R&D: KVSTL Application Support to LSM-Tree Based Key-Value Store via Shingled Translation Layer Data Management

IEEE Transactions on Computers has published an article written by Shuo-Han Chen,Institute of Information Science, Academia Sinica, 38017 Taipei, TW, Taiwan, 11529, Yuhong Liang, and Ming-Chang Yang, Computer Science and Engineering, The Chinese University of Hong Kong, 26451 Hong Kong, Hong Kong, Hong Kong.

Abstract: “LSM-tree based Key-value (KV) stores greatly fit the needs of write-intensive applications with its efficient data store and retrieval operations to datasets. To accommodate ever-growing datasets, shingled magnetic recording (SMR) drives have become a popular option to provide large storage capacity for KV stores at low cost. SMR drives achieve high storage density via overlapping tracks on the disk surface. However, the overlapped track layout induces the sequential-write constraint and prevents KV stores from storing and rearranging KV pairs efficiently. In this paper, we present KVSTL, a KV store aware Shingled Translation Layer (STL), to preserve the merits of existing KV stores, while exploiting the high storage density of SMR drives. KVSTL is proposed as an application support to hide the management complexity of SMR drives and facilitates the management of SMR drives via passing only the level and invalidation information of LSM-tree based KV stores onto SMR drives. The proposed KVSTL achieves its performance enhancement via managing key-value pairs with level awareness and enabling efficient storage space management with the invalidation information. The results show that KVSTL can reduce the written data amount for 69.45% on average and the latency for up to 62.72% when compared with SMR-based LevelDB.“