{"title":"Improving Relational Database Upon the Arrival of Storage Hardware with Built-in Transparent Compression","authors":"Yifan Qiao, Xubin Chen, Jingpeng Hao, Jiangpeng Li, Qi Wu, Jingqiang Wang, Yang Liu, Tong Zhang","doi":"10.1109/nas51552.2021.9605481","DOIUrl":null,"url":null,"abstract":"This paper presents an approach to enable relational database take full advantage of modern storage hardware with built-in transparent compression. Advanced storage appliances (e.g., all-flash array) and some latest SSDs (solid-state drives) can perform hardware-based data compression, transparently from OS and applications. Moreover, the growing deployment of hardware-based compression capability in Cloud storage infrastructure leads to the imminent arrival of cloud-based storage hardware with built-in transparent compression. To make relational database better leverage modern storage hardware, we propose to deploy a dual in-memory vs. on-storage page format: While pages in database cache memory retain the conventional row-based format, each page on storage devices has a column-based format so that it can be better compressed by storage hardware. We present design techniques that can further improve the on-storage page data compressibility through additional light-weight column data transformation. We the impact of compression algorithms on the selection of column data transformation techniques. We integrated the design techniques into MySQL/InnoDB by adding only about 600 lines of code, and ran Sysbench OLTP workloads on a commercial SSD with built-in transparent compression. The results show that the proposed solution can bring up to 45% additional reduction on the storage cost at only a few percentage of performance degradation.","PeriodicalId":135930,"journal":{"name":"2021 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Networking, Architecture and Storage (NAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/nas51552.2021.9605481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper presents an approach to enable relational database take full advantage of modern storage hardware with built-in transparent compression. Advanced storage appliances (e.g., all-flash array) and some latest SSDs (solid-state drives) can perform hardware-based data compression, transparently from OS and applications. Moreover, the growing deployment of hardware-based compression capability in Cloud storage infrastructure leads to the imminent arrival of cloud-based storage hardware with built-in transparent compression. To make relational database better leverage modern storage hardware, we propose to deploy a dual in-memory vs. on-storage page format: While pages in database cache memory retain the conventional row-based format, each page on storage devices has a column-based format so that it can be better compressed by storage hardware. We present design techniques that can further improve the on-storage page data compressibility through additional light-weight column data transformation. We the impact of compression algorithms on the selection of column data transformation techniques. We integrated the design techniques into MySQL/InnoDB by adding only about 600 lines of code, and ran Sysbench OLTP workloads on a commercial SSD with built-in transparent compression. The results show that the proposed solution can bring up to 45% additional reduction on the storage cost at only a few percentage of performance degradation.