A query plan result dataset fast generation method

By dividing the data range in the database system and building an overhead prediction model, the problems of high cost and low accuracy in generating query plan evaluation result datasets are solved, enabling fast and accurate generation of simulated data and supporting efficient training and optimization of learning-based query optimizers.

CN121188092BActive Publication Date: 2026-03-17ZHEJIANG UNIV
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Patent Information

Application Number
CN202511740589.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-25
Publication Date
2026-03-17
Estimated Expiration
2045-11-25

AI Technical Summary

Technical Problem

Existing technologies suffer from high computational costs and poor estimation accuracy when generating query plan evaluation result datasets. In particular, when dealing with the nonlinear and uncertain overhead of machine learning models, traditional methods cannot quickly generate massive amounts of evaluation data, hindering the development of learning-based query optimizers.

Method used

By dividing the full dataset into multiple sample data intervals, recording the total time cost and data characteristics of each interval, constructing randomly combined data intervals, and using a cost prediction model to estimate query execution results, a simulated dataset is generated, including specific feature fitting models for text, image, and video data, adapting to different system configurations.

Benefits of technology

It enables the low-cost and efficient generation of large amounts of simulated data that can be used to evaluate query plans, improves estimation accuracy, supports rapid training and optimization of learning optimizers, and is suitable for query optimization scenarios of various machine learning models.

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Abstract

The application discloses a kind of query plan result dataset fast generation method, this method is first in a plurality of representative data interval actual execution query, record its total time overhead, data characteristics and the processing result of each data;Then based on these samples, construct the prediction model about data interval characteristics and total time overhead. Again by random combination generates a plurality of different data intervals, and then estimates the execution result of query on these intervals, to realize the fast low-cost generation of a large number of available data.The method of the application can greatly reduce the time-consuming of calculation while achieving similar effects to actual query execution, can produce a large number of available data in a short time, has the advantages of strong universality, simple to use and the like.In addition, the application introduces data characteristics (such as text length) and system environment configuration to refine time overhead estimation, and can generate large-scale high-quality data sets for training learning-based query optimizer.
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