A Novel Multi-Stage Forest-Based Key-Value Store for Holistic Performance Improvement

Abstract

Key-value (KV) stores based on multi-stage structures are widely deployed to organize massive amounts of easily searchable user data. However, current KV storage systems inevitably sacrifice at least one of the performance objectives, such as write, read, space efficiency etc., for the optimization of others. To understand the root cause of and ultimately remove such performance disparities among the representative existing KV stores, we analyze their enabling mechanisms and classify them into two fundamental models of data structures facilitating KV operations, namely, the multi-stage tree (MS-tree), and the multi-stage forest (MS-forest). We build SifrDB, a KV store on a novel split forest structure, that achieves the lowest write amplification across all workload patterns and minimizes space reservation for the compaction. To mitigate the read amplification inherent in MS-forest, we introduce a bloom filer mechanism based on Sorted String Tables (SSTs). Furthermore, we also present a highly efficient parallel search approach that fully exploits the access parallelism of modern flash-based storage devices to substantially boost the read performance. Evaluation results show that under both micro and YCSB benchmarks, SifrDB outperforms its closest competitors, i.e., the popular MS-forest implementations, making it a highly desirable choice for the modern KV stores.