Improving Relational Database Upon the Arrival of Storage Hardware with Built-in Transparent Compression
Yifan Qiao, Xubin Chen, Jingpeng Hao, Jiangpeng Li, Qi Wu, Jingqiang Wang, Yang Liu, Tong Zhang · 2021
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.