmarks, our model demonstrates optimization capability close to the best achievable with coarse-grained hints. Controlling the degree of parallelism (query dop) in addition to operator-related hints enables our model to achieve 3x latency improvement on JOB benchmark which sets a new standard for optimization. Our model is interpretable and easy to debug, which is particularly important for deployment in production.
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