Database self-learning optimization method and device based on flow mirror image

Abstract

The invention provides a database self-learning optimization method based on a flow mirror image, and the method comprises the steps: obtaining a latest database configuration parameter of a to-be-optimized database through a self-adaptive learning method, and sending the latest database configuration parameter to a training database; establishing a database mirror image model consistent with thedatabase model of the database to be optimized, and sending the database mirror image model to the training database; establishing a flow mirror image model consistent with the flow model received bythe to-be-optimized database, and sending the flow mirror image model to the training database; obtaining a training result fed back to the self-training database; updating database configuration parameters of the to-be-optimized database according to a training result of the training database; and repeating the above steps for R times, obtaining the latest database configuration parameter when the TPS throughput of the training database in the R training results is the maximum value, and configuring the latest database configuration parameter as the final database configuration parameter to the to-be-optimized database. For a real traffic scene, the performance of the database can be effectively improved.

Description

technical field [0001] The invention relates to the field of database configuration, in particular to a traffic mirroring-based database self-learning optimization method and a traffic mirroring-based database self-learning optimization device. Background technique [0002] Multi-databases usually have many parameters. For example, MySQL and PostgreSQL have hundreds of parameters, while Oracle has thousands of parameters. These parameters will affect the running process of the database, thus affecting the overall throughput and response time of the database. big impact. For a database application scenario, proper parameter selection can improve performance several times. In order to improve the performance of the database, the company usually needs to hire a professional DBA to tune the database parameters. However, for different application scenarios, different loads, different hardware and operating systems, the optimal parameters required are often different. DBAs usual...

AI Technical Summary

Problems solved by technology

[0008] (1) In the face of hundreds of performance parameters, the DBA needs to analyze the load of the database, provide parameter configurations based on the type of database access SQL and personal experience, and conduct test observations, which requires a lot of time and labor costs

[0009] (2) For the scenario of expected traffic growth, DBA lacks estimated model verification, and usually needs to use experience to estimate. The effect of parameter setting is not only dependent on DBA's experience, but it is difficult to follow the rules of the attempted configuration. Difficult to achieve the best

[0010] (3) In addition, there are disadvantages such as single scene, immutable traffic, dependence on training data, serialization of training, and no support for online optimization.

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