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ALEX: An Updatable Adaptive Learned Index
Name
1905.08898.pdf
Description
Submitted version
Size
3.94 MB
Format
Unknown
Checksum (MD5)
892dd51906df118a69366319c09cc70b
Author(s)
Ding, Jialin
Minhas, Umar Farooq
Yu, Jia
Wang, Chi
Do, Jaeyoung
Li, Yinan
Zhang, Hantian
Chandramouli, Badrish
Gehrke, Johannes
Kossman, Donald
Date Issued
2020
Journal
Proceedings of the ACM SIGMOD International Conference on Management of Data
Publisher
ACM
Version
Original manuscript
Abstract
© 2020 Association for Computing Machinery. Recent work on "learned indexes" has changed the way we look at the decades-old field of DBMS indexing. The key idea is that indexes can be thought of as "models" that predict the position of a key in a dataset. Indexes can, thus, be learned. The original work by Kraska et al. shows that a learned index beats a B+ tree by a factor of up to three in search time and by an order of magnitude in memory footprint. However, it is limited to static, read-only workloads. In this paper, we present a new learned index called ALEX which addresses practical issues that arise when implementing learned indexes for workloads that contain a mix of point lookups, short range queries, inserts, updates, and deletes. ALEX effectively combines the core insights from learned indexes with proven storage and indexing techniques to achieve high performance and low memory footprint. On read-only workloads, ALEX beats the learned index from Kraska et al. by up to 2.2X on performance with up to 15X smaller index size. Across the spectrum of read-write workloads, ALEX beats B+ trees by up to 4.1X while never performing worse, with up to 2000X smaller index size. We believe ALEX presents a key step towards making learned indexes practical for a broader class of database workloads with dynamic updates.
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
https://doi.org/10.1145/3318464.3389711
