Skew handling techniques in sort-merge join

Wei Li, Dengfeng Gao, Richard Thomas Snodgrass

Research output: Contribution to journalConference article

12 Scopus citations

Abstract

Joins are among the most frequently executed operations. Several fast join algorithms have been developed and extensively studied; these can be categorized as sort-merge, hash-based, and index-based algorithms. While all three types of algorithms exhibit excellent performance over most data, ameliorating the performance degradation in the presence of skew has been investigated only for hash-based algorithms. However, for sort-merge join, even a small amount of skew present in realistic data can result in a significant performance hit on a commercial DBMS. This paper examines the negative ramifications of skew in sort-merge join and proposes several refinements that deal effectively with data skew. Experiments show that some of these algorithms also impose virtually no penalty in the absence of data skew and are thus suitable for replacing existing sort-merge implementations. We also show how sort-merge band join performance is significantly enhanced with these refinements.

Original languageEnglish (US)
Pages (from-to)169-180
Number of pages12
JournalProceedings of the ACM SIGMOD International Conference on Management of Data
DOIs
StatePublished - Jan 1 2002
EventACM SIGMOD 2002 Proceedings of the ACM SIGMOD International Conference on Managment of Data - Madison, WI, United States
Duration: Jun 3 2002Jun 6 2002

ASJC Scopus subject areas

  • Software
  • Information Systems

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