迈向通过共享数据与工作负载语义实现的预期数据库

中文摘要 AI 辅助

数据库管理系统日益服务于动态和探索性的工作负载，然而其许多决策仍依赖于诸如新近度、频率和地址局部性等低级信号。这些信号捕捉了数据是如何被访问的，但并未揭示正在检查的内容或分析焦点如何演变。我们主张将工作负载语义视为预期决策的一等控制信号。这一观点的核心在于，我们引入了语义局部性和语义轨迹，它们捕捉了邻近查询之间的关系以及这些关系在会话中的演变方式。我们提出了一个框架，该框架在数据、查询和会话层面表示语义上下文，对其随时间的演变进行建模，并将其转化为特定任务的效用估计。我们在语义预取和语义缓存淘汰中实例化了该框架，这两个系统共享一个语义层以做出两个独立的决策。预取利用语义轨迹来预测超越基于地址的局部性所能捕捉的未来访问，而淘汰则利用语义相关性来指导块替换。这些系统提供了初步证据，表明共享的语义上下文可以支持多个数据库管理系统组件。我们进一步概述了这一原则如何扩展到其他决策和数据系统，并讨论了在表示、成本、适应性和评估方面的关键挑战。

英文摘要

Database management systems increasingly serve dynamic and exploratory workloads, yet many of their decisions still rely on low-level signals such as recency, frequency, and address locality. These signals capture how data was accessed, but not what is being examined or how an analytical focus evolves. We argue for treating workload semantics as a first-class control signal for anticipatory decision making. Central to this view, we introduce semantic locality and semantic trajectories, which capture relationships among nearby queries and how those relationships evolve across a session. We propose a framework that represents semantic context at the data, query, and session levels, models its evolution over time, and translates it into task-specific utility estimates. We instantiate this framework in semantic prefetching and semantic cache eviction, which share a semantic layer to make two separate decisions. Prefetching uses semantic trajectories to anticipate future accesses beyond what address-based locality can capture, while eviction uses semantic relevance to inform block replacement. These systems provide initial evidence that shared semantic context can support multiple DBMS components. We further outline how this principle can extend to other decisions and data systems, and discuss key challenges in representation, cost, adaptation, and evaluation.

发表机构

• University of Melbourne（墨尔本大学）
• Aarhus University（奥胡斯大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

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8 pages, 6 Figures