The Log-Structured Merge tree (LSM-tree) can transform random writes into sequential writes, adapting to the high sequential write performance of external storage devices. This has led to its widespread application in...
详细信息
The Log-Structured Merge tree (LSM-tree) can transform random writes into sequential writes, adapting to the high sequential write performance of external storage devices. This has led to its widespread application in...
详细信息
ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
The Log-Structured Merge tree (LSM-tree) can transform random writes into sequential writes, adapting to the high sequential write performance of external storage devices. This has led to its widespread application in various Key-Value (KV) storage systems. However, the LSM-tree has issues such as write amplification and periodic sharp declines in performance. Non-Volatile Memory (NVM) and Zoned Namespace (ZNS) Solid State Drive (SSD) are emerging storage devices that differ significantly from traditional SSDs. Directly applying LSM-tree to NVM and ZNS SSD is challenging due to their unique advantages and characteristics. This paper proposes the DNZ-LSM-Tree, tailored for hybrid external storage constructed using NVM and ZNS SSD. Initially, we present the structure of the DNZ-LSM-Tree, which reconstructs the LSM-tree by utilizing the distinct features of memory, NVM, and ZNS SSD. Subsequently, to address the cascading compaction issue that significantly affects the performance of LSM-tree, we construct a Compaction Cache in NVM and design a layered distribution strategy for Sorted String Tables (SSTables) with cascading compactions. In addition, the zone allocation strategy based on key overlap ratio estimation and the wear leveling strategy for DNZ-LSM-Tree are designed to manage the zones of ZNS SSD. Finally, a prototype of KV storage system based on hybrid storage devices called HNZMS is implemented by employing DNZ-LSM-Tree and tested by YCSB. The results indicate that compared to the storage system called ListDB based on LSM-tree, HNZMS can increase the write throughput by 36.2% and reduce the write amplification by 51.9%.
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