SageDB: A learned database system
Name
CIDR2019_SageDB.pdf
Description
Accepted version
Size
772.69 KB
Format
Unknown
Checksum (MD5)
5352b9108a81a8287c4c6b90fd75f0f2
Author(s)
Kraska, Tim
Alizadeh, Mohammad
Beutel, Alex
Chi, Ed H.
Ding, Jialin
Kristo, Ani
Leclerc, Guillaume
Madden, Samuel R
Mao, Hongzi
Nathan, Vikram
Date Issued
2019
Journal
CIDR 2019 - 9th Biennial Conference on Innovative Data Systems Research
Version
Author's final manuscript
Abstract
© 2019 Conference on Innovative Data Systems Research (CIDR). All rights reserved. Modern data processing systems are designed to be general purpose, in that they can handle a wide variety of different schemas, data types, and data distributions, and aim to provide efficient access to that data via the use of optimizers and cost models. This general purpose nature results in systems that do not take advantage of the characteristics of the particular application and data of the user. With SageDB we present a vision towards a new type of a data processing system, one which highly specializes to an application through code synthesis and machine learning. By modeling the data distribution, workload, and hardware, SageDB learns the structure of the data and optimal access methods and query plans. These learned models are deeply embedded, through code synthesis, in essentially every component of the database. As such, SageDB presents radical departure from the way database systems are currently developed, raising a host of new problems in databases, machine learning and programming systems.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://cidrdb.org/cidr2019/program.html
