LLM Query Optimizer for Automatic Database Execution Planning
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Solution Overview
Problem
Developing and executing data queries against large-scale datasets in distributed database systems is complex and time-consuming, requiring trial-and-error approaches and manual parameter tuning, which adds complexity and burden to the code development process.
Innovation Solution
A large language model-based data query optimizer that accepts programming code as input, analyzes it to determine execution strategies, and generates optimized instructions for query execution, including hierarchical ordering and optimization techniques to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If developers manually tune parameters to optimize query execution, then query execution performance is improved, but development time and complexity increase
Solution Approach 1:
The system enables self-service optimization where the query execution engine automatically analyzes query characteristics, data distribution, and system state to generate optimized execution plans without developer intervention. The engine performs self-tuning of parameters and adaptive optimization, eliminating the need for manual parameter adjustment while maintaining high query performance.
Solution Approach 2:
The system implements feedback mechanisms where execution results, performance metrics, and system state information are continuously monitored and fed back to the optimization engine. This feedback loop enables automatic adjustment of query execution parameters and strategies based on actual performance data, allowing the system to learn and improve over time without additional developer input.
2Productivity
If developers manually tune parameters to optimize query execution, then query execution performance is improved, but operational complexity increases
Solution Approach 1:
The query execution engine performs automatic parameter optimization and execution plan generation without requiring developer expertise in performance tuning. The system encapsulates complex optimization logic within the engine, presenting a simplified interface to developers while automatically handling parameter adjustment, execution strategy selection, and performance optimization.
Solution Approach 2:
The optimization engine acts as an intermediary between the query interface and the execution engine, translating high-level query requests into optimized execution plans. This intermediary layer handles the complexity of parameter tuning, data distribution analysis, and execution strategy selection, shielding developers from operational complexity while delivering optimized query performance.
3Productivity
If trial-and-error approach is used to identify optimal parameters, then query execution performance is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary analysis of query characteristics, data distribution, and system state before query execution to pre-determine optimal execution parameters and strategies. By conducting this analysis in advance rather than through trial-and-error during execution, the system eliminates iterative testing time while ensuring optimal performance from the first execution.
Solution Approach 2:
The system implements real-time feedback mechanisms that monitor query execution performance and automatically adjust parameters based on observed results. This continuous feedback loop replaces the traditional trial-and-error approach by systematically learning from each execution and making data-driven optimizations, significantly reducing the time needed to identify optimal parameters.
Data Source
AI summary
Methods and systems are presented for providing a large language model-based query optimizer to interface between program developers and database systems. The query optimizer receives programming code corresponding to a set of queries intended for a database system from a program developer. The query optimizer then uses a machine learning model to analyze the programming code and to determine a set of strategies for executing the set of data queries corresponding to the programming code. To determine the set of strategies, the machine learning model analyzes dependencies among the set of data queries and retrieves sample data from the database system. The machine learning model implement the set of strategies by incorporating additional instructions in the programming code for the database system such that the database system would execute the set of data queries according to the set of strategies.