Problem-Cube Model for Database Performance Analysis
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Solution Overview
Problem
Current database performance monitoring tools face challenges in efficiently analyzing performance problems due to their reliance on relational database models, which are not problem-specific and struggle with interactive data exploration, making it difficult for database administrators to identify and address performance issues effectively.
Innovation Solution
The implementation of a method using multi-dimensional database models, specifically the problem-cube model, which transforms performance data into a problem-specific form for analysis, allowing for interactive exploration and automatic detection of performance problems through the creation and filling of cubes, and utilizing a cube advisor engine for autonomous analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If relational database models are used to store performance data, then data storage efficiency is improved, but data exploration and problem-specific analysis capability deteriorate
Solution Approach 1:
The patent transforms the flat, two-dimensional relational database structure into a multi-dimensional cube structure. This dimensional transformation allows data to be organized along multiple axes (time, database instance, performance metric, etc.), enabling simultaneous efficient storage and flexible multi-perspective exploration without sacrificing either storage efficiency or analytical capability.
Solution Approach 2:
The patent segments the monolithic relational database into multiple dimensional cubes, where each cube represents a specific performance aspect or problem domain. This segmentation allows database administrators to focus on specific problem areas while maintaining the overall data integrity and storage efficiency of the underlying relational structure.
2Loss of substance
If relational database models with normal forms are used, then data redundancy is reduced, but problem-specific storage and query capability deteriorate
Solution Approach 1:
The patent introduces dimensional cubes that overlay the normalized relational database structure. These cubes provide problem-specific storage and query capabilities by organizing data along dimensions relevant to specific performance problems, without altering the underlying redundancy-free relational schema. This allows the same data to serve both storage efficiency and problem-specific analysis needs.
3Productivity
If SQL queries are used to analyze performance data, then data retrieval is achieved, but interactive exploration and problem determination efficiency deteriorate
Solution Approach 1:
The patent introduces dimensional cubes as an intermediary layer between the relational database and the database administrator. These cubes pre-organize data in problem-specific configurations, serving as a mediator that translates complex SQL query requirements into intuitive multi-dimensional explorations, thereby reducing problem determination time while maintaining data retrieval capability.
Solution Approach 2:
The patent performs preliminary organization of performance data into dimensional cubes that are pre-configured for specific problem types. This preliminary action eliminates the need for ad-hoc SQL query construction during problem determination, allowing database administrators to immediately explore pre-structured data without time-consuming query writing and optimization.
4Measurement precision
If experienced database administrators manually analyze performance data, then problem detection accuracy is improved, but automation level and time consumption deteriorate
Solution Approach 1:
The patent enables the system to serve itself by automatically creating and populating dimensional cubes based on performance data. This self-service capability reduces reliance on manual database administrator intervention while maintaining high problem detection accuracy, as the cubes are automatically structured to highlight problematic patterns and anomalies in the performance data.
Data Source
AI summary
Methods and systems, including computer program products, for monitoring and analyzing a database performance problem stored in a separate performance database with cube-based analysis program tools. The methods and systems include means for detecting one or more performance problems, mapping performance problems to cubes, filling the cubes with performance data collected before, and outputting the filled cubes to an output interface usable to deploy said cubes for cube-based database performance analysis.