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Title: PLAQUE: Automated Predicate Learning at Query Time
Predicate pushing down is a key optimization used to speed up query processing. Much of the existing practice is restricted to pushing predicates explicitly listed in the query. In this paper, we consider the challenge of learning predicates during query execution which are then exploited to accelerate execution. Prior related approaches with a similar goal are restricted (e.g., learn only from only join columns or from specific data statistics). We significantly expand the realm of predicates that can be learned from different query operators (aggregations, joins, grouping, etc.) and develop a system, entitled PLAQUE, that learns such predicates during query execution. Comprehensive evaluations on both synthetic and real datasets demonstrate that the learned predicate approach adopted by PLAQUE can significantly accelerate query execution by up to 33x, and this improvement increases to up to 100x when User-Defined Functions (UDFs) are utilized in queries.  more » « less
Award ID(s):
2008993
PAR ID:
10562499
Author(s) / Creator(s):
;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the ACM on Management of Data
Volume:
2
Issue:
1
ISSN:
2836-6573
Page Range / eLocation ID:
1 to 25
Subject(s) / Keyword(s):
query processing, filters
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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