在基于成本的数据库系统中进行跨层决策时间协调：解决结构性时间错位
Cross-Layer Decision Timing Orchestration in Cost-Based Database Systems: Resolving Structural Temporal Misalignment
AI总结：
本文提出一种跨层决策时间协调框架，通过延迟绑定操作级选择，提升基于成本的数据库系统在不确定性下的执行稳定性。
摘要：
本文分析了传统基于成本的数据库管理系统（DBMS）中的执行不稳定性，并识别出优化与执行阶段之间存在结构性时间错位，这种错位会加剧尾部延迟放大。除了估计精度和原始执行吞吐量之外，我们认为决策时间和运行时信号的可用性在不确定性下对鲁棒性有实质性影响。在传统DBMS架构中，优化器依赖于历史统计信息，执行器观察运行时数据分布和资源状态，而加速器则施加了提前转移成本和摊销约束。这种时间异步性可能导致刚性的早绑定决策，在输入规模变化或统计信息过时时失效。我们提出了一种跨层决策时间协调框架，通过选择性地延迟绑定操作级选择，将最终决策权从编译时优化器转移到运行时执行器。统一风险信号（URS）整合了优化器不确定性、执行时间观察和加速器成本信号，而无需将它们压缩成单一静态成本模型。在修改后的PostgreSQL原型上进行实验，评估（i）输入规模变化，（ii）统计信息漂移，以及（iii）GPU卸载平衡区域，使用受控的微观基准测试。所提出的协调提高了执行稳定性，在严重估计漂移下将P99延迟降低了高达20倍，同时保持可比的中位延迟。
英文摘要：
This paper analyzes execution instability in traditional cost-based database management systems (DBMS) and identifies a structural timing misalignment between optimization and execution stages that contributes to tail-latency amplification. Beyond estimation accuracy and raw execution throughput, we argue that decision timing and the availability of runtime signals materially affect robustness under uncertainty. In conventional DBMS architectures, the optimizer relies on historical statistics, the executor observes runtime data distributions and resource states, and accelerators impose up-front transfer costs and amortization constraints. This temporal asynchrony can lead to rigid early-bound decisions that fail under input-scale shifts or stale statistics. We propose a cross-layer decision timing orchestration framework that shifts final decision authority from the compile-time optimizer to the runtime executor via selective late binding of operator-level choices. A Unified Risk Signal (URS) integrates optimizer uncertainty, execution-time observations, and accelerator cost signals without collapsing them into a single static cost model. Experiments on a modified PostgreSQL prototype evaluate (i) input-scale shift, (ii) stale-statistics drift, and (iii) GPU offload break-even regimes using controlled microbenchmarks. The proposed orchestration improves execution stability, reducing P99 latency by up to 20x under severe estimation drift while maintaining comparable median latency.
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