The goal of multi-objective query optimization (MOQO) is to find query plans that realize a good compromise between conflicting objectives such as minimizing execution time and minimizing monetary fees in a Cloud scenario. A previously proposed exhaustive MOQO algorithm needs hours to optimize even simple TPC-H queries. This is why we propose several approximation schemes for MOQO that generate guaranteed near-optimal plans in seconds where exhaustive optimization takes hours.We integrated all MOQO algorithms into the Postgres optimizer and present experimental results for TPC-H queries; we extended the Postgres cost model and optimize for up to nine conflicting objectives in our experiments. The proposed algorithms are based on a formal analysis of typical cost functions that occur in the context of MOQO. We identify properties that hold for a broad range of objectives and can be exploited for the design of future MOQO algorithms.
Participatory sensing (PS) is becoming a popular data acquisition means for interesting emerging applications. However, as data queries from these applications increase, the sustainability of this platform for multiple concurrent applications is at stake. In this paper 1 , we consider the problem of efficient data acquisition in PS when queries of different types come from different applications. We effectively deal with the issues related to resource constraints, user privacy, data reliability, and uncontrolled mobility. We formulate the problem as multi-query optimization and propose efficient heuristics for its effective solution for the various query types and mixes that enable sustainable sensing. Based on simulations with real and artificial data traces, we found that our heuristic algorithms outperform baseline approaches in a multitude of settings considered.
Query plans offer diverse tradeoffs between conflicting cost metrics such as execution time, energy consumption, or execution fees in a multi-objective scenario. It is convenient for users to choose the desired cost tradeoff in an interactive process, dynamically adding constraints and finally selecting the best plan based on a continuously refined visualization of optimal cost tradeoffs. Multi-objective query optimization (MOQO) algorithms must possess specific properties to support such an interactive process: First, they must be anytime algorithms, generating multiple result plan sets of increasing quality with low latency between consecutive results. Second, they must be incremental, meaning that they avoid regenerating query plans when being invoked several times for the same query but with slightly different user constraints. We present an incremental anytime algorithm for MOQO, analyze its complexity and show that it offers an attractive tradeoff between result update frequency, single invocation time complexity, and amortized time over multiple invocations. Those properties make it suitable to be used within an interactive query optimization process. We evaluate the algorithm in comparison with prior work on TPC-H queries; our implementation is based on the Postgres database management system.
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 Multi-Objective 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.
SkinnerDB uses reinforcement learning for reliable join ordering, exploiting an adaptive processing engine with specialized join algorithms and data structures. It maintains no data statistics and uses no cost or cardinality models. Also, it uses no training workloads nor does it try to link the current query to seemingly similar queries in the past. Instead, it uses reinforcement learning to learn optimal join orders from scratch during the execution of the current query. To that purpose, it divides the execution of a query into many small time slices. Different join orders are tried in different time slices. SkinnerDB merges result tuples generated according to different join orders until a complete query result is obtained. By measuring execution progress per time slice, it identifies promising join orders as execution proceeds. Along with SkinnerDB, we introduce a new quality criterion for query execution strategies. We upper-bound expected execution cost regret, i.e., the expected amount of execution cost wasted due to sub-optimal join order choices. SkinnerDB features multiple execution strategies that are optimized for that criterion. Some of them can be executed on top of existing database systems. For maximal performance, we introduce a customized execution engine, facilitating fast join order switching via specialized multi-way join algorithms and tuple representations. We experimentally compare SkinnerDB’s performance against various baselines, including MonetDB, Postgres, and adaptive processing methods. We consider various benchmarks, including the join order benchmark, TPC-H, and JCC-H, as well as benchmark variants with user-defined functions. Overall, the overheads of reliable join ordering are negligible compared to the performance impact of the occasional, catastrophic join order choice.
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