Abstract
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.
Highlights
SQLite is an embedded database engine with a rich set of features
The popularity of SQLite extends beyond mobile applications; it manifests in various server-side applications such as large-scale data analytics and high-performance bioinformatics, offering high reliability and easy portability in datacenters [6,11]
We studied four different version of SQLite; SQLite refers to as the original version of SQLite and SQLite-INF uses an infinite capacity of memory for the in-memory buffer
Summary
SQLite is an embedded database engine with a rich set of features. Given its efficient and compact design, it is heavily used in mobile applications [1,2,3]. SQLite internally maintains a small size of buffer within the main memory; by absorbing large batches of data updates in the buffer and forcing them into storage at once, SQLite offers high throughput for small writes, which are dominant in mobile devices. To overcome the aforementioned limitation, this paper presents a new version of SQLite that proactively cleans the dirty pages at a coarse granularity, thereby minimizing undesirable performance delays during large transactions. To this end, SQLite keeps track of the number of dirty pages in the buffer, and when it exceeds a given threshold, SQLite batches them to storage altogether.
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