Guangxun Zhao

This paper presents an approach that integrates tiered storage into the Log-Structured Merge (LSM)-tree to balance Key–Value Store (KVS) performance and storage financial cost trade-offs. The implementation focuses on applying tiered storage to LSM-tree-based KVS architectures, using both vertical and horizontal storage alignment strategies or a combination of both. Additionally, these configurations leverage key–value (KV) separation to further improve performance. Our experiments reveal that this approach reduces storage financial costs while offering trade-offs in write and read performance. For write-intensive workloads, our approach achieves competitive performance compared to a fast NVMe Solid State Drive (SSD)-only approach while storing 96% of data on more affordable SATA SSDs. Additionally, it exhibits lookup performance comparable to BlobDB, and improves range query performance by 1.8x over RocksDB on NVMe SSDs. Overall, the approach results in a 49.5% reduction in storage financial cost compared to RocksDB and BlobDB on NVMe SSDs. The integration of selective KV separation further advances these improvements, setting the stage for future research into offloading remote data in LSM-tree tiered storage systems.