Database query plan optimization system and method

Abstract

The invention relates to database technologies and specifically relates to a database query plan optimization system and method. The system comprises a database, a statistical database, a query optimizer, a query assembly and a query plan optimization assembly, wherein the query plan optimization assembly comprises a monitoring component and an adjusting component; the monitoring component is used for detecting the changes of the database; the adjusting component is used for submitting a query based on a middle process according to feedback information of the monitoring component and completing the updating of the statistical database; and the query optimizer is used for constructing a query plan according to the statistical database and finishing the query plan by virtue of the query assembly. The database query plan optimization system and method have the beneficial effects of reducing the cost for executing the plan, avoiding time-consuming data scanning, timely updating statistical data, preventing query contents exceeding a known range, improving accuracy of cardinality estimation so as to improve query efficiency, allowing the optimized query plan to be executed, and ensuring the reliability, accuracy and high efficiency of the query plan.

Description

technical field [0001] The invention relates to database technology, in particular to a system and method for optimizing database query plan. Background technique [0002] In current computer applications, especially in aspects such as libraries and financial industries, the amount of various data is increasing rapidly. In the face of massive data, how to find the optimal result in the database in the shortest time is the focus of data query. [0003] In a relational database system, the optimizer is a module that constructs a query plan, mainly used to determine how the query is executed, and the query cardinality estimation plays an important role in the query optimization process: inaccurate cardinality estimation can lead to suboptimal The plan is selected, and the execution efficiency between the suboptimal and the optimal plan may differ by several orders of magnitude. Due to the large amount of data in the database, the query process works on the data itself, which ...

AI Technical Summary

Problems solved by technology

While the database records are being refreshed, the continuously updated statistical data is used to optimize the query plan; however, the ascending and descending operations of some stored data are often faster than the updating of the statistical data. At this time, the query content exceeds the known range, that is, the query The distribution information of the content has not been updated to the statistical data, and the inaccurate cardinality estimation makes the query inefficient when performing the query task

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