Query rewriting method of database

A query rewriting and database technology, applied in the field of information retrieval, can solve problems such as reducing overhead, achieve the effect of realizing rewriting frequency and improving query rewriting efficiency

Pending Publication Date: 2021-10-19
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Therefore, the third challenge is how to estimate the overhead reduction after rewriting

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  • Query rewriting method of database
  • Query rewriting method of database
  • Query rewriting method of database

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Embodiment Construction

[0013] The query rewriting method of database data proposed by the present invention uses a strategy tree to represent a large number of equivalent queries and corresponding rewriting sequences of any query statement, and selects a high-yield rewriting sequence on the strategy tree based on the Monte Carlo tree search algorithm , to achieve a balance between query overhead and rewriting frequency, use the deep attention network to learn the relationship between the current query and rewriting rules and access data, and use deep learning to fit the overall revenue of the current query based on the query characteristics and the extracted relationship , using the rewriting revenue estimation network, an optimal algorithm is used to select multiple nodes with no ancestor-descendant relationship and the highest total revenue on the strategy tree, and the strategy tree is expanded in parallel to greatly improve the efficiency of query rewriting.

[0014] The query rewriting method of...

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Abstract

The invention belongs to the technical field of information retrieval, and relates to a query rewriting method of a database. According to the method, firstly, a strategy tree is used for representing a large number of equivalent queries of any query statement and a corresponding rewriting sequence, secondly, a high-yield rewriting sequence is selected on the strategy tree based on a Monte Carlo tree search algorithm, and balance of query overhead and rewriting frequency is achieved; in order to more accurately estimate the overall income of the query after rewriting, the deep attention network is utilized to learn the incidence relation between the current query and the rewriting rule and the access data, and deep learning is utilized to fit the overall income of the current query according to the query features and the extracted incidence relation. And finally, aiming at a complex query with a large-scale strategy tree, the invention provides a parallel node selection algorithm, an optimal algorithm is utilized on the strategy tree to select a plurality of nodes which have no ancestor-descendant relationship and have the highest total income, and the strategy tree is expanded in parallel, so that the query rewriting efficiency is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of information retrieval, and relates to a database query rewriting method, in particular to a query rewriting method based on deep learning and Monte Carlo tree search. Background technique [0002] Query rewriting can improve the performance of slow queries (eg, redundant operators) by several orders of magnitude by selecting rewriting operations reasonably. As a fundamental problem in query optimization, query rewriting aims to convert Structured Query Language (SQL) into an equivalent query, but with higher performance. Specifically, query rewriting performs logically equivalent transformations on query statements (such as deleting redundant operators and improving logical subqueries), so as to ensure that (1) the rewritten query is equivalent to the original query; (2) Rewritten query execution time decreased. [0003] Query rewriting is a non-deterministic problem of polynomial complexity, and existi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2453G06F16/22G06N3/04G06N3/08
CPCG06F16/24534G06F16/2246G06N3/04G06N3/08
Inventor 李国良周煊赫
Owner TSINGHUA UNIV
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