{"title":"Building GC-free Key-value Store on HM-SMR Drives with ZoneFS","authors":"Yiwen Zhang, Ting Yao, Ji-guang Wan, Changsheng Xie","doi":"10.1145/3502846","DOIUrl":null,"url":null,"abstract":"Host-managed shingled magnetic recording drives (HM-SMR) are advantageous in capacity to harness the explosive growth of data. For key-value (KV) stores based on log-structured merge trees (LSM-trees), the HM-SMR drive is an ideal solution owning to its capacity, predictable performance, and economical cost. However, building an LSM-tree-based KV store on HM-SMR drives presents severe challenges in maintaining the performance and space utilization efficiency due to the redundant cleaning processes for applications and storage devices (i.e., compaction and garbage collection). To eliminate the overhead of on-disk garbage collection (GC) and improve compaction efficiency, this article presents GearDB, a GC-free KV store tailored for HM-SMR drives. GearDB improves the write performance and space efficiency through three new techniques: a new on-disk data layout, compaction windows, and a novel gear compaction algorithm. We further augment the read performance of GearDB with a new SSTable layout and read ahead mechanism. We implement GearDB with LevelDB, and use zonefs to access a real HM-SMR drive. Our extensive experiments confirm that GearDB achieves both high performance and space efficiency, i.e., on average 1.7× and 1.5× better than LevelDB in random write and read, respectively, with up to 86.9% space efficiency.","PeriodicalId":273014,"journal":{"name":"ACM Transactions on Storage (TOS)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Storage (TOS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3502846","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Host-managed shingled magnetic recording drives (HM-SMR) are advantageous in capacity to harness the explosive growth of data. For key-value (KV) stores based on log-structured merge trees (LSM-trees), the HM-SMR drive is an ideal solution owning to its capacity, predictable performance, and economical cost. However, building an LSM-tree-based KV store on HM-SMR drives presents severe challenges in maintaining the performance and space utilization efficiency due to the redundant cleaning processes for applications and storage devices (i.e., compaction and garbage collection). To eliminate the overhead of on-disk garbage collection (GC) and improve compaction efficiency, this article presents GearDB, a GC-free KV store tailored for HM-SMR drives. GearDB improves the write performance and space efficiency through three new techniques: a new on-disk data layout, compaction windows, and a novel gear compaction algorithm. We further augment the read performance of GearDB with a new SSTable layout and read ahead mechanism. We implement GearDB with LevelDB, and use zonefs to access a real HM-SMR drive. Our extensive experiments confirm that GearDB achieves both high performance and space efficiency, i.e., on average 1.7× and 1.5× better than LevelDB in random write and read, respectively, with up to 86.9% space efficiency.