For nearly half a century, the core design of query optimizers in industrial database systems has remained remarkably stable, relying on foundational principles from System R and the Volcano/Cascades framework. However, the rise of cloud computing, massive data volumes, and unified data platforms has exposed the limitations of this traditional, monolithic architecture. Taking an industrial perspective, this paper reviews the past and present of query optimization in production systems and identifies the challenges they face today. Then this paper highlights three key trends gaining momentum in the industry that promise to address these challenges. First, a tighter feedback loop between query optimization and query execution is being used to improve the robustness of query performance. Second, the scope of optimization is expanding from a single query to entire workloads through the convergence of query optimization and workload optimization. Third, and perhaps most transformatively, the industry is moving from monolithic designs to composable architectures that foster agility and cross-engine collaboration. Together, these trends chart a clear path toward a more dynamic, holistic, and adaptable future for query optimization in practice.

翻译：近半个世纪以来，工业数据库系统中查询优化器的核心设计始终保持惊人的稳定性，其基础原理源自System R及Volcano/Cascades框架。然而，云计算、海量数据与统一数据平台的兴起，暴露了这种传统单体架构的局限性。本文从工业视角出发，回顾生产系统中查询优化的历史与现状，剖析其当前面临的挑战。进而重点阐述业界正在兴起的三大关键趋势，这些趋势有望应对上述挑战：首先，通过建立查询优化与查询执行间更紧密的反馈循环，提升查询性能的鲁棒性；其次，通过查询优化与工作负载优化的融合，将优化范围从单一查询扩展至整体工作负载；最后，也是最具变革性的趋势，业界正从单体设计转向可组合架构，以提升系统敏捷性并促进跨引擎协作。这些趋势共同为实践中查询优化指明了迈向更动态、更全面、更适应性强的发展路径。