Method, system, device, and storage medium for improving data query efficiency and stability
By setting a timing task framework in a distributed database, identifying the difference in node data volume and performing cache optimization, the problem of full data query performance stability in distributed databases is solved, and faster query speed and higher stability are achieved.
CN114168627BActive Publication Date: 2025-08-05四川启睿克科技有限公司
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Patent Information
- Application Number
- CN202111543349.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-12-16
- Publication Date
- 2025-08-05
- Estimated Expiration
- 2041-12-16
AI Technical Summary
Technical Problem
The difference in data volume between different physical nodes in a distributed database leads to a bottleneck in the performance stability of the entire data query, affecting the query efficiency.
Method used
By setting a timing task framework, we can identify the data volume differences of each physical node, calculate standard scores, identify the difference in query efficiency stability, and determine the data cache optimization strategy based on the score, especially for nodes with high degree of data volume discreteness to cache optimization in advance.
Benefits of technology
Without affecting data consistency and integrity, shorten the return time of the full data and improve query efficiency and stability.
✦ Generated by Eureka AI based on patent content.
Abstract
The present invention provides a method for improving the efficiency and stability of full-scale data queries in a distributed database, comprising: setting a timed task framework based on a distributed database; obtaining the data volume of each physical node of the distributed database as a variable value of a sample of each physical node; obtaining the average of the data volume of different physical nodes of the distributed database based on the variable value; determining the standard deviation of the degree of discreteness of data volume differences of samples of different physical nodes of the distributed database based on the variable value, the number of physical nodes, and the average of the data volume of different physical nodes; determining a standard score for calculating the degree of discreteness of each data volume of samples of different physical nodes of the distributed database based on the standard deviation; identifying the query efficiency and stability differences between physical nodes with different data volumes based on the standard score, and determining a decision-making mechanism; and determining the amount of data that needs to be queried and cached in advance based on the variable value and the standard score.
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