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一直保持稳定，主要基于System R和Volcano/Cascades框架的基本原理。然而，云计算、海量数据和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，回顾了生产系统中查询优化的过去与现状，并指出了它们当前面临的挑战。随后，本文重点阐述了业界正在兴起的、有望应对这些挑战的三个关键趋势。首先，通过加强查询优化与查询执行之间的反馈闭环，提升查询性能的鲁棒性。其次，优化范围正从单一查询扩展到整个工作负载，实现了查询优化与工作负载优化的融合。第三，也可能是最具变革性的趋势是，业界正从单体架构转向可组合架构，以促进敏捷性和跨引擎协作。这些趋势共同为实际环境中查询优化走向更动态、更全面、更灵活的未来指明了清晰路径。