DS ‐ LSM ‐Tree: SSTable ‐Decoupled LSM ‐Tree

ABSTRACT

The Log‐Structured Merge Tree (LSM‐tree) is widely adopted in key‐value (KV) storage systems because it transforms random writes into sequential writes. It has also become an important storage structure for supporting highly concurrent data access in high‐performance computing, cloud computing, and large‐scale data‐intensive applications. However, LSM‐tree still suffers from write amplification and periodic performance degradation, especially on emerging storage devices with strict sequential‐write constraints, such as Zoned Namespace Solid State Drives (ZNS SSDs). Meanwhile, Non‐Volatile Memory (NVM) provides new opportunities for optimizing the internal organization of LSM‐tree‐based storage systems. We observe that traditional SSTables tightly couple metadata with data blocks, causing frequent metadata updates to trigger unnecessary rewrites of large data regions and further exacerbating write amplification. To address this problem, we propose DS‐LSM‐tree, an LSM‐tree design based on SSTable decoupling. In DS‐LSM‐tree, large and low‐update‐frequency data blocks are sequentially stored on ZNS SSDs, while frequently updated fine‐grained metadata are migrated to NVM, thereby alleviating the mismatch between update granularity and storage device characteristics. Based on the decoupled SSTable design, we further develop a ZNS‐aware SSTable metadata structure and propose an NVM‐based logical merge strategy for SSTable data blocks, which reduces unnecessary physical data migration during compaction. We implement a prototype system named SPMS based on DS‐LSM‐tree and evaluate it using YCSB benchmarks. Experimental results show that, compared with ListDB, SPMS improves write throughput by up to 51.2%, increases read throughput by 5.1%, and reduces write amplification by 53.1%.