Building an Efcient Key-Value Store in a Flexible Address Space

Abstract

Data management applications store their data using structured files in which data are usually sorted to serve indexing and queries. However, in-place insertions and removals of data are not naturally supported in a file's address space. To avoid repeatedly rewriting existing data in a sorted file to admit changes in place, applications usually employ extra layers of indirections, such as mapping tables and logs, to admit changes out of place. However, this approach leads to increased access cost and excessive complexity. This paper presents a novel storage abstraction that provides a flexible address space, where in-place updates of arbitrary-sized data, such as insertions and removals, can be performed efficiently. With these mechanisms, applications can manage sorted data in a linear address space with minimal complexity. Extensive evaluations show that a key-value store built on top of it can achieve high performance and efficiency with a simple implementation.