Database Query Parameter Tuning by Query Category Matching
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Solution Overview
Problem
Existing database query systems face challenges in maintaining high query success rates due to inefficient manual parameter adjustments, which can lead to query failures affecting service level agreements and user experience, especially in online clusters with complex environments.
Innovation Solution
A data query method that categorizes query requests into specific categories, adjusts database parameters based on these categories using a Markov decision process and Combinatorial Upper Confidence Bound algorithm to optimize parameter combinations, improving query success rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual parameter adjustments are used to optimize database queries, then query performance can be improved, but the complexity of operation increases and query success rate decreases due to inefficiency
Solution Approach 1:
The system automatically categorizes query requests and adjusts database parameters without manual intervention. The database management system performs self-optimization by matching query categories with optimal parameter configurations, eliminating the need for manual parameter tuning while improving query success rates.
Solution Approach 2:
The system dynamically changes database parameters based on query category matching. Different query categories are associated with different optimal parameter settings, and the system automatically selects and applies the appropriate parameters for each query type, improving productivity while maintaining ease of operation.
2Reliability
If database parameters are adjusted to improve query success rate, then query reliability improves, but the device complexity increases due to parameter management
Solution Approach 1:
The system segments query requests into different categories based on their characteristics. By dividing the query space into distinct categories, the system can manage parameters more efficiently, associating specific parameter configurations with each category rather than managing all parameters for all queries, thus reducing overall complexity while improving reliability.
Solution Approach 2:
The system pre-establishes the relationship between query categories and optimal parameter configurations. This preliminary categorization and parameter association work is done in advance, creating a lookup structure that simplifies real-time parameter selection and reduces the complexity of parameter management during query execution.
3Productivity
If manual parameter adjustments are performed, then some query performance can be optimized, but time is lost due to inefficient adjustment processes
Solution Approach 1:
The database management system automatically performs parameter adjustment without waiting for manual intervention. When a query request arrives, the system self-service by categorizing the query and immediately applying the appropriate parameters, eliminating the time loss associated with manual parameter adjustment processes.
Solution Approach 2:
The system performs parameter preparation in advance by pre-defining optimal parameter configurations for different query categories. This preliminary action ensures that when queries need to be executed, the appropriate parameters are already ready and can be applied immediately, significantly reducing the time required for parameter adjustment.
Data Source
AI summary
The present disclosure provides a data query method and a related device. The method includes: receiving a query request for target data, where the query request is used to request to query the target data in a target database; matching the query request with a plurality of candidate query categories; in response to the query request matching a target query category in the plurality of candidate query categories, determining at least one target database parameter of the target database, according to the target query category; adjusting at least one database parameter of the target database, based on the at least one target database parameter; and querying the target data in the target database after parameter adjustment, based on the query request.