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Multi-Objective Parametric Query Optimization


Multi-Objective Parametric Query Optimization, illustration

We propose a generalization of the classical database query optimization problem: multi-objective parametric query (MPQ) optimization. MPQ compares alternative processing plans according to multiple execution cost metrics. It also models missing pieces of information on which plan costs depend upon as parameters. Both features are crucial to model query processing on modern data processing platforms.

MPQ generalizes previously proposed query optimization variants, such as multi-objective query optimization, parametric query optimization, and traditional query optimization. We show, however, that the MPQ problem has different properties than prior variants and solving it requires novel methods. We present an algorithm that solves the MPQ problem and finds, for a given query, the set of all relevant query plans. This set contains all plans that realize optimal execution cost tradeoffs for any combination of parameter values. Our algorithm is based on dynamic programming and recursively constructs relevant query plans by combining relevant plans for query parts. We assume that all plan execution cost functions are piecewise-linear in the parameters. We use linear programming to compare alternative plans and to identify plans that are not relevant. We present a complexity analysis of our algorithm and experimentally evaluate its performance.

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Introduction

* 1.1. Context

The goal of the database query optimization is to map a query (describing the data to generate) to the optimal query plan (describing how to generate the data). Query optimization is a long standing research area in the database field dating back to the 1970s.14 The original query optimization problem model has been motivated by the capabilities of data processing systems at that time. However, there have been fundamental advances in data processing techniques and systems in the meantime. Hence the original problem model is not sufficiently expressive to capture all relevant aspects of modern data processing systems. In this paper, we propose an extension of the classical query optimization problem model and a corresponding optimization algorithm.

Alternative query plans are compared according to their execution cost (e.g., execution time) in query optimization. Query optimization variants can be classified according to how they model the execution cost of a single query plan. Traditional query optimization14 models the cost of a query plan as scalar cost value c ∈ ℝ. This implies that query plans are compared according to one single cost metric. It also implies that all information required to produce cost estimates is available to the query optimizer. The goal in classical query optimization is to find a query plan with minimal execution cost.


 

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