Design and implementation of skiplist-based key-value store on non-volatile memory

Abstract

Non-volatile random access memory (NVRAM) is a promising approach to persistent data storage with outstanding advantages over traditional storage devices, such as hard disk drives (HDDs) and solid state drives (SSDs). Some of its biggest advantages are its DRAM-like read latency and microsecond-level write latency, which are several hundred times faster than those in the original block device. However, one of the issues with using NVRAM as a storage device is designing an indexing system for its data stores to fully utilize NVRAM characteristics. The state-of-the-art indexing systems of non-volatile key-value stores are usually based on B+-trees or their variants, which were originally designed for block-based storage devices with better sequential performance than random performance. The semantics of B+-tree require data being sorted into leaf nodes and inner nodes and frequent splitting and merging to keep balanced. However, all the sorting, splitting, and merging operations cause extra write to NVRAM, which decreases its performance. In this article, we propose NV-Skiplist, a skiplist-based indexing system for key-value stores on NVRAM that fully uses the features of both NVRAM and DRAM. NV-Skiplist constructs its bottom layer in non-volatile memory to maintain data persistence and support range scans. It builds its upper layers in DRAM to retain rapid index searching and prevent consistently large overhead. We also propose a multiranged variant of NV-Skiplist to increase its search performance and scalability. We evaluate the performance of NV-Skiplist and wB+-tree which is a state-of-art scheme on an NVRAM emulator on a server with an Intel Xeon E5-2620 v2 processor. The results show that our design outperforms the original tree-based, non-volatile key-value stores up to 48%.