Optimizing databases by learning hidden parameters of solid state drives

Aarati Kakaraparthy, Jignesh M. Patel, Kwanghyun Park, Brian P. Kroth

Research output: Contribution to journalConference articlepeer-review

8 Citations (Scopus)


Solid State Drives (SSDs) are complex devices with varying internal implementations, resulting in subtle dierences in behavior between devices. In this paper, we demonstrate how a database engine can be optimized for a particular de-vice by learning its hidden parameters. This can not only improve an application's performance, but also potentially increase the lifetime of the SSD. Our approach for optimiz-ing a database for a given SSD consists of three steps: learn-ing the hidden parameters of the device, proposing rules to analyze the I/O behavior of the database, and optimizing the database by eliminating violations of these rules. We obtain two different characteristics of an SSD, namely the request size profile and the location profile, from which we learn multiple internal parameters. Based on these pa-rameters, we propose rules to analyze the I/O behavior of a database engine. Using these rules, we uncover sub-optimal I/O patterns in SQLite3 and MariaDB when running on our experimental SSDs. Finally, we present three techniques to optimize these database engines: (1) use-hot-locations on SSD-S, which improves the SELECT operation throughput of SQLite3 and MariaDB by 29% and 27% respectively; it also improves the performance of YCSB on MariaDB by 1%-22% depending on the workload mix, (2) write-aligned-stripes on SSD-T, reduces the wear-out caused by SQLite3 write-ahead log (WAL) file by 3.1%, and (3) contain-write-in-flash-page on SSD-T, which reduces the wear-out caused by the MariaDB binary log file by 6.7%.

Original languageEnglish
Pages (from-to)519-532
Number of pages14
JournalProceedings of the VLDB Endowment
Issue number4
Publication statusPublished - 2019 Dec 9
Event46th International Conference on Very Large Data Bases, VLDB 2020 - Tokyo, Japan
Duration: 2020 Aug 312020 Sept 4

Bibliographical note

Publisher Copyright:
© VLDB Endowment.

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science


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