Database materialized view construction system and method and system creation method

Abstract

The embodiment of the invention discloses a database materialized view construction system and method and a system creation method. The system comprises a query load preprocessing module, an offline training module and an online recommendation module. The query load preprocessing module is used for preprocessing an input query load to obtain sub-query representatives; the offline training module is used for training a preset deep learning model and a reinforcement learning model by querying load historical data; the online recommendation module is used for predicting the query cost of a new query obtained after the original query corresponding to each sub-query representation is subjected to view rewriting by using the materialized view corresponding to the sub-query representation througha deep learning model to obtain a newly queried query representation and obtaining the optimal sub-query with the lowest query cost and the corresponding optimal materialized view through the reinforcement learning model. According to the scheme of the embodiment, the redundancy cost of batch query is reduced to a large extent, and convergence of the materialized view approximate optimization process is achieved.

Description

technical field [0001] This article relates to information retrieval technology, especially a database materialized view construction system, method and system creation method. Background technique [0002] In the field of database research, there are many ways to optimize batch queries, one of which is to use materialized view technology to reduce the redundancy cost of batch queries. Specifically, in an online analytical processing (OLAP) system, there are identical subqueries among analytical SQL query statements, and these common subqueries cause redundant query costs. In order to solve such problems, an optimization method is to construct corresponding views for common subqueries, and then reduce redundant query costs by storing the query results of the views and then reusing them. In other words, multiple reuse of one query can greatly improve query efficiency and reduce unnecessary query costs. [0003] However, most of the existing methods still need to rely on the...

AI Technical Summary

Problems solved by technology

Among them, the optimization object of the first part is determined by means of probability calculation, which will cause the overall optimization goal to oscillate back and forth between two different optimization steps and fail to converge

Therefore, the difficulty here is how to solve the problem that this approximate optimization process cannot converge

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