Key-Value Store Coupled with an Operating System for Storing Large-Scale燰alues

Abstract

The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value pair. Various types of studies have been conducted to improve the performance of the key-value store while maintaining its flexibility. However, the research efforts storing the large-scale values such as multimedia data files (e.g., images or videos) in the key-value store were limited. In this study, we propose a new key-value store, WR-Store++ aiming to store the large-scale values stably. Specifically, it provides a new design of separating data and index by working with the built-in data structure of the Windows operating system and the file system. The utilization of the built-in data structure of the Windows operating system achieves the efficiency of the key-value store and that of the file system extends the limited space of the storage significantly. We also present chunk-based memory management and parallel processing of WR-Store++ to further improve its performance in the GET operation. Through the experiments, we show that WR-Store++ can store at least 32.74 times larger datasets than the existing baseline key-value store, WR-Store, which has the limitation in storing large-scale data sets. Furthermore, in terms of processing efficiency, we show that WR-Store++ outperforms not only WR-Store but also the other state-of-the-art key-value stores, LevelDB, RocksDB, and BerkeleyDB, for individual key-value operations and mixed workloads.