Jianan Yuan, Hua Liu, Shangyu Wu, Yi-Chien Lin, Tiantian Wang, Chenlin Ma, Rui Mao, Yi Wang
{"title":"Work-in-Progress: Lark: A Learned Secondary Index Toward LSM-tree for Resource-Constrained Embedded Storage Systems","authors":"Jianan Yuan, Hua Liu, Shangyu Wu, Yi-Chien Lin, Tiantian Wang, Chenlin Ma, Rui Mao, Yi Wang","doi":"10.1109/CODES-ISSS55005.2022.00012","DOIUrl":null,"url":null,"abstract":"LSM-tree-based key-value stores are popular in embedded storage systems. With the growing demands of data analysis, the secondary index is created to support non-primary-key lookups. However, the lookup efficiency and space consumption of secondary index remain for further optimization. Inspired by the learned index, this paper presents Lark, a learned secondary index toward LSM-tree for resource-constrained embedded storage systems. Lark employs machine learning to speed up the non-primary-key queries and compress secondary indexes. Our preliminary evaluations show that, in comparison with traditional secondary index schemes, Lark achieves better lookup performance with less space consumption.","PeriodicalId":129167,"journal":{"name":"2022 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CODES-ISSS55005.2022.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
LSM-tree-based key-value stores are popular in embedded storage systems. With the growing demands of data analysis, the secondary index is created to support non-primary-key lookups. However, the lookup efficiency and space consumption of secondary index remain for further optimization. Inspired by the learned index, this paper presents Lark, a learned secondary index toward LSM-tree for resource-constrained embedded storage systems. Lark employs machine learning to speed up the non-primary-key queries and compress secondary indexes. Our preliminary evaluations show that, in comparison with traditional secondary index schemes, Lark achieves better lookup performance with less space consumption.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
