Type
Conference PaperAuthors
Khayyat, Zuhair
Lucia, William
Singh, Meghna
Ouzzani, Mourad
Papotti, Paolo
Quiané-Ruiz, Jorge-Arnulfo
Tang, Nan
Kalnis, Panos

KAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2015-09-01Permanent link to this record
http://hdl.handle.net/10754/593180
Metadata
Show full item recordAbstract
Inequality joins, which join relational tables on inequality conditions, are used in various applications. While there have been a wide range of optimization methods for joins in database systems, from algorithms such as sort-merge join and band join, to various indices such as B+-tree, R*-tree and Bitmap, inequality joins have received little attention and queries containing such joins are usually very slow. In this paper, we introduce fast inequality join algorithms. We put columns to be joined in sorted arrays and we use permutation arrays to encode positions of tuples in one sorted array w.r.t. the other sorted array. In contrast to sort-merge join, we use space efficient bit-arrays that enable optimizations, such as Bloom filter indices, for fast computation of the join results. We have implemented a centralized version of these algorithms on top of PostgreSQL, and a distributed version on top of Spark SQL. We have compared against well known optimization techniques for inequality joins and show that our solution is more scalable and several orders of magnitude faster.Publisher
VLDB EndowmentConference/Event name
Proceedings of the VLDB Endowment - Proceedings of the 41st International Conference on Very Large Data BasesAdditional Links
http://dl.acm.org/citation.cfm?doid=2831360.2831362ae974a485f413a2113503eed53cd6c53
10.14778/2831360.2831362