Progressive Parametric Query Optimization

Bizarro, Pedro; Bruno, Nicolas; DeWitt, David J.

doi:10.1109/tkde.2008.160

Cited by 47 publications

(34 citation statements)

References 9 publications

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“…Embedded SQL queries are a classical use case for PQ [17,7]: to avoid query optimization overhead at run time, all potentially relevant query plans are calculated in advance for a given query template. Parameters model the selectivity of unspecified predicates or the amount of buffer space that is available at run time.…”

Section: Scenariomentioning

confidence: 99%

“…The cost functions depend on parameters that represent for instance predicate selectivities. The goal in PQ is usually to generate a plan set that contains one optimal plan for each possible parameter value combination [13,17,18,7]. Many approaches to PQ are based on parameter space decomposition [13,17,18,12,7].…”

Section: Related Workmentioning

confidence: 99%

“…The optimization goal is to find a set of query plans that are all Pareto-optimal, meaning that no other plan has better cost according to all cost metrics at the same time. Parametric Query Optimization (PQ) [13,17,7] generalizes the classical model in a different way and associates each query plan with a cost function c : R n → R describing the cost of the plan as function of multiple parameters whose values are not known at optimization time. The optimization goal is to find a plan set that contains an optimal plan for each possible combination of parameter values.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective parametric query optimization

Trummer

2017

Commun. ACM

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Classical query optimization compares query plans according to one cost metric and associates each plan with a constant cost value. In this paper, we introduce the MultiObjective Parametric Query Optimization (MPQ) problem where query plans are compared according to multiple cost metrics and the cost of a given plan according to a given metric is modeled as a function that depends on multiple parameters. The cost metrics may for instance include execution time or monetary fees; a parameter may represent the selectivity of a query predicate that is unspecified at optimization time.MPQ generalizes parametric query optimization (which allows multiple parameters but only one cost metric) and multi-objective query optimization (which allows multiple cost metrics but no parameters). We formally analyze the novel MPQ problem and show why existing algorithms are inapplicable. We present a generic algorithm for MPQ and a specialized version for MPQ with piecewise-linear plan cost functions. We prove that both algorithms find all relevant query plans and experimentally evaluate the performance of our second algorithm in a Cloud computing scenario.

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Section: Scenariomentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-objective parametric query optimization

Trummer

2017

Commun. ACM

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“…In [19], and later in [4], validity ranges are computed for each sub-plan of a query execution plan. The best plan is first chosen based on single-point estimates.…”

Section: Related Workmentioning

confidence: 99%

Handling Estimation Inaccuracy in Query Optimization

Moumen

Morvan

Hameurlain

2016

Web Technologies and Applications

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract. Cost-based Optimizers choose query execution plans using a cost model. The latter relies on the accuracy of estimated statistics. Unfortunately, compile-time estimates often differ significantly from runtime values, leading to a suboptimal plan choices. In this paper, we propose a compile-time strategy, wherein the optimization process is fully aware of the estimation inaccuracy. This is ensured by the use of intervals of estimates rather than single-point estimates of error-prone parameters. These intervals serve to identify plans that provide stable performance in several run-time conditions, so called robust. Our strategy relies on a probabilistic approach to decide which plan to choose to start the execution. Our experiments show that our proposal allows a considerable improvement of the ability of a query optimizer to produce a robust execution plan in case of large estimation errors.

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“…Adding to these challenges is the lack of standardized approaches for capturing and measurement of Performance requirements in application systems. Hence, given the criticality and limitations, we believe that looking for innovative approaches towards performance gains in the form of totally new ideas or improvements proposed to the existing ideas [1,2,3,4] that can bring in substantial improvements will surely be a differentiating factor to mitigate the "Performance" issue in software systems addressing the needs of larger audiences of IT industry.…”

Section: A Motivationsmentioning

confidence: 99%

A Model for Building Dynamic Indexes & Storage and Re-use of Optimal Query Plans Generated thru Progressive Optimization (POP)

Vobugari¹,

Somayajulu²,

Subraya³

2012

IJMLC

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Abstract-Query Optimization is at the core for contribution towards performance improvements in application systems. A lot of ideas have been proposed towards Query Optimization and there is lot of On-going research happening in this area. Virtually every commercial query optimizer chooses the best plan for a query using a cost model which is based on cardinality estimation. If cardinality estimation is inaccurate, then this may result in optimizer to choose a sub-optimal plan. But once the optimizer chooses an optimal plan for execution based on the approach of POP, the need for generating an optimal plan for subsequent execution of the same query at a later point in time can be minimized/reduced/exempted by storing the execution plan. This paper proposes a Model for building Dynamic Indexes & Storage and Re-Use of Optimal Query plans generated thru Progressive Optimization (POP) for performance gains. This approach is an extension to the work implemented in "Robust Query Processing through Progressive Optimization". This paper proposes a model to build Learning system within the database to analyze the stream of incoming queries and project viable indexes as against the initial indexes created by the Administrator and also store and re-use of Optimal Query Plans generated thru Progressive Query Optimization (POP).Index Terms-QoS, PoP.

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Progressive Parametric Query Optimization

Cited by 47 publications

References 9 publications

Multi-objective parametric query optimization

Multi-objective parametric query optimization

Handling Estimation Inaccuracy in Query Optimization

A Model for Building Dynamic Indexes & Storage and Re-use of Optimal Query Plans Generated thru Progressive Optimization (POP)

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