Machine Learning Index Recommendations for Slow Query Performance
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Solution Overview
Problem
Identifying suitable indexes to improve database query performance is challenging, particularly in large, dynamic systems, as it involves manual analysis and is prone to human error, and existing systems lack real-time analysis and scalability.
Innovation Solution
An automated index recommendation system using machine learning models analyzes query and index data to identify optimal indexes, integrating user feedback for seamless integration into database schemas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to identify suitable indexes, then human expertise can be applied, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning system that uses trained models to predict index recommendations. The system automatically analyzes query workloads and database schemas to generate index recommendations without human intervention, thereby eliminating time loss while maintaining or improving accuracy through consistent algorithmic application.
Solution Approach 2:
The system enables the database to self-optimize by automatically analyzing its own query patterns and generating index recommendations. The machine learning model continuously learns from query workloads and autonomously identifies suitable indexes, allowing the system to self-improve performance without external human analysis.
2Speed
If multiple indexes are implemented to address slow queries, then query performance may improve, but system overhead increases
Solution Approach 1:
The machine learning model predicts index recommendation values that quantify the expected performance benefit versus overhead cost of each potential index. The system uses these predicted values to select only those indexes with favorable performance-overhead ratios, dynamically adjusting index creation decisions based on learned patterns from query workloads and database characteristics.
Solution Approach 2:
Instead of implementing all possible indexes or relying on exhaustive analysis, the system applies partial action by selecting only the most promising index recommendations based on predicted performance improvement. The machine learning model identifies a subset of high-value indexes that will provide sufficient performance benefit without incurring excessive overhead from unnecessary indexes.
3Productivity
If automated systems are used for index recommendations, then scalability improves, but system complexity increases
Solution Approach 1:
The patent implements a universal machine learning-based recommendation system that handles diverse query types, database schemas, and workload patterns through a single automated platform. The system performs multiple functions including query analysis, index prediction, performance evaluation, and recommendation generation, thereby achieving scalability across different database scenarios without proportionally increasing complexity.
Data Source
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AI summary
Systems and methods described herein relate to automatic index recommendations for improved database query performance. Candidate indexes are identified. The candidate indexes are associated with a database query that is classified as a slow query. A feature vector is generated for each candidate index to represent statement features and statistical features associated with the candidate index. The feature vectors are provided to one or more machine learning models to obtain an index recommendation value for each candidate index. An index recommendation is presented at a user device. The index recommendation identifies a first index of the candidate indexes based at least partially on the index recommendation value obtained for the first index. User input indicative of a user selection of the first index is received. A database schema is updated to include the first index in response to the user input.