CN116881230A - Automatic relational database optimization method based on cloud platform - Google Patents
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Abstract
The invention relates to the field of databases, in particular to a cloud platform-based automatic relational database optimization method, which comprises the following steps: s1, a monitoring node for monitoring and collecting user query related index information is established on a cloud platform; s2, tracking, analyzing and recording user query requests and related index information in real time; s3, classifying according to the type and the frequency of the user request, and generating a preset query strategy matched with the type of query; s4, applying the generated query strategy to a target database, and recording an execution result and related index information; s5, automatically updating and optimizing preset query strategies of the corresponding types of queries, selecting the optimal strategy and applying the optimal strategy to the subsequent real-time query requests. The invention combines the machine learning technology to automatically update and optimize the preset query strategy, thereby achieving the purpose of automatically optimizing the query strategy.
Description
Technical Field
The invention relates to the technical field of databases, in particular to an automatic relational database optimization method based on a cloud platform.
Background
Conventional relational databases present performance bottlenecks in the face of large-scale data and complex queries, resulting in excessive query response times and increased system load. In order to improve the performance of relational databases, many methods of manual tuning, index design, physical storage, etc. have been proposed. However, these methods all require manual intervention and do not meet the real-time automated optimization requirements.
The Chinese patent with the bulletin number of CN104503974A discloses an automatic optimizing method of a relational database based on a cloud platform, which can collect the running state information of the database and the hardware index information of a virtual machine where the database is located at regular time, and timely actively optimize suggestions, passively optimize suggestions or automatically passively optimize, thereby improving the running performance stability of the database in the cloud platform. The method can output active optimization suggestions, output passive optimization suggestions and output automatic passive optimization.
However, the above disclosed solution has the following disadvantages: although the utilization rate of the physical device can be improved, the query strategy cannot be automatically optimized to improve the query efficiency of the user.
Disclosure of Invention
The invention aims to provide a method for automatically optimizing a relational database based on a cloud platform, aiming at the problem that the cloud platform in the background technology cannot automatically optimize a query strategy.
On one hand, the invention provides a cloud platform-based automatic relational database optimization method, which comprises the following steps:
s1, a monitoring node for monitoring and collecting user query related index information is established on a cloud platform;
s2, tracking, analyzing and recording user query requests and related index information in real time;
s3, sorting according to the type and the frequency of the user request, generating a preset query strategy matched with the type of query, sorting from fast to slow according to the query time corresponding to the historical query strategy, and selecting the query strategy with the forefront sorting by default;
s4, applying the generated query strategy to a target database, and recording an execution result and related index information;
s5, automatically updating and optimizing preset query strategies of the corresponding type of query according to the historical query mode and the related index information based on a learning algorithm, selecting an optimal strategy and applying the optimal strategy to a subsequent real-time query request.
Preferably, in S1, the user query related index information includes a query type, a query preference, a query frequency, a query time period, and a system load.
Preferably, in S2, the type and the preference direction of the user query are obtained by analysis, and the content range of the query is approximately queried in a specific time period, and the query frequency in the specific time period is obtained.
Preferably, in S3, the query policy includes keyword matching based, semantic analysis based, user history based, and collaborative filtering based.
Preferably, based on the keyword matching one or more keywords entered by the user, the system matches documents in the relational database that contain similar content based on the keywords; by performing semantic analysis on the user input based on the semantic analysis, understanding the intention and the context behind the user input, so that related resources are recommended better; recommending relevant resources according to past search behaviors and preferences of a user based on historical behaviors of the user, establishing a personalized model by collecting and analyzing user data, and providing customized search results to the user; the relevant resources are recommended based on collaborative filtering using information shared among multiple users and other people who have similar characteristics and interests as the current target user.
Preferably, in S4, the relevant index information is the adoption condition of the query result, and the adoption condition is judged by the user looking up time, cursor position and copy condition, and the adoption condition includes the adoption and non-adoption of two conditions.
Preferably, in S5, the method for selecting the optimal strategy includes the steps of: s51, selecting an optimal query strategy according to query classification; s52, judging the current system load, if the system load is greater than 80%, only executing the query strategy with the fastest history query and outputting a query result; s53, if the system load is less than or equal to 80%, firstly executing the query strategy with the fastest historical query and outputting the query result, and then sequentially executing other query strategies; s54, comparing the query time of the multiple query strategies, selecting the query strategy with the fastest query time, re-determining the optimal query strategy of the type of query, and re-ordering the query strategies of the type of query.
On the other hand, the invention provides a cloud platform-based relational database automatic optimization system which is used for executing the cloud platform-based relational database automatic optimization method and comprises a cloud platform, a database, a monitoring module, an analysis module, a query classification module, a query strategy module and a query strategy updating module; the cloud platform is connected with the database data transmission; the monitoring module is arranged in the cloud platform and used for monitoring and collecting related index information; the analysis module is used for tracking, analyzing and recording user requests and related index information; the query classification module is used for classifying the type and the frequency of the user request; the query strategy module is used for generating a query strategy sequence, executing the current optimal query strategy and outputting a result; the query strategy updating module is used for updating the query strategy ordering condition in real time, selecting the optimal strategy and applying the optimal strategy to the subsequent real-time query request.
Compared with the prior art, the invention has the following beneficial technical effects: in order to adapt to the change of the user query requirement and the change of the database performance, a learning algorithm is adopted to learn and iterate the historical query mode and the related index information, the execution results of different query strategies and the related index information are analyzed, and the preset query strategy is automatically updated and optimized by combining a machine learning technology, so that the aim of automatically optimizing the query strategy is fulfilled.
Drawings
FIG. 1 is a workflow diagram of one embodiment of the present invention;
FIG. 2 is a flow chart of a method of selecting an optimal strategy;
fig. 3 is a schematic structural diagram of an automatic relational database optimization system based on a cloud platform.
Detailed Description
Examples
As shown in fig. 1, the method for automatically optimizing the relational database based on the cloud platform provided by the invention comprises the following steps:
s1, a monitoring node for monitoring and collecting user query related index information is established on a cloud platform; the user query related index information includes query type, query preference, query frequency, query time period, and system load.
S2, tracking, analyzing and recording user query requests and related index information in real time; the analysis obtains the type and the preference direction of the user query, the general query content range in a specific time period and the query frequency in the specific time period.
S3, sorting according to the type and the frequency of the user request, generating a preset query strategy matched with the type of query, sorting from fast to slow according to the query time corresponding to the historical query strategy, and selecting the query strategy with the forefront sorting by default; query policies include keyword matching based, semantic analysis based, user history based behavior, and collaborative filtering based.
S4, applying the generated query strategy to a target database, and recording an execution result and related index information; the related index information is the adoption condition of the query result, the adoption condition is judged by checking time, cursor position and copying condition of a user, and the adoption condition comprises the adoption condition and the non-adoption condition.
S5, automatically updating and optimizing preset query strategies of the corresponding type of query according to the historical query mode and the related index information based on a learning algorithm, selecting an optimal strategy and applying the optimal strategy to a subsequent real-time query request.
Working principle: and the cloud platform is adopted as a basic framework, and the powerful computing and storage resources and the load balancing capability of the cloud platform are utilized to realize the automatic optimization of the relational database. And the monitoring nodes are installed on the cloud platform kernel, and data analysis and processing are performed by collecting user requests and related index information. By tracking, analyzing and recording user requests and related index information in real time, the performance influence condition of different types, frequencies and other characteristics on operation in each specific scene can be known. And then generating a matched and highly targeted query strategy according to the characteristics, and applying the query strategy to a target database. Meanwhile, the system can continuously monitor various index information in the running process, record the execution result and automatically update the query strategy in real time.
In this embodiment, in order to adapt to the change of the user query requirement and the change of the database performance, a learning algorithm is adopted to learn and iterate the historical query mode and the related index information, and the execution result and the related index information of different query strategies are analyzed, and the preset query strategy is automatically updated and optimized by combining with the machine learning technology, so that the purpose of automatically optimizing the query strategy is achieved.
Examples
As shown in fig. 1, in the automatic optimization method for a relational database based on a cloud platform provided by the invention, compared with the first embodiment, the system matches one or more keywords input by a user based on keyword matching, and the system matches files containing similar contents in the relational database according to the keywords; by performing semantic analysis on the user input based on the semantic analysis, understanding the intention and the context behind the user input, related resources are recommended better, such as entities, trigger events and the like can be extracted by utilizing natural language processing technology; recommending relevant resources according to past search behaviors and preferences of a user based on historical behaviors of the user, establishing a personalized model by collecting and analyzing user data, and providing customized search results to the user; the relevant resources are recommended based on collaborative filtering using information shared among multiple users and other people who have similar characteristics and interests as the current target user.
In this embodiment, when using different types of query strategies, a trade-off between accuracy and recall may occur, such as increasing accuracy, which typically results in a decrease in recall and vice versa. Therefore, in practical application, policy selection and adjustment can be performed according to specific requirements and user feedback.
Examples
As shown in fig. 2, in the automatic relational database optimization method based on the cloud platform provided by the invention, compared with the first embodiment, in S5, the method for selecting the optimal strategy includes the following steps: s51, selecting an optimal query strategy according to query classification; s52, judging the current system load, if the system load is greater than 80%, only executing the query strategy with the fastest history query and outputting a query result; s53, if the system load is less than or equal to 80%, firstly executing the query strategy with the fastest historical query and outputting the query result, and then sequentially executing other query strategies; s54, comparing the query time of the multiple query strategies, selecting the query strategy with the fastest query time, re-determining the optimal query strategy of the type of query, and re-ordering the query strategies of the type of query.
In this embodiment, according to the system load 80% being the limit, if the system load is greater than 80%, it is indicated that the current system load is greater, at this time, it needs to be ensured that all users can obtain the query result faster, only the historical optimal query policy can be executed, and when the system load is less than or equal to 80%, it is indicated that there is a greater margin in the current system operation, multiple query policies corresponding to the same query are conditionally executed, and the query policies corresponding to different types of queries are adjusted in real time compared with the result, so as to provide a more efficient and stable automatic optimization method.
Examples
As shown in fig. 3, the automatic optimizing system of the relational database based on the cloud platform based on the embodiment of the automatic optimizing method of the relational database based on the cloud platform comprises a cloud platform, a database, a monitoring module, an analyzing module, a query classifying module, a query strategy module and a query strategy updating module; the cloud platform is connected with the database data transmission; the monitoring module is arranged in the cloud platform and used for monitoring and collecting related index information; the analysis module is used for tracking, analyzing and recording user requests and related index information; the query classification module is used for classifying the type and the frequency of the user request; the query strategy module is used for generating a query strategy sequence, executing the current optimal query strategy and outputting a result; the query strategy updating module is used for updating the query strategy ordering condition in real time, selecting the optimal strategy and applying the optimal strategy to the subsequent real-time query request.
In this embodiment, the aim of automatically optimizing the query strategy is achieved by analyzing the execution results and related index information of different query strategies and automatically updating and optimizing the preset query strategy by combining the machine learning technology.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.
Claims (8)
1. The automatic relational database optimizing method based on the cloud platform is characterized by comprising the following steps of:
s1, a monitoring node for monitoring and collecting user query related index information is established on a cloud platform;
s2, tracking, analyzing and recording user query requests and related index information in real time;
s3, sorting according to the type and the frequency of the user request, generating a preset query strategy matched with the type of query, sorting from fast to slow according to the query time corresponding to the historical query strategy, and selecting the query strategy with the forefront sorting by default;
s4, applying the generated query strategy to a target database, and recording an execution result and related index information;
s5, automatically updating and optimizing preset query strategies of the corresponding type of query according to the historical query mode and the related index information based on a learning algorithm, selecting an optimal strategy and applying the optimal strategy to a subsequent real-time query request.
2. The automatic optimization method of a relational database based on a cloud platform as recited in claim 1, wherein in S1, the user query related index information includes a query type, a query preference, a query frequency, a query time period, and a system load.
3. The automatic optimization method of a relational database based on a cloud platform according to claim 1, wherein in S2, the type and the preference direction of the user query are analyzed and obtained, and the content range of the query is approximately queried in a specific time period, and the query frequency in the specific time period is obtained.
4. The method for automatically optimizing a relational database based on a cloud platform as recited in claim 1, wherein in S3, the query strategy comprises keyword matching based, semantic analysis based, user history behavior based, and collaborative filtering based.
5. The automatic optimization method of a relational database based on a cloud platform as claimed in claim 4, wherein the system matches the documents containing similar contents in the relational database based on the keywords matching one or more keywords inputted by the user; by performing semantic analysis on the user input based on the semantic analysis, understanding the intention and the context behind the user input, so that related resources are recommended better; recommending relevant resources according to past search behaviors and preferences of a user based on historical behaviors of the user, establishing a personalized model by collecting and analyzing user data, and providing customized search results to the user; the relevant resources are recommended based on collaborative filtering using information shared among multiple users and other people who have similar characteristics and interests as the current target user.
6. The automatic optimization method of the relational database based on the cloud platform according to claim 1, wherein in S4, the relevant index information is the adoption of the query result, the adoption is judged by the user checking time, the cursor position and the duplication, and the adoption includes the adoption and the non-adoption.
7. The automatic optimization method of a relational database based on a cloud platform as claimed in claim 1, wherein in S5, the selecting the best strategy method comprises the steps of: s51, selecting an optimal query strategy according to query classification; s52, judging the current system load, if the system load is greater than 80%, only executing the query strategy with the fastest history query and outputting a query result; s53, if the system load is less than or equal to 80%, firstly executing the query strategy with the fastest historical query and outputting the query result, and then sequentially executing other query strategies; s54, comparing the query time of the multiple query strategies, selecting the query strategy with the fastest query time, re-determining the optimal query strategy of the type of query, and re-ordering the query strategies of the type of query.
8. The automatic optimizing system of the relational database based on the cloud platform is used for executing the automatic optimizing method of the relational database based on the cloud platform, and is characterized by comprising a cloud platform, a database, a monitoring module, an analyzing module, a query classifying module, a query strategy module and a query strategy updating module; the cloud platform is connected with the database data transmission; the monitoring module is arranged in the cloud platform and used for monitoring and collecting related index information; the analysis module is used for tracking, analyzing and recording user requests and related index information; the query classification module is used for classifying the type and the frequency of the user request; the query strategy module is used for generating a query strategy sequence, executing the current optimal query strategy and outputting a result; the query strategy updating module is used for updating the query strategy ordering condition in real time, selecting the optimal strategy and applying the optimal strategy to the subsequent real-time query request.
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