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FITing-Tree: A Data-aware Index Structure
Name
1801.10207.pdf
Description
Accepted version
Size
2.58 MB
Format
Adobe PDF
Checksum (MD5)
c98a15b66558a1d02cc7608e25789416
Author(s)
Galakatos, Alex
Markovitch, Michael
Binnig, Carsten
Fonseca, Rodrigo
Kraska, Tim
Journal
Proceedings of the ACM SIGMOD International Conference on Management of Data
Publisher
Association for Computing Machinery (ACM)
Version
Author's final manuscript
Abstract
© 2019 Association for Computing Machinery. Index structures are one of the most important tools that DBAs leverage to improve the performance of analytics and transactional workloads. However, building several indexes over large datasets can often become prohibitive and consume valuable system resources. In fact, a recent study showed that indexes created as part of the TPC-C benchmark can account for 55% of the total memory available in a modern DBMS. This overhead consumes valuable and expensive main memory, and limits the amount of space available to store new data or process existing data. In this paper, we present a novel data-aware index structure called FITing-Tree which approximates an index using piece-wise linear functions with a bounded error specified at construction time. This error knob provides a tunable parameter that allows a DBA to FIT an index to a dataset and workload by being able to balance lookup performance and space consumption. To navigate this tradeoff, we provide a cost model that helps determine an appropriate error parameter given either (1) a lookup latency requirement (e.g., 500ns) or (2) a storage budget (e.g., 100MB). Using a variety of real-world datasets, we show that our index is able to provide performance that is comparable to full index structures while reducing the storage footprint by orders of magnitude.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1145/3299869.3319860
