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

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 th...

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Bibliographic Details
Published in:IEEE transactions on computers Vol. 74; no. 2; pp. 495 - 509
Main Authors: Cai, Miao, Shen, Junru, Yuan, Yifan, Qu, Zhihao, Ye, Baoliu
Format: Journal Article
Language:English
Published: IEEE 01.02.2025
Subjects:
Bandwidth
Computers
Costs
Fabrics
Hardware
heterogeneous memory
in-memory database
Indexing
Key-value store
Memory management
Nonvolatile memory
Performance evaluation
Random access memory
ISSN:0018-9340, 1557-9956
Online Access:Get full text
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Summary: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<inline-formula><tex-math notation="LaTeX">\times</tex-math> <mml:math><mml:mo>×</mml:mo></mml:math><inline-graphic xlink:href="cai-ieq1-3500352.gif"/> </inline-formula>, 18.89<inline-formula><tex-math notation="LaTeX">\times</tex-math> <mml:math><mml:mo>×</mml:mo></mml:math><inline-graphic xlink:href="cai-ieq2-3500352.gif"/> </inline-formula>, and 13.67<inline-formula><tex-math notation="LaTeX">\times</tex-math> <mml:math><mml:mo>×</mml:mo></mml:math><inline-graphic xlink:href="cai-ieq3-3500352.gif"/> </inline-formula> in read-, write-, and scan-intensive scenarios, respectively.
ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2024.3500352