AI 中文总结
本文针对现有数据库内存抽象的局限，提出Oracle AI Database的MGA共享内存方案，在TPC-H和ONNX推理测试中实现了显著的延迟降低与内存优化。
AI 中文摘要
尽管现代数据库系统已存在多个内存区域，但在生产环境约束下，提供高效的内存形式仍是一项挑战。在企业级数据系统中，现有抽象在粗粒度全局共享与严格进程隔离之间存在权衡，导致数据复制、内存碎片化，且对受控共享的支持有限。随着工作负载日益多样化，这些挑战愈发凸显，系统必须在容忍进程故障的同时保持可预测的性能。本文介绍了Oracle AI Database中的托管全局区域（Managed Global Area，MGA），这是一种作用域受限的共享内存抽象，可解决上述局限。MGA允许组件在选定进程间显式定义分配源、成员资格和协调语义，同时与生产数据库引擎集成。与Oracle中完全共享的内存区域（如系统全局区（System Global Area，SGA））不同，MGA支持动态进程成员资格和模块化内存使用，无需施加系统范围的可见性。我们在对共享内存执行有压力的分析和AI工作负载上评估了MGA，包括TPC-H哈希连接和ONNX Runtime推理。在并发执行下，MGA可将连接密集型TPC-H查询的延迟降低多达35%；对于基于ONNX的推理，启用MGA的模型共享可将内存占用减少多达90%，并降低大模型推理延迟多达37%。这些结果表明，动态作用域的共享内存可提升生产数据库系统的效率和可预测性。
英文摘要
Despite the presence of multiple memory regions in modern database systems, supporting an efficient form of memory remains a challenge under production constraints. In enterprise-grade data systems, existing abstractions impose a trade-off between coarse-grained global sharing and strict process isolation, resulting in data copying, memory fragmentation, and limited support for controlled sharing. These challenges become more pronounced as workloads grow more diverse, and systems must tolerate process failures while maintaining predictable performance. This paper introduces the Managed Global Area (MGA), a scoped shared-memory abstraction in Oracle AI Database that addresses these limitations. MGA allows components to explicitly define allocation source, membership, and coordination semantics across selected processes while integrating with a production database engine. Unlike fully shared memory regions in Oracle, such as the System Global Area (SGA), MGA supports dynamic process membership and modular memory usage without imposing system-wide visibility. We evaluate MGA on analytical and AI workloads that stress shared-memory execution, including TPC-H hash joins and ONNX Runtime inference. Under concurrent execution, MGA reduces latency for join-intensive TPC-H queries by up to 35%. For ONNX- based inference, MGA-enabled model sharing reduces memory footprint by up to 90% and lowers large-model inference latency by up to 37%. These results demonstrate that dynamically scoped shared memory can improve both efficiency and predictability in production database systems.
Comments13 pages, 6 figures, 2 tables, This is the authors' accepted manuscript. The final version will appear in the Proceedings of the VLDB Endowment (PVLDB), 2026
