Scaling Persistent In-Memory Key-Value Stores Over Modern Tiered, Heterogeneous Memory Hierarchies

Miao Cai, Junru Shen, Yifan Yuan, Zhihao Qu, Baoliu Ye

Abstract: Recent advances in ultra-fast non-volatile memories (e.g., 3D XPoint) and high-speed interconnect fabrics (e.g., RDMA) enable a high-performance tiered, heterogeneous memory system, effectively overcoming the cost, scaling, and capacity limitations in DRAM-based key-value stores. To fully unleash the performance potential of such memory systems, this paper presents BonsaiKV+, a key-value store that makes the best use of different components in a modern RDMA-enabled heterogeneous memory system. The core of BonsaiKV+ is a tri-layer architecture that achieves efficient, elastic scaling up/out using a set of novel mechanisms and techniques—pipelined tiered indexing, NVM congestion control mechanisms, fine-grained data striping, and NUMA-aware data management—to leverage hardware strengths and tackle device deficiencies. We compare BonsaiKV+ with state-of-the-art key-value stores using a variety of YCSB workloads. Evaluation results demonstrate that BonsaiKV+ outperforms others by up to 7.30$\times$, 18.89$\times$, and 13.67$\times$ in read-, write-, and scan-intensive scenarios, respectively.