Historical Query Rating for Global Query Optimization
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Solution Overview
Problem
Traditional query optimization processes are limited to data available at the time of execution, leading to sub-optimal global query optimization.
Innovation Solution
A global query optimization module that uses present and historical query execution data to optimize query transformations and execution plans by generating and rating query candidates and execution plan candidates, selecting those that exceed predefined thresholds, and updating execution data to improve future optimizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional query optimization processes use only data available at the time of execution, then the optimization process is simple and fast, but the query optimization quality is sub-optimal
Solution Approach 1:
The system performs preliminary actions by collecting and storing query execution data from previous query executions before actual optimization occurs. This historical data is accumulated in advance to inform future optimization decisions, allowing the system to learn from past performance patterns and make more informed optimization choices without adding complexity to the real-time optimization process.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring query execution performance and using this information to refine future query optimizations. Execution data from previous queries feeds back into the optimization process, creating a closed-loop system where optimization quality improves over time based on actual performance outcomes rather than relying solely on theoretical cost estimates.
2Productivity
If traditional query optimization processes are used, then the system is simple to operate, but the time and resources required for query execution are not minimized
Solution Approach 1:
The system performs preliminary analysis of query execution patterns and performance characteristics before actual query execution. By pre-processing and storing execution data from historical queries, the system prepares optimization insights in advance, reducing the time and computational resources needed during actual query execution while improving overall productivity.
Solution Approach 2:
The optimization system serves itself by automatically learning from its own execution data without requiring external intervention. The system monitors its own performance, collects execution metrics, and uses this self-generated data to continuously improve query optimization, thereby reducing execution time and resource consumption while maintaining operational simplicity.
3Manufacturing precision
If query execution data from previous executions is collected and used, then global query optimization is improved, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary component that acts as a buffer between query execution and the optimization process. This intermediary layer collects, stores, and pre-processes execution data, separating the complexity of data collection and analysis from the actual optimization logic. This allows the core optimization engine to remain relatively simple while still benefiting from comprehensive historical data.
Data Source
AI summary
Techniques are provided for global query optimization. In one embodiment, the techniques involve receiving an input query, generating query candidates based on the input query, determining ratings of the query candidates, selecting a primary query of the query candidates based on the ratings of the query candidates, comparing a rating of the primary query to a query threshold, and generating a recommended query based on the rating.