AKV: Agile Read-Efficiently Key-Value OLTP Engine for Non-Volatile Memory

Shuai Liu,Jianbin Qin,Tianyun Wang,Yuxing Chen,Anqun Pan,Rui Mao,Yu-Xuan Qiu,Makoto Onizuka,Chuan Xiao

Published 2026 in IEEE Transactions on Knowledge and Data Engineering

ABSTRACT

Non-volatile memory (NVM), as an emerging storage technology, offers several advantageous features for OLTP engines, including byte-addressability, high capacity, low energy consumption, and data persistence across power failures. Despite these benefits, the current mainstream OLTP engines still commonly adopt a hybrid architecture that deeply couples DRAM with NVM, which results in a complex system architecture and high recovery costs. In this paper, we aim to construct a highly available, stable, and recoverable OLTP engine that guarantees ACID properties through an agile system architecture. We introduce AKV (Agile Key-Value), an NVM-only OLTP storage engine designed to provide effective space utilization, high throughput, and fast failure recovery. AKV addresses the challenges of NVM space management, write redundancy, and concurrency control with two novel techniques: dual-version concurrency control and circular dual-version storage. Experimental results demonstrate that AKV achieves higher throughput (up to 69.7%) and faster recovery (up to 54×) compared to existing storage engines in most scenarios of the TPC-C benchmarks. Additionally, the codebase of AKV (4k+ lines) is more concise than that of SOTA OLTP engines like Zen (8k+ lines) and Falcon (11k+ lines). In addition, this study innovatively proposes a read abort optimization strategy based on dynamic version changes. The experimental results show that this strategy can significantly reduce the transaction abort rate of AKV in specific workload scenarios while maintaining stable system throughput, achieving a maximum reduction of up to 73% in the abort count.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    IEEE Transactions on Knowledge and Data Engineering

  • Publication date

    2026-02-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-32 of 32 references · Page 1 of 1

CITED BY

  • No citing papers are available for this paper.

Showing 0-0 of 0 citing papers · Page 1 of 1