Bifocal Sampling for Skew-Resistant Join Size Estimation.

Sumit Ganguly, Phillip B. Gibbons, Yossi Matias, Abraham Silberschatz: Bifocal Sampling for Skew-Resistant Join Size Estimation. SIGMOD Conference 1996: 271-281

@inproceedings{DBLP:conf/sigmod/GangulyGMS96,
  author    = {Sumit Ganguly and
               Phillip B. Gibbons and
               Yossi Matias and
               Abraham Silberschatz},
  editor    = {H. V. Jagadish and
               Inderpal Singh Mumick},
  title     = {Bifocal Sampling for Skew-Resistant Join Size Estimation},
  booktitle = {Proceedings of the 1996 ACM SIGMOD International Conference on
               Management of Data, Montreal, Quebec, Canada, June 4-6, 1996},
  publisher = {ACM Press},
  year      = {1996},
  pages     = {271-281},
  ee        = {http://doi.acm.org/10.1145/233269.233340, db/conf/sigmod/GangulyGMS96.html},
  crossref  = {DBLP:conf/sigmod/96},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

This paper introduces bifocal sampling, a new technique for estimating the size of an equi-join of two relations. Bifocal sampling classifies tuples in each relation into two groups, sparse and dense, based on the number of tuples with the same join value. Distinct estimation procedures are employed that focus on various combinations for joining tuples (e.g., for estimating the number of joining tuples that are dense in both relations). This combination of estimation procedures overcomes some well-known problems in previous schemes, enabling good estimates with no a priori knowledge about the data distribution. The estimate obtained by the bifocal sampling algorithm is proven to lie with high probability within a small constant factor of the actual join size, regardless of the skew, as long as the join size is Omega(n lg n), for relations consisting of n tuples. The algorithm requires a sample size at most O(sqrt(n) lg n). By contrast, previous algorithms using a sample of similar size may require the join size to be Omega(n sqrt(n)) to guarantee an accurate estimate. Experimental results support the theoretical claims and show that bifocal sampling is practical and effective.

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