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Quickly Generating Billion-Record
Synthetic Databases
January 1, 1994
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BibTex
Authors

Jim Gray

Ken Baclawski

Prakash Sundaresan

Susanne Englert
Publication Type
Inproceedings
Publisher
Association for Computing Machinery, Inc.
Copyright © 1994 by the Association for Computing Machinery, Inc. Permission to make
digital or hard copies of part or all of this work for personal or classroom use is granted without
fee provided that copies are not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. Copyrights for components of this
work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy
otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific
permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212)
869-0481, or permissions@acm.org. The definitive version of this paper can be found at
ACM’s Digital Library –http://www.acm.org/dl/.

Abstract

Related Info
Abstract
Evaluating database system performance often requires generating synthetic databases – ones
having certain statistical properties but filled with dummy information. When evaluating
different database designs, it is often necessary to generate several databases and evaluate each
design. As database sizes grow to terabytes, generation often takes longer than evaluation. This
paper presents several database generation techniques. In particular it discusses: (1) Parallelism
to get generation speedup and scaleup. (2) Congruential generators to get dense unique uniform
distributions. (3) Special-case discrete logarithms to generate indices concurrent to the base table
generation. (4) Modification of (2) to get exponential, normal, and self-similar distributions. The
discussion is in terms of generating billion-record SQL databases using C programs running on a
shared-nothing computer system consisting of a hundred processors, with a thousand discs. The
ideas apply to smaller databases, but large databases present the more difficult problems.
Related Info
Related Files
 syntheticdatagen.doc
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