ML Query Scoring for Database Execution Overhead Control
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Solution Overview
Problem
Users' varying experience levels in writing database queries often result in inefficient use of computing resources, leading to increased costs and platform instability due to non-optimal queries, which current monitoring systems fail to address proactively.
Innovation Solution
A machine-learning-based query optimization system that analyzes proposed queries through a trained model to determine execution overhead and triggers corrective actions when thresholds are exceeded, optimizing queries to minimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users write queries with varying experience levels, then query writing flexibility is improved, but computing resource efficiency deteriorates
Solution Approach 1:
The system implements feedback by monitoring query execution overhead in real-time and using machine learning models to analyze the relationship between query characteristics and resource consumption. This feedback loop enables the system to identify inefficient queries and provide recommendations to users, improving their query writing skills while reducing resource waste.
Solution Approach 2:
The patent replaces manual query optimization (mechanical human effort) with an automated machine learning system. The ML model automatically analyzes queries, predicts execution overhead, and suggests optimizations, substituting the need for users to manually write efficient queries while maintaining flexibility in query writing.
2Adaptability or versatility
If complex queries are executed without optimization, then query functionality is improved, but platform stability deteriorates
Solution Approach 1:
The system performs preliminary action by analyzing and scoring queries before they are executed. The machine learning model evaluates the proposed query, predicts its execution overhead, and provides feedback in advance. This allows complex queries to be optimized beforehand, ensuring they maintain full functionality while preventing platform instability by identifying and correcting problematic queries before execution.
3Loss of information
If real-time query monitoring is implemented, then resource consumption visibility is improved, but system complexity deteriorates
Solution Approach 1:
The system implements self-service by using machine learning models that automatically learn from historical query execution data and continuously improve their prediction accuracy without manual intervention. The ML system autonomously monitors resource consumption, identifies patterns, and provides optimization recommendations, reducing the need for complex manual monitoring infrastructure while maintaining high visibility into resource usage.
Data Source
AI summary
A method for query optimization may include capturing a proposed database query input into a user interface. The method may further include providing the proposed database query to a machine-learning model. The machine-learning model may have been trained, using one or more gathered and/or simulated sets of query execution overhead data and one or more gathered and/or simulated sets of database queries, to determine a potential execution overhead of a database query and output a query execution score. The method may further include outputting, by the machine-learning model, the query execution score based on the proposed database query. The method may further include determining that the query execution score exceeds a query execution score threshold. The method may further include triggering a corrective action based on the query execution score exceeding the query execution score threshold.