论文标题
在Microsoft的生产中部署转导的查询优化器
Deploying a Steered Query Optimizer in Production at Microsoft
论文作者
论文摘要
现代的分析工作负载是高度异质和大规模复杂的，使许多客户和场景都无法维持一般的查询优化器。结果，重要的是将这些优化器专门用于工作量的实例。在本文中，我们将继续进行最新的工作，以将查询优化器转向更好的工作量计划，并在将先前的研究思想推向生产部署方面取得了长足的进步。在此过程中，我们解决了一些操作挑战，包括使转向行动更易于管理，将转向的成本保持在预算之内，并避免产生意外的性能回归。我们最终的系统，QQ-ADVISOR本质上将查询计划者外部化为大规模离线管道，以更好地探索和专业化。我们讨论了设计的各个方面，并在微软的生产范围工作负载上展示了详细的结果，该系统默认情况默认情况下启用了系统。
Modern analytical workloads are highly heterogeneous and massively complex, making generic query optimizers untenable for many customers and scenarios. As a result, it is important to specialize these optimizers to instances of the workloads. In this paper, we continue a recent line of work in steering a query optimizer towards better plans for a given workload, and make major strides in pushing previous research ideas to production deployment. Along the way we solve several operational challenges including, making steering actions more manageable, keeping the costs of steering within budget, and avoiding unexpected performance regressions in production. Our resulting system, QQ-advisor, essentially externalizes the query planner to a massive offline pipeline for better exploration and specialization. We discuss various aspects of our design and show detailed results over production SCOPE workloads at Microsoft, where the system is currently enabled by default.
