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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 percent on average and the latency for up to 62.72 percent when compared with SMR-based LevelDB. [ABSTRACT FROM AUTHOR]

The past decades have witnessed the tremendous success of Conventional Magnetic Recording (CMR)-based Hard Disk Drives (HDDs) in data storage. To eliminate the bottleneck of CMR-based HDDs in providing higher areal density, an emerging Interlaced Magnetic Recording (IMR) is capable of achieving higher areal density with limited changes to disk makeup. Nevertheless, existing approaches for IMR-based HDDs may suffer serious read and write performance degradation as compared with CMR-based HDDs. Thus, this article presents a device-level solution, namely MAGIC translation layer, which aims at MA kinG I MR-based HDDs perform like C MR-based HDDs in terms of comparable access performance. Specifically, not merely trying to improve the performance of raw IMR-based HDDs, this work, for the first time, moves one step forward to minimize the performance gap between IMR and CMR-based HDDs. Technically, by 1) fully utilizing two special CMR-like potentials of IMR and 2) gracefully trading the sequential access performance as space usage increases, MAGIC minimizes track rewriting overheads to achieve CMR-like performance. Our results reveal that MAGIC not only improves the write performance compared with existing designs, but also has potential to approach read and write performance of CMR-based HDD. [ABSTRACT FROM AUTHOR]