Data table connection sequence selection method based on machine learning

Abstract

The invention discloses a data table connection sequence selection method based on machine learning. The method comprises the following steps: S1, encoding SQL statements, and respectively generating eigenvectors of columns, data tables and connection relationships; s2, designing a vector tree AT according to the eigenvectors of the columns and the data table to generate eigenvectors of a connection tree; s3, designing a partial connection plan model SP to generate a feature vector of a partial connection plan according to the columns, the data table, the connection relationship and the feature vector of the connection tree, and further generating a feature vector of a connection state at the next moment; and S4, constructing a deep reinforcement learning model J according to the feature vector of the connection state at the next moment, and generating an optimal connection sequence of the data table in combination with the partial connection plan model SP and the vector tree AT. The problem that the query efficiency is low due to the fact that an existing query optimizer generates the suboptimal connection sequence of the data table is solved.

Description

technical field [0001] The invention relates to the field of databases, in particular to a machine learning-based data table connection sequence selection method. Background technique [0002] Query operation is the basic operation of relational database, and query efficiency is an important index of database system. So query optimization has become an important research direction in the field of database. Query optimization is to build an execution plan with the least cost, so as to achieve the lowest real query time during query execution and achieve a good user experience. Usually, query optimization includes aspects such as cardinality estimation, cost model, and connection order selection. The effect of query optimization directly affects the performance of database applications. [0003] The most difficult point of the database query optimizer is the problem of selecting the order of multi-table connection. Solving this problem is very complicated and expensive. Be...

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Problems solved by technology

[0005] Aiming at the above-mentioned deficiencies in the prior art, a machine learning-based data table connection sequence selection method provided by the present invention solves the problem of low query efficiency caused by the suboptimal connection sequence of data tables generated by the existing query optimizer

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