Machine Learning Controller for SQL Query Execution Plan Optimization
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Solution Overview
Problem
Manual performance tuning of complex SQL queries in relational databases is time-consuming, requires specific knowledge of database properties, and needs repetition upon data volume changes, making it inefficient and labor-intensive.
Innovation Solution
A system utilizing machine learning controllers and deep learning algorithms to process SQL query requests, generate probable smart execution plans, and automatically rewrite queries for improved performance, reducing the need for manual tuning by analyzing cost profiles and optimizer rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual performance tuning is performed on complex SQL queries, then query execution performance can be optimized, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service performance tuning by automatically analyzing query execution plans and generating optimized versions without requiring manual human intervention. The automated query optimizer examines cost profiles, identifies suboptimal operations, and produces improved execution plans independently.
Solution Approach 2:
The patent replaces the mechanical manual tuning process with an automated computational system. Instead of human experts manually examining and adjusting query plans, a machine learning-based optimizer automatically performs the analysis and optimization, substituting human cognitive work with algorithmic processing.
2Reliability
If manual performance tuning is performed, then query optimization can be achieved, but it requires specific knowledge of database properties and different tuning methods for different databases
Solution Approach 1:
The automated query optimizer is designed to work universally across different database systems and query types. It implements a general framework that adapts to various database properties and query complexities without requiring separate tuning methods for each case, making the optimization process accessible to users regardless of their specialized knowledge.
Solution Approach 2:
The system performs self-service optimization by automatically adapting to different database properties and query characteristics without requiring user expertise. The optimizer independently analyzes the specific context of each query and applies appropriate optimization strategies tailored to the situation.
3Reliability
If manual performance tuning is performed, then initial query optimization can be achieved, but the entire tuning process must be repeated when data volume changes
Solution Approach 1:
The system performs preliminary analysis of query execution plans and establishes baseline performance metrics in advance. By pre-computing cost profiles and identifying optimization opportunities before data changes occur, the system can quickly adapt when data volumes change without requiring complete re-tuning of the entire query portfolio.
Solution Approach 2:
The automated optimizer implements continuous feedback mechanisms that monitor data volume changes and query performance metrics. When data volumes change, the system receives feedback about the new conditions and automatically adjusts execution plans accordingly, eliminating the need to repeat the entire tuning process from scratch.
4Reliability
If traditional query optimization methods are used, then basic performance tuning can be achieved, but complex SQL queries require different tuning methods and extensive manual effort
Solution Approach 1:
The patent merges multiple disparate tuning methods and optimization techniques into a single unified automated system. Instead of requiring different manual tuning approaches for different types of complex queries, the system integrates various optimization strategies into one cohesive framework that handles diverse query scenarios automatically.
Solution Approach 2:
The system replaces the complex mechanical process of manual query tuning with an automated computational approach. The machine learning-based optimizer handles the complexity of analyzing and optimizing complex SQL queries without requiring human experts to navigate through multiple different tuning methodologies.
Data Source
AI summary
Methods, systems, and computing platforms for data communication are disclosed. The processor(s) may be configured to electronically receive a SQL query request for a relational computer readable database having plurality of computer readable records. The processor(s) may be configured to electronically process the SQL query request with a machine learning controller based on a set of machine learning training data so as to output at least one probable smart execution plan for executing the SQL query request. The processor(s) may be configured to electronically output the at least one probable smart execution plan for executing the SQL query request to a relational database management processing module.