Analyzing plan diagrams of database query optimizers
VLDB '05 Proceedings of the 31st international conference on Very large data bases
QAGen: generating query-aware test databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Parametric query optimization for linear and piecewise linear cost functions
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Using the optimizer to generate an effective regression suite: a first step
Proceedings of the Third International Workshop on Testing Database Systems
Plan space analysis: an early warning system to detect plan regressions in cost-based optimizers
Proceedings of the Fourth International Workshop on Testing Database Systems
Parallel data generation for performance analysis of large, complex RDBMS
Proceedings of the Fourth International Workshop on Testing Database Systems
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Effective design and testing of database engines and applications is predicated on the ability to easily construct alternative scenarios with regard to the database contents. A limiting factor, however, is that the time and/or space overheads incurred in creating and maintaining these databases may render it infeasible to model the desired scenarios. In this paper, we present CODD, a lucid graphical tool that attempts to alleviate these difficulties through the construction of "dataless databases". Specifically, CODD implements a unified visual interface through which databases with the desired meta-data characteristics can be efficiently simulated without persistently generating and/or storing their contents. Metadata validation is incorporated to ensure that the simulated database is both legal and consistent. CODD is currently operational on a rich suite of popular database engines, and introduces two additional facets of relevance to test teams: First, it supports a cost-based database scaling model, in addition to the size-based scaling models that have long been in vogue. Second, it provides for largely automated meta-data transfer across different engines, facilitating the comparative study of systems. We showcase here the ability of CODD to elegantly simulate a variety of testing scenarios ranging from legacy applications to Big Data environments.