A dip in the reservoir: maintaining sample synopses of evolving datasets

  • Authors:

  • Rainer Gemulla;Wolfgang Lehner;Peter J. Haas

  • Affiliations:

  • Technische Universität Dresden, Dresden, Germany;Technische Universität Dresden, Dresden, Germany;IBM Almaden Research Center, San Jose, California

  • Venue:
  • VLDB '06 Proceedings of the 32nd international conference on Very large data bases
  • Year:
  • 2006

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Abstract

Perhaps the most flexible synopsis of a database is a random sample of the data; such samples are widely used to speed up processing of analytic queries and data-mining tasks, enhance query optimization, and facilitate information integration. In this paper, we study methods for incrementally maintaining a uniform random sample of the items in a dataset in the presence of an arbitrary sequence of insertions and deletions. For "stable" datasets whose size remains roughly constant over time, we provide a novel sampling scheme, called "random pairing" (RP) which maintains a bounded-size uniform sample by using newly inserted data items to compensate for previous deletions. The RP algorithm is the first extension of the almost 40-year-old reservoir sampling algorithm to handle deletions. Experiments show that, when dataset-size fluctuations over time are not too extreme, RP is the algorithm of choice with respect to speed and sample-size stability. For "growing" datasets, we consider algorithms for periodically "resizing" a bounded-size random sample upwards. We prove that any such algorithm cannot avoid accessing the base data, and provide a novel resizing algorithm that minimizes the time needed to increase the sample size.