Adaptive Caching for Continuous Queries

  • Authors:

  • Shivnath Babu;Kamesh Munagala;Jennifer Widom;Rajeev Motwani

  • Affiliations:

  • Stanford University;Duke University;Stanford University;Stanford University

  • Venue:
  • ICDE '05 Proceedings of the 21st International Conference on Data Engineering
  • Year:
  • 2005

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Abstract

We address the problem of executing continuous multiway join queries in unpredictable and volatile environments. Our query class captures windowed join queries in data stream systems as well as conventional maintenance of materialized join views. Our adaptive approach handles streams of updates whose rates and data characteristics may change over time, as well as changes in system conditions such as memory availability. In this paper we focus specifically on the problem of adaptive placement and removal of caches to optimize join performance. Our approach automatically considers conventional tree-shaped join plans with materialized subresults at every intermediate node, subresult-free MJoins, and the entire spectrum between them. We provide algorithms for selecting caches, monitoring their cost and benefits in current conditions, allocating memory to caches, and adapting as conditions change. All of our algorithms are implemented in the STREAM prototype Data Stream Management System and a thorough experimental evaluation is included.