Input-sensitive scalable continuous join query processing

  • Authors:

  • Pankaj K. Agarwal;Junyi Xie;Jun Yang;Hai Yu

  • Affiliations:

  • Duke University, Durham, NC;Oracle Corporation, Redwood City, CA;Duke University, Durham, NC;Google Inc., Mountain View, CA

  • Venue:
  • ACM Transactions on Database Systems (TODS)
  • Year:
  • 2009

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Abstract

This article considers the problem of scalably processing a large number of continuous queries. Our approach, consisting of novel data structures and algorithms and a flexible processing framework, advances the state-of-the-art in several ways. First, our approach is query sensitive in the sense that it exploits potential overlaps in query predicates for efficient group processing. We partition the collection of continuous queries into groups based on the clustering patterns of the query predicates, and apply specialized processing strategies to heavily clustered groups (or hotspots). We show how to maintain the hotspots efficiently, and use them to scalably process continuous select-join, band-join, and window-join queries. Second, our approach is also data sensitive, in the sense that it makes cost-based decisions on how to process each incoming tuple based on its characteristics. Experiments demonstrate that our approach can improve the processing throughput by orders of magnitude.