The Volcano optimizer generator: extensibility and efficient search

Graefe, Goetz; McKenna, William J.

doi:10.1109/icde.1993.344061

Cited by 244 publications

(225 citation statements)

References 12 publications

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“…We show how to extend the System-R query optimization algorithm [SAC + 79] to perform parametric query optimization with piecewise linear cost functions. We have also extended the Volcano query optimization algorithm [GM93] in a similar fashion. The solution works for an arbitrary number of parameters.…”

Section: Permission To Copy Without Fee All or Part Of This Materials mentioning

confidence: 99%

Parametric Query Optimization for Linear and Piecewise Linear Cost Functions

Hulgeri

Sudarshan

2002

VLDB '02: Proceedings of the 28th International Conference on Very Large Databases

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The cost of a query plan depends on many parameters, such as predicate selectivities and available memory, whose values may not be known at optimization time.Parametric query optimization (PQO) optimizes a query into a number of candidate plans, each optimal for some region of the parameter space.We first propose a solution for the PQO problem for the case when the cost functions are linear in the given parameters. This solution is minimally intrusive in the sense that an existing query optimizer can be used with minor modifications: the solution invokes the conventional query optimizer multiple times, with different parameter values.We then propose a solution for the PQO problem for the case when the cost functions are piecewise-linear in the given parameters. The solution is based on modification of an existing query optimizer. This solution is quite general, since arbitrary cost functions can be approximated to piecewise linear form. Both the solutions work for an arbitrary number of parameters.

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Section: Permission To Copy Without Fee All or Part Of This Materials mentioning

confidence: 99%

Parametric Query Optimization for Linear and Piecewise Linear Cost Functions

Hulgeri

Sudarshan

2002

VLDB '02: Proceedings of the 28th International Conference on Very Large Databases

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“…Spatio-temporal queries are submitted to the query optimizer to generate adequate query execution plans. The query optimizer (e.g., System R [45] and Volcano [16]) picks the best execution plan based on the total cost. ST-Histograms provide the query optimizer with the selectivity estimates used in calculating the total cost.…”

Section: Related Workmentioning

confidence: 99%

Spatio-temporal Histograms

Elmongui

Mokbel

Aref

2005

Advances in Spatial and Temporal Databases

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Abstract. This paper presents a framework for building and continuously maintaining spatio-temporal histograms (ST-Histograms, for short). ST-Histograms are used for selectivity estimation of continuous pipelined query operators. Unlike traditional histograms that examine and/or sample all incoming data tuples, ST-Histograms are built by monitoring the actual selectivities of the outstanding continuous queries. ST-Histograms have three main features: (1) The ST-Histograms are built with (almost) no overhead to the system. We use only feedback (i.e., the actual selectivity) from the existing continuous queries. (2) Rather than wasting system resources in maintaining accurate histograms for the whole spatial space, we only maintain accurate histograms for that part of the space that is relevant to the current existing queries. The rest of the space has less accurate histograms. (3) The ST-Histograms are equipped with a periodicity detection procedure that predicts the future execution of the continuous queries. Hence, the query processing engine can continuously adapt the continuous query pipeline to reflect this prediction. Experimental results based on a real implementation inside a data stream management system show a superior performance of ST-Histograms in terms of providing accurate operator selectivity estimations with no extra overhead.

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“…Specifically, in each iteration, a list of candidate configurations pSchemaList is created by applying all applicable transformations to the current configuration pSchema (line 7). Each of these candidate configurations is evaluated using GetP SchemaCost and the configuration with the smallest cost is selected (lines [8][9][10][11][12][13][14]. This process is repeated until the current configuration can no longer be improved.…”

Section: Searching For the Best Shreddingmentioning

confidence: 99%

“…[13]); although, obviously, the optimizer should reflect the actual costs in the target relational system. As a result, different database systems that use different optimizers and cost-models can easily be connected to LegoDB.…”

Section: Searching For the Best Shreddingmentioning

confidence: 99%