On optimizing relational self-joins

TY - GEN

T1 - On optimizing relational self-joins

AU - Cao, Yu

AU - Zhou, Yongluan

AU - Chan, Chee Yong

AU - Tan, Kian-Lee

N1 - Conference code: 15

PY - 2012

Y1 - 2012

N2 - Self-join, which joins a relation with itself, is a prevalent operation in relational database systems. Despite its wide applicability, there has been little attention devoted to improving its performance. In this paper, we present SCALE (Sort for Clustered Access with Lazy Evaluation), an efficient self-join algorithm, which takes advantage of the fact that both inputs of a self-join operation are instances of the same relation. SCALE first sorts the relation on one join attribute, say R. A. In this way, for every value of the other join attribute, say R. B, its matching R. A tuples are essentially clustered. As SCALE scans the sorted relation, each tuple is joined with its matching tuples co-existing in memory. For tuples where full-range clustered accesses to their matching tuples are not possible, they are buffered and the unfinished part of join processing deferred. Such lazy evaluation minimizes the need for "random" access to the matching tuples. SCALE further optimizes the memory allocation for clustered access and lazy evaluation to keep the processing cost minimal. Our analytical study shows that SCALE degenerates gracefully to a Sort-Merge Join in the worst case. We have also implemented SCALE in PostgreSQL, and results of our extensive experimental study show that it outperforms both Sort-Merge Join and Hybrid Hash Join by a wide margin in (almost) all cases.

AB - Self-join, which joins a relation with itself, is a prevalent operation in relational database systems. Despite its wide applicability, there has been little attention devoted to improving its performance. In this paper, we present SCALE (Sort for Clustered Access with Lazy Evaluation), an efficient self-join algorithm, which takes advantage of the fact that both inputs of a self-join operation are instances of the same relation. SCALE first sorts the relation on one join attribute, say R. A. In this way, for every value of the other join attribute, say R. B, its matching R. A tuples are essentially clustered. As SCALE scans the sorted relation, each tuple is joined with its matching tuples co-existing in memory. For tuples where full-range clustered accesses to their matching tuples are not possible, they are buffered and the unfinished part of join processing deferred. Such lazy evaluation minimizes the need for "random" access to the matching tuples. SCALE further optimizes the memory allocation for clustered access and lazy evaluation to keep the processing cost minimal. Our analytical study shows that SCALE degenerates gracefully to a Sort-Merge Join in the worst case. We have also implemented SCALE in PostgreSQL, and results of our extensive experimental study show that it outperforms both Sort-Merge Join and Hybrid Hash Join by a wide margin in (almost) all cases.

U2 - 10.1145/2247596.2247612

DO - 10.1145/2247596.2247612

M3 - Article in proceedings

SP - 120

EP - 131

BT - Proceedings of the 15th International Conference on Extending Database Technology

PB - Association for Computing Machinery

CY - New York

T2 - 15th International Conference on Extending Database Technology

Y2 - 27 March 2012 through 30 March 2012

ER -