Faculty of Smart Systems Software, Soongsil University, 369 Sangdoro, Dongjak-gu, Seoul 06978, Korea.
Int J Environ Res Public Health. 2019 Aug 26;16(17):3096. doi: 10.3390/ijerph16173096.
This article explores the performance optimizations of an embedded database memory management system to ensure high responsiveness of real-time healthcare data frameworks. SQLite is a popular embedded database engine extensively used in medical and healthcare data storage systems. However, SQLite is essentially built around lightweight applications in mobile devices, and it significantly deteriorates when a large transaction is issued such as high resolution medical images or massive health dataset, which is unlikely to occur in embedded systems but is quite common in other systems. Such transactions do not fit in the in-memory buffer of SQLite, and SQLite enforces memory reclamation as they are processed. The problem is that the current SQLite buffer management scheme does not effectively manage these cases, and the naïve reclamation scheme used significantly increases the user-perceived latency. Motivated by this limitation, this paper identifies the causes of high latency during processing of a large transaction, and overcomes the limitation via proactive and coarse-grained memory cleaning in SQLite.The proposed memory reclamation scheme was implemented in SQLite 3.29, and measurement studies with a prototype implementation demonstrated that the SQLite operation latency decreases by 13% on an average and up to 17.3% with our memory reclamation scheme as compared to that of the original version.
本文探讨了嵌入式数据库内存管理系统的性能优化，以确保实时医疗数据框架的高响应能力。SQLite 是一种广泛应用于医疗和保健数据存储系统的流行嵌入式数据库引擎。然而，SQLite 本质上是为移动设备中的轻量级应用程序构建的，当发出大事务（如高分辨率医疗图像或大量健康数据集）时，其性能会显著下降，这种情况不太可能在嵌入式系统中发生，但在其他系统中却很常见。这种事务无法适应 SQLite 的内存缓冲区，并且 SQLite 在处理它们时会强制进行内存回收。问题是，当前的 SQLite 缓冲区管理方案无法有效地管理这些情况，而使用的简单回收方案会显著增加用户感知的延迟。受此限制的启发，本文确定了在处理大事务时导致高延迟的原因，并通过在 SQLite 中进行主动和粗粒度的内存清理来克服这一限制。所提出的内存回收方案已在 SQLite 3.29 中实现，原型实现的测量研究表明，与原始版本相比，我们的内存回收方案平均将 SQLite 操作延迟降低了 13%，最高降低了 17.3%。
