Automated Database Index Management via Machine Learning
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Solution Overview
Problem
Database performance is hindered by changing usage patterns over time, as existing methods lack automation in optimizing index selection and type changes, leading to diminished performance improvements.
Innovation Solution
A method utilizing a machine learning model to analyze query logs to identify columns that require index additions, removals, or modifications, based on query patterns, allowing for automated adjustments such as changing index types from binary tree to hash or generalized search tree indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to identify and manage database indexes, then database administrators can control index selection, but the process becomes time-consuming and cannot adapt quickly to changing usage patterns
Solution Approach 1:
The system enables automated self-service index management by using machine learning models to analyze query logs and automatically identify optimal index candidates, eliminating the need for manual database administrator intervention in routine index selection tasks
Solution Approach 2:
The patent replaces manual mechanical processes of index analysis and selection with an automated machine learning-based system that processes query logs and generates index recommendations, significantly improving efficiency while reducing time consumption
2Speed
If indexes are added to improve query speed, then database search performance improves, but database size increases
Solution Approach 1:
The system dynamically changes index parameters by analyzing query patterns and automatically adding, removing, or modifying indexes based on actual usage, ensuring indexes are maintained only when they provide performance benefits and removing them when they consume excessive storage space
Solution Approach 2:
The patent implements dynamic index management where the database system continuously monitors query patterns and automatically adjusts the index configuration, adding indexes when query performance degrades and removing them when they are no longer beneficial, creating a flexible adaptation mechanism
3Adaptability or versatility
If multiple index types are used to optimize different query patterns, then query performance across various operations improves, but the complexity of index management increases
Solution Approach 1:
The machine learning model automatically performs the complex task of analyzing query patterns and selecting appropriate index types, eliminating the need for database administrators to manually understand and manage different index types, thus maintaining versatility while reducing management complexity
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the database queries and the index management system, which automatically analyzes query patterns and determines optimal index configurations, simplifying the management process while maintaining adaptability to different query types
Data Source
AI summary
A database index management system uses one or more machine learning models to analyze a query log in relation to a database. A machine learning model may identify a query pattern and/or a change in the query pattern from the query log, identify a column associated with the query pattern, and identify an addition, removal, or modification of an index related to the identified column. The database index management system may perform one or more additions, removals or modifications of indices of the database based on query patterns identified in the query log. The database index management system continuously improves database performance in response to changing database usage patterns over time.