The query execution engine in Tandem's new ServerWare SQL product

Celis, Pedro; Zeller, Hans

doi:10.1109/pdis.1996.568690

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Commercial systems from Microsoft [Gra96] and Tandem [Cel96] are based on Cascades. They include techniques similar to those we present here, but to our knowledge these are the first analyses and testing of those techniques.…”

Section: Previous Workmentioning

confidence: 99%

Exploiting upper and lower bounds in top-down query optimization

Shapiro

Maier²,

Benninghoff³

et al.

Proceedings 2001 International Database Engineering and Applications Symposium

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Section: Previous Workmentioning

confidence: 99%

Exploiting upper and lower bounds in top-down query optimization

Shapiro

Maier²,

Benninghoff³

et al.

Proceedings 2001 International Database Engineering and Applications Symposium

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“…Many modern optimizers use a transformation rule-based framework including industrial query optimizers such as IBM Starbust [18], Microsoft SQL Server [10], Oracle [2], Tandem's NonStopSQL [3] as well as academic prototypes such as the Volcano optimizer [11].…”

Section: VIIImentioning

confidence: 99%

Rule profiling for query optimizers and their implications

Chaudhuri

Giakoumakis

Narasayya

et al. 2010

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)

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Many modern optimizers use a transformation rule based framework. While there has been a lot of work on identifying new transformation rules, there has been little work focused on empirically evaluating the effectiveness of these transformation rules. In this paper we present the results of an empirical study of "profiling" transformation rules in Microsoft SQL Server using a diverse set of real world and benchmark query workloads. We also discuss the implications of these results for designing and testing query optimizers.

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“…In this section we describe the main features of the Cascades Optimization Framework, developed in the mid-nineties and used as the foundation for both industrial (e.g., Tandem's NonStop SQL [5] and Microsoft SQL Server [10]) and academic (e.g., Columbia [2]) query optimizers. Rather than providing a detailed description of all the features in Cascades, we will give a high level overview of the framework followed by a focused description of the components that are relevant to this work (see [9,13] for more details).…”

Section: The Cascades Frameworkmentioning

confidence: 99%

Configuration-parametric query optimization for physical design tuning

Bruno

Nehme

2008

Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data

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Automated physical design tuning for database systems has recently become an active area of research and development. Existing tuning tools explore the space of feasible solutions by repeatedly optimizing queries in the input workload for several candidate configurations. This general approach, while scalable, often results in tuning sessions waiting for results from the query optimizer over 90% of the time. In this paper we introduce a novel approach, called Configuration-Parametric Query Optimization, that drastically improves the performance of current tuning tools. By issuing a single optimization call per query, we are able to generate a compact representation of the optimization space that can then produce very efficiently execution plans for the input query under arbitrary configurations. Our experiments show that our technique speedsup query optimization by 30x to over 450x with virtually no loss in quality, and effectively eliminates the optimization bottleneck in existing tuning tools. Our techniques open the door for new, more sophisticated optimization strategies by eliminating the main bottleneck of current tuning tools.

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The query execution engine in Tandem's new ServerWare SQL product

Cited by 4 publications

References 7 publications

Exploiting upper and lower bounds in top-down query optimization

Exploiting upper and lower bounds in top-down query optimization

Rule profiling for query optimizers and their implications

Configuration-parametric query optimization for physical design tuning

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