A method for automatic optimization of relational database based on cloud platform
An automatic optimization and database technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as poor timeliness, achieve the effect of not wasting resources, reducing misjudgment problems, and improving operational performance stability
Active Publication Date: 2017-07-18
北京中景世纪科技有限公司
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Problems solved by technology
[0008] The present invention is to solve the problem that the performance stability of the database running on the cloud platform is adjusted by human intervention, the timeliness is poor, and at the same time, it relies on the high technical level of the administrator. Hardware index information, timely active optimization suggestions, passive optimization suggestions or automatic passive optimization, an automatic optimization method for relational databases based on cloud platforms to improve the stability of database operation performance in cloud platforms
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[0037] Example: An automatic optimization method for relational database based on cloud platform, see image 3 As shown, including the following steps:
[0038] (1-1) When the workprocess process is started, the fork program is called through the postmaster process, and the workprocess process plug-in code required by the workprocess process is edited according to the database coding agreement. The workprocess process plug-in code includes: SQL plug-in code, .c plug-in code, .control plug-in code and Makefile plug-in code; among them, SQL plug-in code includes workprocess process status table and indicator configuration table; .c plug-in code includes workproecess C code; .control includes control file; Makefile includes configuration information for installing the plug-in;
[0039] (1-2) Create a new project directory under the contrib directory of the database source code directory, and copy and install the workprocess process plug-in code into the project directory;
[0040] (1-3...
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The invention discloses an automatic optimization method of a relational database on the basis of a cloud platform, and belongs to the technical field of cloud platform database optimization. The method can collect the operation state information of a database and the hardware index information of a virtual machine where the database is installed in fixed time, can timely output an initiative optimization suggestion, a passive optimization suggestion and automatic passive optimization, and improves the operation performance stability of the database in the cloud platform. According to a database coding protocol, a workprocess process plugin code required by a workprocess process is edited, an item directory is created under a contrib directory of a database source code directory, and the workprocess process plugin code is copied and installed into the item directory; a workprocess process plugin is created in the database; a database configuration file postgresql.conf is configured, and a dynamic-link library file corresponding to the workprocess process is loaded; and when the database starts, a _PG_init hook program and a workprocess process entry function are called to start the workprocess process. The automatic optimization method can output the initiative optimization suggestion, the passive optimization suggestion and the automatic passive optimization.
Description
Technical field [0001] The invention relates to the technical field of cloud platform database optimization, in particular to a method for automatically optimizing a relational database based on a cloud platform. Background technique [0002] In the cloud platform, in order to achieve the purpose of energy saving and emission reduction, it is necessary to adopt a reasonable combination of virtual machines to reduce the number of physical servers booted in the cloud environment without affecting the business, so as to reduce power consumption. [0003] For example, when matching peak-shifting services, the common hardware resources do not exceed the resources provided by the physical server hardware and the services will not be affected. At the same time, the utilization rate of the physical server can be improved, and the demand for the physical server in the cloud environment can be reduced. See figure 1 As shown, on the time axis P1, the CPU peak-shifting service A S3 is matched ...
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CPCG06F16/217
Inventor 周正中
Owner 北京中景世纪科技有限公司