TY - GEN
T1 - HyperDB
T2 - 53rd International Conference on Parallel Processing, ICPP 2024
AU - Zhou, Ruisong
AU - Zhang, Yuzhan
AU - Li, Chunhua
AU - Zhou, Ke
AU - Wang, Peng
AU - Zhang, Gong
AU - Zhang, Ji
AU - Zhang, Guangyu
N1 - Publisher Copyright:
© 2024 Owner/Author.
PY - 2024/8/12
Y1 - 2024/8/12
N2 - Log-structured merge tree (LSM-tree) has been widely adopted by modern key-value stores. Deploying LSM-tree across heterogeneous SSD storage which combines the fast but expensive NVMe storage tier with the slow but economical SATA storage tier has emerged as the optimal choice for maximizing cost-effectiveness. However, existing studies typically focus on optimizing the performance of individual storage layers, thereby impeding the full utilization potential of both storage layers. We notice that they tend to over-rely on one storage layer and underutilize the other. In this paper, we present HyperDB, a novel hybrid key-value store designed to enhance the overall performance of both layers via deploying tailored data structures in different media. Especially, HyperDB devises a zone-based data layout for NVMe SSDs to reduce migration overhead, while also implementing a semi-sorted table on the SATA storage layer to minimize merge overhead. Furthermore, we propose a preemptive compaction method at the block-granularity level to further alleviate resource consumption caused by background compaction. Experimental results show that HyperDB achieves 2.25 × faster on average throughput and a 60.3% reduction in background task traffic, compared to the standard use of RocksDB in data centers today.
AB - Log-structured merge tree (LSM-tree) has been widely adopted by modern key-value stores. Deploying LSM-tree across heterogeneous SSD storage which combines the fast but expensive NVMe storage tier with the slow but economical SATA storage tier has emerged as the optimal choice for maximizing cost-effectiveness. However, existing studies typically focus on optimizing the performance of individual storage layers, thereby impeding the full utilization potential of both storage layers. We notice that they tend to over-rely on one storage layer and underutilize the other. In this paper, we present HyperDB, a novel hybrid key-value store designed to enhance the overall performance of both layers via deploying tailored data structures in different media. Especially, HyperDB devises a zone-based data layout for NVMe SSDs to reduce migration overhead, while also implementing a semi-sorted table on the SATA storage layer to minimize merge overhead. Furthermore, we propose a preemptive compaction method at the block-granularity level to further alleviate resource consumption caused by background compaction. Experimental results show that HyperDB achieves 2.25 × faster on average throughput and a 60.3% reduction in background task traffic, compared to the standard use of RocksDB in data centers today.
KW - data migration
KW - hierarchical storage
KW - key-value stores
KW - LSM-tree
UR - http://www.scopus.com/inward/record.url?scp=85202434269&partnerID=8YFLogxK
U2 - 10.1145/3673038.3673153
DO - 10.1145/3673038.3673153
M3 - Conference contribution
AN - SCOPUS:85202434269
T3 - ACM International Conference Proceeding Series
SP - 453
EP - 463
BT - 53rd International Conference on Parallel Processing, ICPP 2024 - Main Conference Proceedings
PB - Association for Computing Machinery
Y2 - 12 August 2024 through 15 August 2024
ER -